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Computer-assisted language learning
Computer-assisted language learning
Computer-assisted language learning
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Computer-assisted language learning (CALL), known as computer-assisted learning (CAL) in British English and computer-aided language instruction (CALI) and computer-aided instruction (CAI) in American English,[1] is, per Levy (1997: p. 1), "the exploration and study of computer applications in language teaching and learning."[2] CALL embraces a wide range of information and communications technology "applications and approaches to teaching and learning foreign languages, ranging from the traditional drill-and-practice programs that characterized CALL in the 1960s and 1970s to more recent manifestations of CALL, such as those utilized virtual learning environment and Web-based distance learning. It also extends to the use of corpora and concordancers, interactive whiteboards,[3] computer-mediated communication (CMC),[4] language learning in virtual worlds, and mobile-assisted language learning (MALL).[5]

The term CALI (computer-assisted language instruction) was used before CALL, originating as a subset of the broader term CAI (computer-assisted instruction). CALI fell out of favor among language teachers, however, because it seemed to emphasize a teacher-centered instructional approach. Language teachers increasingly favored a student-centered approach focused on learning rather than instruction. CALL began to replace CALI in the early 1980s (Davies & Higgins, 1982: p. 3).[6] and it is now incorporated into the names of the growing number of professional associations worldwide.

An alternative term, technology-enhanced language learning (TELL),[7] also emerged around the early 1990s: e.g. the TELL Consortium project, University of Hull.

The current philosophy of CALL emphasizes student-centered materials that empower learners to work independently. These materials can be structured or unstructured but typically incorporate two key features: interactive and individualized learning. CALL employs tools that assist teachers in facilitating language learning, whether reinforcing classroom lessons or providing additional support to learners. The design of CALL materials typically integrates principles from language pedagogy and methodology, drawing from various learning theories such as behaviourism, cognitive theory, constructivism, and second-language acquisition theories like Stephen Krashen's. monitor hypothesis.

A combination of face-to-face teaching and CALL is usually referred to as blended learning. Blended learning is designed to increase learning potential and is more commonly found than pure CALL (Pegrum 2009: p. 27).[8]

See Davies et al. (2011: Section 1.1, What is CALL?).[9] See also Levy & Hubbard (2005), who raise the question Why call CALL "CALL"?[10]

History

[edit]

CALL dates back to the 1960s, when it was first introduced on university mainframe computers. The PLATO project, initiated at the University of Illinois in 1960, is an important landmark in the early development of CALL (Marty 1981).[11] The advent of the microcomputer in the late 1970s brought computing within the range of a wider audience, resulting in a boom in the development of CALL programs and a flurry of publications of books on CALL in the early 1980s.

Dozens of CALL programs are currently available on the internet, at prices ranging from free to expensive,[12] and other programs are available only through university language courses.

There have been several attempts to document the history of CALL. Sanders (1995) covers the period from the mid-1960s to the mid-1990s, focusing on CALL in North America.[13] Delcloque (2000) documents the history of CALL worldwide, from its beginnings in the 1960s to the dawning of the new millennium.[14] Davies (2005) takes a look back at CALL's past and attempts to predict where it is going.[15] Hubbard (2009) offers a compilation of 74 key articles and book excerpts, originally published in the years 1988–2007, that give a comprehensive overview of the wide range of leading ideas and research results that have exerted an influence on the development of CALL or that show promise in doing so in the future.[16] A published review of Hubbard's collection can be found in Language Learning & Technology 14, 3 (2010).[17]

Butler-Pascoe (2011) looks at the history of CALL from a different point of view, namely the evolution of CALL in the dual fields of educational technology and second/foreign language acquisition and the paradigm shifts experienced along the way.[18]

See also Davies et al. (2011: Section 2, History of CALL).[9]

Typology and phases

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During the 1980s and 1990s, several attempts were made to establish a CALL typology. A wide range of different types of CALL programs was identified by Davies & Higgins (1985),[19] Jones & Fortescue (1987),[20] Hardisty & Windeatt (1989)[21] and Levy (1997: pp. 118ff.).[2] These included gap-filling and Cloze programs, multiple-choice programs, free-format (text-entry) programs, adventures and simulations, action mazes, sentence-reordering programs, exploratory programs—and "total Cloze", a type of program in which the learner has to reconstruct a whole text. Most of these early programs still exist in modernised versions.

Since the 1990s, it has become increasingly difficult to categorise CALL as it now extends to the use of blogs, wikis, social networking, podcasting, Web 2.0 applications, language learning in virtual worlds and interactive whiteboards (Davies et al. 2010: Section 3.7).[9]

Warschauer (1996)[22] and Warschauer & Healey (1998)[23] took a different approach. Rather than focusing on the typology of CALL, they identified three historical phases of CALL, classified according to their underlying pedagogical and methodological approaches:

  • Behavioristic CALL: conceived in the 1950s and implemented in the 1960s and 1970s.
  • Communicative CALL: 1970s to 1980s.
  • Integrative CALL: embracing Multimedia and the Internet: 1990s.

Most CALL programs in Warschauer & Healey's first phase, Behavioristic CALL (1960s to 1970s), consisted of drill-and-practice materials in which the computer presented a stimulus and the learner provided a response. At first, both could be done only through text. The computer would analyse students' input and give feedback, and more sophisticated programs would react to students' mistakes by branching to help screens and remedial activities. While such programs and their underlying pedagogy still exist today, behaviouristic approaches to language learning have been rejected by most language teachers, and the increasing sophistication of computer technology has led CALL to other possibilities.

The second phase described by Warschauer & Healey, Communicative CALL, is based on the communicative approach that became prominent in the late 1970s and 1980s (Underwood 1984).[24] In the communicative approach the focus is on using the language rather than analysis of the language, and grammar is taught implicitly rather than explicitly. It also allows for originality and flexibility in student output of language. The communicative approach coincided with the arrival of the PC, which made computing much more widely available and resulted in a boom in the development of software for language learning. The first CALL software in this phase continued to provide skill practice but not in a drill format—for example: paced reading, text reconstruction and language games—but the computer remained the tutor. In this phase, computers provided context for students to use the language, such as asking for directions to a place, and programs not designed for language learning such as Sim City, Sleuth and Where in the World is Carmen Sandiego? were used for language learning. Criticisms of this approach include using the computer in an ad hoc and disconnected manner for more marginal aims rather than the central aims of language teaching.

The third phase of CALL described by Warschauer & Healey, Integrative CALL, starting from the 1990s, tried to address criticisms of the communicative approach by integrating the teaching of language skills into tasks or projects to provide direction and coherence. It also coincided with the development of multimedia technology (providing text, graphics, sound and animation) as well as Computer-mediated communication (CMC). CALL in this period saw a definitive shift from the use of the computer for drill and tutorial purposes (the computer as a finite, authoritative base for a specific task) to a medium for extending education beyond the classroom. Multimedia CALL started with interactive laser videodiscs such as Montevidisco (Schneider & Bennion 1984)[25] and A la rencontre de Philippe (Fuerstenberg 1993),[26] both of which were simulations of situations where the learner played a key role. These programs later were transferred to CD-ROMs, and new role-playing games (RPGs) such as Who is Oscar Lake? made their appearance in a range of different languages.

In a later publication Warschauer changed the name of the first phase of CALL from Behavioristic CALL to Structural CALL and also revised the dates of the three phases (Warschauer 2000):[27]

  • Structural CALL: 1970s to 1980s.
  • Communicative CALL: 1980s to 1990s.
  • Integrative CALL: 2000 onwards.

Bax (2003)[28] took issue with Warschauer & Haley (1998) and Warschauer (2000) and proposed these three phases:

  • Restricted CALL – mainly behaviouristic: 1960s to 1980s.
  • Open CALL – i.e. open in terms of feedback given to students, software types and the role of the teacher, and including simulations and games: 1980s to 2003 (i.e. the date of Bax's article).
  • Integrated CALL – still to be achieved. Bax argued that at the time of writing language teachers were still in the Open CALL phase, as true integration could only be said to have been achieved when CALL had reached a state of "normalisation" – e.g. when using CALL was as normal as using a pen.

See also Bax & Chambers (2006)[29] and Bax (2011),[30] in which the topic of "normalisation" is revisited.

Flashcards

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A basic use of CALL is in vocabulary acquisition using flashcards, which requires quite simple programs. Such programs often make use of spaced repetition, a technique whereby the learner is presented with the vocabulary items that need to be committed to memory at increasingly longer intervals until long-term retention is achieved. This has led to the development of a number of applications known as spaced repetition systems (SRS),[31] including the generic Anki or SuperMemo package and programs such as BYKI[32] and phase-6,[33] which have been designed specifically for learners of foreign languages.

Software design and pedagogy

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Above all, careful consideration must be given to pedagogy in designing CALL software, but publishers of CALL software tend to follow the latest trend, regardless of its desirability. Moreover, approaches to teaching foreign languages are constantly changing, dating back to grammar-translation, through the direct method, audio-lingualism and a variety of other approaches, to the more recent communicative approach and constructivism (Decoo 2001).[34]

Designing and creating CALL software is an extremely demanding task, calling upon a range of skills. Major CALL development projects are usually managed by a team of people:

  • A subject specialist (also known as a content provider) – usually a language teacher – who is responsible for providing the content and pedagogical input. More than one subject specialist is required for larger CALL projects.
  • A programmer who is familiar with the chosen programming language or authoring tool.
  • A graphic designer, to produce pictures and icons, and to advise on fonts, colour, screen layout, etc.
  • A professional photographer or, at the very least, a very good amateur photographer. Graphic designers often have a background in photography too.
  • A sound engineer and a video technician will be required if the package is to contain substantial amounts of sound and video.
  • An instructional designer. Developing a CALL package is more than just putting a text book into a computer. An instructional designer will probably have a background in cognitive psychology and media technology, and will be able to advise the subject specialists in the team on the appropriate use of the chosen technology (Gimeno & Davies 2010).[35]

CALL inherently supports learner autonomy, the final of the eight conditions that Egbert et al. (2007) cite as "Conditions for Optimal Language Learning Environments". Learner autonomy places the learner firmly in control so that he or she "decides on learning goals" (Egbert et al., 2007, p. 8).[36]

It is all too easy when designing CALL software to take the comfortable route and produce a set of multiple-choice and gap-filling exercises, using a simple authoring tool (Bangs 2011),[37] but CALL is much more than this; Stepp-Greany (2002), for example, describes the creation and management of an environment incorporating a constructivist and whole language philosophy. According to constructivist theory, learners are active participants in tasks in which they "construct" new knowledge derived from their prior experience. Learners also assume responsibility for their learning, and the teacher is a facilitator rather than a purveyor of knowledge. Whole language theory embraces constructivism and postulates that language learning moves from the whole to the part, rather than building sub-skills to lead towards the higher abilities of comprehension, speaking, and writing. It also emphasises that comprehending, speaking, reading, and writing skills are interrelated, reinforcing each other in complex ways. Language acquisition is, therefore, an active process in which the learner focuses on cues and meaning and makes intelligent guesses. Additional demands are placed upon teachers working in a technological environment incorporating constructivist and whole language theories. The development of teachers' professional skills must include new pedagogical as well as technical and management skills. Regarding the issue of teacher facilitation in such an environment, the teacher has a key role to play, but there could be a conflict between the aim to create an atmosphere for learner independence and the teacher's natural feelings of responsibility. In order to avoid learners' negative perceptions, Stepp-Greany points out that it is especially important for the teacher to continue to address their needs, especially those of low-ability learners.[38]

Multimedia

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Language teachers have been avid users of technology for a very long time. Gramophone records were among the first technological aids to be used by language teachers in order to present students with recordings of native speakers' voices, and broadcasts from foreign radio stations were used to make recordings on reel-to-reel tape recorders. Other examples of technological aids that have been used in the foreign language classroom include slide projectors, film-strip projectors, film projectors, videocassette recorders and DVD players. In the early 1960s, integrated courses (which were often described as multimedia courses) began to appear. Examples of such courses are Ecouter et Parler (consisting of a coursebook and tape recordings)[39] and Deutsch durch die audiovisuelle Methode (consisting of an illustrated coursebook, tape recordings and a film-strip – based on the Structuro-Global Audio-Visual method).[40]

During the 1970s and 1980s standard microcomputers were incapable of producing sound and they had poor graphics capability. This represented a step backwards for language teachers, who by this time had become accustomed to using a range of different media in the foreign language classroom. The arrival of the multimedia computer in the early 1990s was therefore a major breakthrough as it enabled text, images, sound and video to be combined in one device and the integration of the four basic skills of listening, speaking, reading and writing (Davies 2011: Section 1).[41]

Examples of CALL programs for multimedia computers that were published on CD-ROM and DVD from the mid-1990s onwards are described by Davies (2010: Section 3).[41] CALL programs are still being published on CD-ROM and DVD, but Web-based multimedia CALL has now virtually supplanted these media.

Following the arrival of multimedia CALL, multimedia language centres began to appear in educational institutions. While multimedia facilities offer many opportunities for language learning with the integration of text, images, sound and video, these opportunities have often not been fully utilised. One of the main promises of CALL is the ability to individualise learning but, as with the language labs that were introduced into educational institutions in the 1960s and 1970s, the use of the facilities of multimedia centres has often devolved into rows of students all doing the same drills (Davies 2010: Section 3.1).[41] There is therefore a danger that multimedia centres may go the same way as the language labs. Following a boom period in the 1970s, language labs went rapidly into decline. Davies (1997: p. 28) lays the blame mainly on the failure to train teachers to use language labs, both in terms of operation and in terms of developing new methodologies, but there were other factors such as poor reliability, lack of materials and a lack of good ideas.[42]

Managing a multimedia language centre requires not only staff who have a knowledge of foreign languages and language teaching methodology but also staff with technical know-how and budget management ability, as well as the ability to combine all these into creative ways of taking advantage of what the technology can offer. A centre manager usually needs assistants for technical support, for managing resources and even the tutoring of students. Multimedia centres lend themselves to self-study and potentially self-directed learning, but this is often misunderstood. The simple existence of a multimedia centre does not automatically lead to students learning independently. Significant investment of time is essential for materials development and creating an atmosphere conducive to self-study. Unfortunately, administrators often have the mistaken belief that buying hardware by itself will meet the needs of the centre, allocating 90% of its budget to hardware and virtually ignoring software and staff training needs (Davies et al. 2011: Foreword).[43] Self-access language learning centres or independent learning centres have emerged partially independently and partially in response to these issues. In self-access learning, the focus is on developing learner autonomy through varying degrees of self-directed learning, as opposed to (or as a complement to) classroom learning. In many centres learners access materials and manage their learning independently, but they also have access to staff for help. Many self-access centres are heavy users of technology and an increasing number of them are now offering online self-access learning opportunities. Some centres have developed novel ways of supporting language learning outside the context of the language classroom (also called 'language support') by developing software to monitor students' self-directed learning and by offering online support from teachers. Centre managers and support staff may need to have new roles defined for them to support students' efforts at self-directed learning: v. Mozzon-McPherson & Vismans (2001), who refer to a new job description, namely that of the "language adviser".[44]

Internet

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The emergence of the World Wide Web (now known simply as "the Web") in the early 1990s marked a significant change in the use of communications technology for all computer users. Email and other forms of electronic communication had been in existence for many years, but the launch of Mosaic, the first graphical Web browser, in 1993 brought about a radical change in the ways in which we communicate electronically. The launch of the Web in the public arena immediately began to attract the attention of language teachers. Many language teachers were already familiar with the concept of hypertext on stand-alone computers, which made it possible to set up non-sequential structured reading activities for language learners in which they could point to items of text or images on a page displayed on the computer screen and branch to any other pages, e.g. in a so-called "stack" as implemented in the HyperCard program on Apple Mac computers. The Web took this one stage further by creating a worldwide hypertext system that enabled the user to branch to different pages on computers anywhere in the world simply by pointing and clicking at a piece of text or an image. This opened up access to thousands of authentic foreign-language websites to teachers and students that could be used in a variety of ways. A problem that arose, however, was that this could lead to a good deal of time-wasting if Web browsing was used in an unstructured way (Davies 1997: pp. 42–43),[42] and language teachers responded by developing more structured activities and online exercises (Leloup & Ponterio 2003).[45] Davies (2010) lists over 500 websites, where links to online exercises can be found, along with links to online dictionaries and encyclopaedias, concordancers, translation aids and other miscellaneous resources of interest to the language teacher and learner.[46]

The launch of the (free) Hot Potatoes (Holmes & Arneil) authoring tool, which was first demonstrated publicly at the EUROCALL 1998 conference, made it possible for language teachers to create their own online interactive exercises. Other useful tools are produced by the same authors.[47]

In its early days the Web could not compete seriously with multimedia CALL on CD-ROM and DVD. Sound and video quality was often poor, and interaction was slow. But now the Web has caught up. Sound and video are of high quality and interaction has improved tremendously, although this does depend on sufficient bandwidth being available, which is not always the case, especially in remote rural areas and developing countries. One area in which CD-ROMs and DVDs are still superior is in the presentation of listen/respond/playback activities, although such activities on the Web are continually improving.

Since the early 2000s there has been a boom in the development of so-called Web 2.0 applications. Contrary to popular opinion, Web 2.0 is not a new version of the Web, rather it implies a shift in emphasis from Web browsing, which is essentially a one-way process (from the Web to the end-user), to making use of Web applications in the same way as one uses applications on a desktop computer. It also implies more interaction and sharing. Walker, Davies & Hewer (2011: Section 2.1)[48] list the following examples of Web 2.0 applications that language teachers are using:

There is no doubt that the Web has proved to be a main focus for language teachers, who are making increasingly imaginative use of its wide range of facilities: see Dudeney (2007)[50] and Thomas (2008).[51] Above all, the use of Web 2.0 tools calls for a careful reexamination of the role of the teacher in the classroom (Richardson 2006).[52]

Corpora and concordancers

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Corpora have been used for many years as the basis of linguistic research and also for the compilation of dictionaries and reference works such as the Collins Cobuild series, published by HarperCollins.[53] Tribble & Barlow (2001),[54] Sinclair (2004)[55] and McEnery & Wilson (2011)[56] describe a variety of ways in which corpora can be used in language teaching.

An early reference to the use of electronic concordancers in language teaching can be found in Higgins & Johns (1984: pp. 88–94),[57] and many examples of their practical use in the classroom are described by Lamy & Klarskov Mortensen (2010).[58]

It was Tim Johns (1991), however, who raised the profile of the use of concordancers in the language classroom with his concept of Data-driven learning (DDL).[59] DDL encourages learners to work out their own rules about the meaning of words and their usage by using a concordancer to locate examples in a corpus of authentic texts. It is also possible for the teacher to use a concordancer to find examples of authentic usage to demonstrate a point of grammar or typical collocations, and to generate exercises based on the examples found. Various types of concordancers and where they can be obtained are described by Lamy & Klarskov Mortensen (2011).[58]

Robb (2003) shows how it is possible to use Google as a concordancer, but he also points out a number of drawbacks, for instance there is no control over the educational level, nationality, or other characteristics of the creators of the texts that are found, and the presentation of the examples is not as easy to read as the output of a dedicated concordancer that places the key words (i.e. the search terms) in context.[60]

Virtual worlds

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Virtual worlds date back to the adventure games and simulations of the 1970s, for example Colossal Cave Adventure, a text-only simulation in which the user communicated with the computer by typing commands at the keyboard. Language teachers discovered that it was possible to exploit these text-only programs by using them as the basis for discussion. Jones G. (1986) describes an experiment based on the Kingdom simulation, in which learners played roles as members of a council governing an imaginary kingdom. A single computer in the classroom was used to provide the stimulus for discussion, namely simulating events taking place in the kingdom: crop planting time, harvest time, unforeseen catastrophes, etc.[61]

The early adventure games and simulations led on to multi-user variants, which were known as MUDs (Multi-user domains). Like their predecessors, MUDs were text-only, with the difference that they were available to a wider online audience. MUDs then led on to MOOs (Multi-user domains object-oriented), which language teachers were able to exploit for teaching foreign languages and intercultural understanding: see Donaldson & Kötter (1999)[62] and (Shield 2003).[63]

The next major breakthrough in the history of virtual worlds was the graphical user interface. Lucasfilm's Habitat (1986), was one of the first virtual worlds that was graphically based, albeit only in a two-dimensional environment. Each participant was represented by a visual avatar who could interact with other avatars using text chat.

Three-dimensional virtual worlds such as Traveler and Active Worlds, both of which appeared in the 1990s, were the next important development. Traveler included the possibility of audio communication (but not text chat) between avatars who were represented as disembodied heads in a three-dimensional abstract landscape. Svensson (2003) describes the Virtual Wedding Project, in which advanced students of English made use of Active Worlds as an arena for constructivist learning.[64]

The 3D world of Second Life was launched in 2003. Initially perceived as another role-playing game (RPG), it began to attract the interest of language teachers with the launch of the first of the series of SLanguages conferences in 2007.[65] Walker, Davies & Hewer (2011: Section 14.2.1)[48] and Molka-Danielsen & Deutschmann (2010)[66] describe a number of experiments and projects that focus on language learning in Second Life. See also the Wikipedia article Virtual world language learning.

To what extent Second Life and other virtual worlds will become established as important tools for teachers of foreign languages remains to be seen. It has been argued by Dudeney (2010) in his That's Life blog that Second Life is "too demanding and too unreliable for most educators". The subsequent discussion shows that this view is shared by many teachers, but many others completely disagree.[67]

Regardless of the pros and cons of Second Life, language teachers' interest in virtual worlds continues to grow. The joint EUROCALL/CALICO Virtual Worlds Special Interest Group[68] was set up in 2009, and there are now many areas in Second Life that are dedicated to language learning and teaching, for example the commercial area for learners of English, which is managed by Language Lab,[69] and free areas such as the region maintained by the Goethe-Institut[70] and the EduNation Islands.[71] There are also examples of simulations created specifically for language education, such as those produced by the EC-funded NIFLAR[72] and AVALON[73] projects. NIFLAR is implemented both in Second Life and in Opensim.

Human language technologies

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Human language technologies (HLT) comprise a number of areas of research and development that focus on the use of technology to facilitate communication in a multilingual information society. Human language technologies are areas of activity in departments of the European Commission that were formerly grouped under the heading language engineering (Gupta & Schulze 2011: Section 1.1).[74]

The parts of HLT that is of greatest interest to the language teacher is natural language processing (NLP), especially parsing, as well as the areas of speech synthesis and speech recognition.

Speech synthesis has improved immeasurably in recent years. It is often used in electronic dictionaries to enable learners to find out how words are pronounced. At word level, speech synthesis is quite effective, the artificial voice often closely resembling a human voice. At phrase level and sentence level, however, there are often problems of intonation, resulting in speech production that sounds unnatural even though it may be intelligible. Speech synthesis as embodied in text to speech (TTS) applications is invaluable as a tool for unsighted or partially sighted people. Gupta & Schulze (2010: Section 4.1) list several examples of speech synthesis applications.[74]

Speech recognition is less advanced than speech synthesis. It has been used in a number of CALL programs, in which it is usually described as automatic speech recognition (ASR). ASR is not easy to implement. Ehsani & Knodt (1998) summarise the core problem as follows:

"Complex cognitive processes account for the human ability to associate acoustic signals with meanings and intentions. For a computer, on the other hand, speech is essentially a series of digital values. However, despite these differences, the core problem of speech recognition is the same for both humans and machines: namely, of finding the best match between a given speech sound and its corresponding word string. Automatic speech recognition technology attempts to simulate and optimize this process computationally."[75]

Programs embodying ASR normally provide a native speaker model that the learner is requested to imitate, but the matching process is not 100% reliable and may result in a learner's perfectly intelligible attempt to pronounce a word or phrase being rejected (Davies 2010: Section 3.4.6 and Section 3.4.7).[41]

Parsing is used in a number of ways in CALL. Gupta & Schulze (2010: Section 5) describe how parsing may be used to analyse sentences, presenting the learner with a tree diagram that labels the constituent parts of speech of a sentence and shows the learner how the sentence is structured.[74]

Parsing is also used in CALL programs to analyse the learner's input and diagnose errors. Davies (2002)[76] writes:

"Discrete error analysis and feedback were a common feature of traditional CALL, and the more sophisticated programs would attempt to analyse the learner's response, pinpoint errors, and branch to help and remedial activities. ... Error analysis in CALL is, however, a matter of controversy. Practitioners who come into CALL via the disciplines of computational linguistics, e.g. Natural Language Processing (NLP) and Human Language Technologies (HLT), tend to be more optimistic about the potential of error analysis by computer than those who come into CALL via language teaching. [...] An alternative approach is the use of Artificial Intelligence (AI) techniques to parse the learner's response – so-called intelligent CALL (ICALL) – but there is a gulf between those who favour the use of AI to develop CALL programs (Matthews 1994)[77] and, at the other extreme, those who perceive this approach as a threat to humanity (Last 1989:153)".[78]

Underwood (1989)[79] and Heift & Schulze (2007)[80] present a more positive picture of AI.

Research into speech synthesis, speech recognition and parsing and how these areas of NLP can be used in CALL are the main focus of the NLP Special Interest Group[81] within the EUROCALL professional association and the ICALL Special Interest Group[82] within the CALICO professional association. The EUROCALL NLP SIG also maintains a Ning.[83]

Impact

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The question of the impact of CALL in language learning and teaching has been raised at regular intervals ever since computers first appeared in educational institutions (Davies & Hewer 2011: Section 3).[84] Recent large-scale impact studies include the study edited by Fitzpatrick & Davies (2003)[85] and the EACEA (2009) study,[86] both of which were produced for the European Commission.

A distinction needs to be made between the impact and the effectiveness of CALL. Impact may be measured quantitatively and qualitatively in terms of the uptake and use of ICT in teaching foreign languages, issues of availability of hardware and software, budgetary considerations, Internet access, teachers' and learners' attitudes to the use of CALL,[87] changes in the ways in which languages are learnt and taught, and paradigm shifts in teachers' and learners' roles. Effectiveness, on the other hand, usually focuses on assessing to what extent ICT is a more effective way of teaching foreign languages compared to using traditional methods – and this is more problematic as so many variables come into play. Worldwide, the picture of the impact of CALL is extremely varied. Most developed nations work comfortably with the new technologies, but developing nations are often beset with problems of costs and broadband connectivity. Evidence on the effectiveness of CALL – as with the impact of CALL – is extremely varied and many research questions still need to be addressed and answered. Hubbard (2002) presents the results of a CALL research survey that was sent to 120 CALL professionals from around the world asking them to articulate a CALL research question they would like to see answered. Some of the questions have been answered but many more remain open.[88] Leakey (2011) offers an overview of current and past research in CALL and proposes a comprehensive model for evaluating the effectiveness of CALL platforms, programs and pedagogy.[89]

A crucial issue is the extent to which the computer is perceived as taking over the teacher's role. Warschauer (1996: p. 6) perceived the computer as playing an "intelligent" role, and claimed that a computer program "should ideally be able to understand a user's spoken input and evaluate it not just for correctness but also for appropriateness. It should be able to diagnose a student's problems with pronunciation, syntax, or usage and then intelligently decide among a range of options (e.g. repeating, paraphrasing, slowing down, correcting, or directing the student to background explanations)."[22] Jones C. (1986), on the other hand, rejected the idea of the computer being "some kind of inferior teacher-substitute" and proposed a methodology that focused more on what teachers could do with computer programs rather than what computer programs could do on their own: "in other words, treating the computer as they would any other classroom aid".[90] Warschauer's high expectations in 1996 have still not been fulfilled, and currently there is an increasing tendency for teachers to go down the route proposed by Jones, making use of a variety of new tools such as corpora and concordancers, interactive whiteboards[3] and applications for online communication.[4]

Since the advent of the Web there has been an explosion in online learning, but to what extent it is effective is open to criticism. Felix (2003) takes a critical look at popular myths attached to online learning from three perspectives, namely administrators, teachers and students. She concludes: "That costs can be saved in this ambitious enterprise is clearly a myth, as are expectations of saving time or replacing staff with machines."[91]

As for the effectiveness of CALL in promoting the four skills, Felix (2008) claims that there is "enough data in CALL to suggest positive effects on spelling, reading and writing", but more research is needed in order to determine its effectiveness in other areas, especially speaking online. She claims that students' perceptions of CALL are positive, but she qualifies this claim by stating that the technologies need to be stable and well supported, drawing attention to concerns that technical problems may interfere with the learning process. She also points out that older students may not feel comfortable with computers and younger students may not possess the necessary meta-skills for coping effectively in the challenging new environments. Training in computer literacy for both students and teachers is essential, and time constraints may pose additional problems. In order to achieve meaningful results she recommends "time-series analysis in which the same group of students is involved in experimental and control treatment for a certain amount of time and then switched – more than once if possible".[92]

Types of technology training in CALL for language teaching professionals certainly vary. Within second language teacher education programs, namely pre-service course work, we can find "online courses along with face-to-face courses", computer technology incorporated into a more general second language education course, "technology workshops","a series of courses offered throughout the teacher education programs, and even courses specifically designed for a CALL certificate and a CALL graduate degree"[93] The Organization for Economic Cooperation and Development has identified four levels of courses with only components, namely "web-supplemented, web-dependent, mixed mod and fully online".[94]

There is a rapidly growing interest in resources about the use of technology to deliver CALL. Journals that have issues that "deal with how teacher education programs help prepare language teachers to use technology in their own classrooms" include Language Learning and Technology (2002), Innovations in Language Learning and Teaching (2009) and the TESOL international professional association's publication of technology standards for TESOL includes a chapter on preparation of teacher candidates in technology use, as well as the upgrading of teacher educators to be able to provide such instruction. Both CALICO and EUROCALL have special interest groups for teacher education in CALL.[95]

Professional associations

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The following professional associations are dedicated to the promulgation of research, development and practice relating to the use of new technologies in language learning and teaching. Most of them organise conferences and publish journals on CALL.[96]

  • APACALL: The Asia-Pacific Association for Computer-Assisted Language Learning. APACALL publishes the APACALL Book Series and APACALL Newsletter Series.
  • AsiaCALL: The Asia Association of Computer Assisted Language Learning, Korea. AsiaCALL publishes the AsiaCALL Online Journal.
  • Association of University Language Centres (AULC) in the UK and Ireland.
  • CALICO: Established in 1982. Currently based at Texas State University, USA. CALICO publishes the CALICO Journal.
  • EUROCALL: Founded by a group of enthusiasts in 1986 and established with the aid of European Commission funding as a formal professional association in 1993. Currently based at the University of Ulster, Northern Ireland. EUROCALL's journal, ReCALL, is published by Cambridge University Press. EUROCALL also publishes the EUROCALL Review.
  • IALLT: The US-based International Association for Language Learning Technology, originally known as IALL (International Association for Learning Labs). IALLT is a professional organisation dedicated to promoting effective uses of media centres for language teaching, learning, and research. IALLT published the IALLT Journal until 2018. In early 2019, IALLT officially merged the journal into The FLTMAG [2].
  • IATEFL: The UK-based International Association of Teachers of English as a Foreign Language. IATEFL embraces the Learning Technologies Special Interest Group (LTSIG) and publishes the CALL Review newsletter.
  • JALTCALL: Japan. The JALT CALL SIG publishes The JALT CALL Journal.
  • IndiaCALL:The India Association of Computer Assisted Language Learning. IndiaCALL is an affiliate of AsiaCALL, an associate of IATEFL, and an IALLT Regional Group.
  • LET: The Japan Association for Language Education and Technology (LET), formerly known as the Language Laboratory Association (LLA), and now embraces a wider range of language learning technologies.
  • PacCALL: The Pacific Association for Computer Assisted Language Learning, promoting CALL in the Pacific, from East to Southeast Asia, Oceania, across to the Americas. Organises the Globalization and Localization in Computer-Assisted Language Learning (GLoCALL) conference jointly with APACALL.
  • TCLT: Technology and Chinese Language Teaching, an organization of Chinese CALL studies in the United States, with biennial conference and workshops since 2000 and a double blind, peer-reviewed online publication-Journal of Technology and Chinese Language Teaching since 2010 and in-print supplement Series of Technology and Chinese Language Teaching in the U.S. with China Social Sciences Press since 2012.
  • WorldCALL: A worldwide umbrella association of CALL associations. The first WorldCALL conference was held at the University of Melbourne in 1998. The second WorldCALL conference took place in Banff, Canada, 2003. The third WorldCALL took place in Japan in 2008. The fourth WorldCALL conference took place in Glasgow, Scotland, 2013. The fifth WorldCALL conference took place in Concepción, Chile in 2018.

See also

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References

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Revisions and contributorsEdit on WikipediaRead on Wikipedia

Computer-assisted language learning

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Computer-assisted language learning (CALL) is a subfield of that investigates the integration of digital technologies to mediate and enhance language teaching and learning processes. It involves the use of computer-based tools, such as interactive software, applications, and networked platforms, to deliver instructional stimuli, elicit learner responses, and provide immediate feedback, thereby supporting skills like vocabulary acquisition, grammar practice, and . CALL originated in the with early experiments using mainframe computers under behaviorist pedagogical paradigms, but it gained prominence in the through the development of custom language learning programs on personal computers. Its evolution has paralleled technological advancements, shifting from structural approaches in the – (emphasizing drill-and-practice exercises) to communicative models in the – (focusing on interaction via tools like and chat), integrative phases in the 2000s (incorporating and web-based resources), and ecological perspectives since the 2010s (viewing within broader sociocultural contexts). As of 2023, CALL research had expanded exponentially, with over 5,600 scholarly articles published between 1979 and 2023, reflecting its interdisciplinary roots in , , and . Key components of CALL include (CMC) for real-time interaction, mobile-assisted language learning (MALL) via smartphones and apps, and data-driven learning approaches that leverage corpora for authentic language exposure. Emerging trends emphasize personalized and adaptive systems powered by (AI), such as intelligent tutoring systems and technologies, alongside immersive virtual environments and generative AI tools to foster and equity in diverse educational settings. Dedicated journals like CALICO Journal (established 1983) and Computer Assisted Language Learning (1990) have documented these developments, highlighting CALL's role in addressing global challenges.

Definitions and Scope

Definition of CALL

Computer-assisted language learning (CALL) is defined as the use of computers and digital technologies to facilitate teaching and learning, focusing on interactive applications that support pedagogical objectives in second and . This approach encompasses a broad range of tools, from software programs to resources, designed to create engaging environments that go beyond rote memorization toward meaningful use. The term emphasizes the computer's role as an aid rather than a replacement for human instruction, integrating technology to enhance accessibility and effectiveness in . The core objectives of CALL include improving the four primary language skills—listening, speaking, reading, and writing—while fostering and aligning digital tools with established pedagogical frameworks. By providing individualized learning paths, CALL enables learners to practice at their own pace, receive tailored exercises, and develop self-directed strategies for language mastery. These goals are achieved through the integration of technology that supports , cultural understanding, and real-world application, ultimately aiming to make more dynamic and learner-centered. In distinction from traditional language teaching methods, which often rely on teacher-led, classroom-based instruction with limited opportunities for practice, CALL leverages to deliver immediate feedback, simulate authentic communication scenarios, and enable data-driven . This allows for repetitive, low-stakes practice without the constraints of time or , promoting deeper engagement and retention through adaptive algorithms and elements. Unlike static textbooks or lectures, CALL environments adapt to progress, offering simulations of real-life interactions that build confidence and fluency. The term "computer-assisted language learning" was coined in the early 1980s, evolving from earlier concepts such as computer-aided instruction (CAI) that focused on drill-and-practice models in the and . Its initial usage appeared in academic discussions around , marking a shift toward more interactive and integrative uses of in . Computer-assisted language learning (CALL) is often distinguished from computer-assisted instruction (CAI), which refers to broader educational applications of computers for drill-and-practice in various subjects, whereas CALL specifically targets with an emphasis on developing through interactive and contextualized activities. This focus in CALL shifts from rote instruction to learner-centered exploration of linguistic structures and real-world usage, as articulated in foundational conceptualizations of the field. In contrast, mobile-assisted learning (MALL) represents a specialized subset of CALL, confined to portable devices such as smartphones and tablets, which enable anytime, anywhere access but introduce constraints like smaller screens and limited processing power that differentiate it from the more comprehensive computing environments of traditional CALL. While CALL encompasses desktop, networked, and systems for structured practice, MALL prioritizes informal, on-the-go learning through apps and short-form content, often blurring formal and informal boundaries. Technology-assisted language learning (TALL) serves as a broader umbrella term that extends beyond CALL by incorporating a wider array of technological tools, including non-computer-based devices like audio players or interactive whiteboards, which lack the full computational capabilities central to CALL. As a precursor to TALL, CALL specifically leverages computer hardware and software for , whereas TALL reflects the evolution toward diverse digital and analog technologies in the digital age. Intelligent CALL (ICALL) constitutes an advanced subset of CALL that integrates techniques, such as and adaptive algorithms, to provide personalized feedback and dynamic interactions tailored to learner needs, unlike the more static elements of CALL systems. This AI-driven approach enables ICALL to model learner proficiency and generate contextually relevant responses, enhancing the pedagogical depth beyond conventional CALL applications. The acronym CALL itself was popularized through Michael Levy's seminal 1997 work, which defined it as the search for and study of computer applications in teaching and learning, building on earlier terms like computer-assisted instruction (CALI) that emerged as subsets of general CAI in the and .

Historical Development

Early Beginnings (Pre-1990s)

The origins of computer-assisted language learning (CALL) trace back to the , when early experiments with mainframe computers introduced interactive drills for language instruction. The (Programmed Logic for Automatic Teaching Operations) system, developed at the University of Illinois in 1960 by Donald L. Bitzer, represented a pioneering effort in this domain. PLATO utilized a central mainframe connected to multiple terminals, enabling students to engage in individualized language exercises such as grammar drills, vocabulary building, and translation tasks across various languages, including French, German, and Spanish. By the late 1970s, the system supported over 50,000 student hours of language instruction per semester, demonstrating its scale in academic settings. The marked a distinctly behaviorist phase in CALL, heavily influenced by structuralist linguistics and audio-lingual teaching methods that emphasized repetition and pattern practice. Software during this period focused on drill-and-practice formats, where computers acted as tireless tutors providing immediate feedback on responses to rules, sentence patterns, and items, allowing learners to progress at their own pace. As noted by Warschauer and Healey, this approach viewed as a process of habit formation through stimulus-response reinforcement, with programs like those on exemplifying repetitive exercises that mirrored language laboratory techniques. Early adopters, including researchers at institutions like the University of Stony Brook, integrated these tools into curricula, though adoption remained limited to universities with access to costly mainframe technology. Hardware limitations severely constrained early CALL applications, confining interactions to text-based interfaces due to the absence of capabilities, slow processing speeds, and reliance on noisy teletypes or basic keyboards. These technical barriers prevented integration or dynamic simulations, resulting in rigid, linear programs that prioritized rote memorization over . Despite these shortcomings, the era laid foundational groundwork by demonstrating computers' potential for personalized instruction. The saw formal recognition of CALL through the establishment of professional organizations and conferences, such as the founding of in 1983 and the first International CALL Conference in , , in 1985, followed by EUROCALL's inception in 1986. These events fostered collaboration among educators and marked the field's emergence as a distinct .

Modern Evolution (1990s-2025)

The marked a pivotal shift in computer-assisted learning (CALL) from behaviorist drill-and-practice approaches to more communicative and integrative paradigms, emphasizing interactive and authentic use. According to Warschauer's typology, the communicative phase, spanning the late 1970s to the , focused on learner-centered activities that promoted intrinsic motivation and real-world communication, such as simulations and games like or Sleuth to stimulate discussion. This evolution was facilitated by the advent of multimedia CD-ROMs, which enabled richer, interactive software incorporating audio, video, and hypermedia for integrated skill development. By the mid-, a wide array of CD-ROM-based programs, such as the Who is Oscar Lake? series, provided immersive simulations that contrasted with the hardware-limited tools of earlier decades. The integrative phase, emerging in the mid-, further integrated these technologies with the , allowing access to hypermedia environments and global communication tools like and MOOs for authentic materials and learner control. The 2000s saw the boom transform CALL into web-based platforms, expanding access to collaborative and resources beyond standalone software. Early web-based tools, such as Stone's online versions launched in the late 2000s (e.g., TOTALe in 2009) and platforms like LiveMocha (2007), served as precursors to modern apps by offering interactive lessons, , and community forums for practice. These developments aligned with Warschauer's integrative CALL, incorporating elements like blogs, wikis, and podcasts to foster social interaction and in language learning. Online forums and early MOOCs also emerged, enabling asynchronous discussions and resource sharing, which democratized access but highlighted initial digital divides in connectivity. In the , the proliferation of mobile devices integrated CALL with and models, making language practice ubiquitous and personalized. Apps like , launched in 2011, built on 2000s precursors by gamifying lessons with adaptive algorithms and social sharing features, reaching millions through accessibility. Other platforms, such as (2010) and (mobile expansion in the early ), emphasized and integration, supporting environments where mobile tools complemented classroom instruction. features, including groups on platforms like HelloTalk (2012), enhanced peer interaction, while studies showed improved engagement in hybrid settings combining apps with traditional pedagogy. From 2020 to 2025, the accelerated CALL's adoption of AI-driven personalization and hybrid learning, with large language models (LLMs) emerging as key tools for intelligent tutoring. Post-pandemic shifts emphasized AI integrations like chatbots and adaptive systems powered by models such as GPT variants, enabling real-time feedback and conversational practice in remote settings. For instance, LLMs facilitated automated grading and simulation, enhancing in online courses. However, the pandemic underscored equity challenges, as the exacerbated access disparities for low-income and rural learners lacking devices or , prompting calls for inclusive policies in hybrid CALL implementations. Key milestones include the 1996 of Warschauer's seminal typology paper, which formalized these phases, and the 2020s surge in generative AI research, with 30 studies in top CALL journals on generative AI applications by 2024.

Pedagogical Frameworks

Typology and Phases

One of the most influential typologies for understanding the evolution of computer-assisted learning (CALL) is that proposed by Warschauer and Healey, which divides its development into three phases aligned with prevailing pedagogical paradigms and technological capabilities. The first phase, behavioristic or structural CALL (1960s to 1970s), emphasized repetitive drills and pattern practice, reflecting behaviorist learning theories where the computer acted primarily as a tutor providing immediate feedback on discrete items, such as rules and , with limited opportunities for creative use. This approach was constrained by early computing technology, like mainframe systems, which supported programmed instruction but not dynamic interaction. The second phase, communicative CALL (late 1970s to early 1980s), shifted toward fostering meaningful interactions and simulations, drawing on principles that prioritized fluency and context over accuracy alone. Here, computers facilitated learner-centered activities, such as branching dialogues and role-plays, enabling users to generate responses in a more naturalistic manner, though still often within predefined scripts; this phase corresponded to the rise of personal computers and early software that supported communicative affordances. The third phase, integrative CALL (late 1980s onward), integrated and internet-based tools for authentic, task-oriented learning, influenced by constructivist theories that view as a social and contextual process. in this era, including hypermedia and networked environments, allowed for holistic skill development through real-world tasks, blurring lines between language practice and content exploration, with the computer serving as both tutor and tool to support collaborative and individualized learning.
PhaseTime PeriodKey CharacteristicsAssociated Learning TheoryTechnology AffordancesTeacher/Learner Roles
Behavioristic/Structural1960s–1970sDrill-and-practice; focus on accuracy and repetition (stimulus-response-reinforcement)Mainframes, simple software for Teacher-centered: Computer as master; learner as passive responder
CommunicativeLate 1970s–early 1980sSimulations, interactions for meaning; emphasis on ()Personal computers, branching programsBalanced: Shift to ; teacher as facilitator
IntegrativeLate 1980s+Authentic tasks, integration; holistic skillsConstructivism (social construction of knowledge), hypermedia, collaborative toolsLearner-centered: Computer as scaffold; teacher as guide
This table summarizes Warschauer's typology, highlighting shifts in roles from teacher-dominated instruction to learner-driven exploration. Michael Levy's model builds on similar phase distinctions, framing CALL within a tutor-tool continuum that evolves across behaviorist, communicative, and integrative stages, with explicit criteria such as evolving roles from authoritative overseers to supportive integrators, and learner roles from rote memorizers to active constructors of knowledge, emphasizing how technology mediates these dynamics. Alternative typologies offer complementary perspectives; for instance, Bax proposes restricted, open, and integrated phases, where the restricted stage mirrors behavioristic constraints, the open stage encourages exploratory tool use akin to communicative approaches, and the integrated stage parallels Warschauer's final phase but stresses normalization of in everyday . These typologies are identified through criteria like technology affordances—ranging from rigid input-output systems to adaptive networks—and underlying learning theories, from stimulus-response models to sociocultural ones, ensuring phases reflect both and pedagogical alignment. Since the 2000s, CALL has transitioned toward a post-integrative or fourth phase, often termed Intelligent CALL (ICALL), incorporating to enhance through adaptive, contextually rich environments that simulate real-world use with personalized feedback on open-ended inputs. This evolution builds on prior phases by leveraging -driven tools, such as intelligent tutoring systems, to detect learner needs and apply pedagogical strategies dynamically, fostering more authentic and individualized learning experiences. Recent developments (2023–2025) include generative for task-based interactions and models like C.H.A.T.S. (Conversational, Holistic, Authentic, Transformative, Situated) for -enhanced learning, as well as the Interactive Pedagogical Model of Language Learning (IPMLL) integrating CALL with -assisted learning (AIALL).

Software Design and Pedagogy

Software design in computer-assisted language learning (CALL) emphasizes the integration of pedagogical principles to support effective , drawing heavily from constructivist theories that view learning as an active process of knowledge construction through social interaction and experience. Constructivism posits that learners build understanding by engaging with meaningful tasks, where software facilitates this by providing dynamic environments that encourage exploration and collaboration. A key application is , which aligns with Vygotsky's (ZPD)—the gap between what learners can achieve independently and with guidance—allowing CALL tools to offer graduated support that fades as proficiency grows, such as adaptive prompts in interactive exercises that guide users toward self-correction. This integration ensures that software not only delivers content but also promotes learner agency, mirroring real-world language use in collaborative digital spaces. Central to CALL software design are principles of usability, adaptability, and accessibility, which ensure that interfaces support diverse learning needs without overwhelming users. Usability focuses on intuitive interfaces that minimize , enabling learners to concentrate on language tasks rather than navigating complex menus—for instance, through clear visual hierarchies and responsive feedback loops that align with flows. Adaptability involves paths, where algorithms adjust difficulty based on user performance, such as branching scenarios in grammar drills that escalate complexity within a learner's ZPD. Accessibility principles, guided by standards like the (WCAG), ensure equitable access for users with disabilities, incorporating features like compatibility and customizable text sizes to accommodate visual or motor impairments in language practice tools. Evaluation frameworks for CALL software prioritize pedagogical efficacy alongside technical functionality, with Chapelle's criteria serving as a foundational model for assessing task quality. These include language learning potential (the potential for meaningful language input and output), learner fit (alignment with individual proficiency and goals), authenticity (simulation of real communicative contexts), positive impact (motivation and reduced anxiety), meaning focus (emphasis on over rote memorization), and practicality (feasibility in resource-constrained settings). This framework operationalizes constructivist pedagogy by evaluating how well software tasks foster interactionist learning, such as through authentic dialogues that encourage negotiation of meaning. Empirical studies applying these criteria have demonstrated that well-evaluated CALL tools improve outcomes in retention and when authenticity and learner fit are high. Feedback mechanisms in CALL software play a pivotal role in reinforcing learning, with the timing—immediate versus delayed—tailored to pedagogical goals like error correction and retention. Immediate feedback, provided right after an error (e.g., highlighting a grammatical mistake during a writing exercise), supports quick self-correction and is particularly effective for , such as syntax rules, by reducing in real-time. Delayed feedback, delivered after task completion (e.g., a summary report analyzing patterns in spoken responses), enhances long-term retention by encouraging metacognitive reflection, though it may increase initial frustration if not scaffolded. analysis algorithms underpin these mechanisms, often employing rule-based or approaches to detect patterns; for example, a simple for basic syntactic detection might involve:

if input_sentence matches pattern "subject-verb disagreement": flag_error(type="[syntax](/page/Hungarian_noun_phrase)", location=verb_position) suggest_correction("Adjust [verb](/page/Verb) to plural form")

if input_sentence matches pattern "subject-verb disagreement": flag_error(type="[syntax](/page/Hungarian_noun_phrase)", location=verb_position) suggest_correction("Adjust [verb](/page/Verb) to plural form")

Meta-analyses confirm that while immediate feedback boosts short-term accuracy in CALL environments, a hybrid approach combining both timings optimizes overall proficiency gains. Ethical considerations in CALL software design are paramount, particularly regarding data in learner tracking, to prevent misuse of sensitive performance data that could stigmatize users or enable unauthorized profiling. Tracking features, such as logging interaction patterns for adaptive personalization, must adhere to principles of , data minimization, and transparency, ensuring learners understand how their progress data is collected, stored, and used—often compliant with regulations like the General Data Protection Regulation (GDPR). Breaches in , such as sharing analytics without explicit permission, raise concerns about equity, as underrepresented learners may face biased inferences from incomplete data sets. Frameworks for ethical design advocate anonymization techniques and regular audits to safeguard , fostering trust in CALL tools as supportive rather than surveillant environments.

Core Technologies

Multimedia Applications

Multimedia applications in computer-assisted language learning (CALL) incorporate audio, video, and graphical elements to support through multi-sensory engagement, allowing learners to practice skills in a controlled, interactive environment. These tools emerged prominently in the with the advent of affordable hardware, enabling the delivery of rich content on standalone platforms like CD-ROMs, which bundled high-quality audio clips, video segments, and animations for offline use. For instance, early CALL programs such as Athelstan's Interactive Language Series utilized CD-ROMs to present dialogues with synchronized audio and visual cues, promoting comprehension without requiring internet connectivity. By the , the shift to streaming capabilities on personal computers expanded access to dynamic content, though standalone applications retained value for structured, self-paced learning in resource-limited settings. Integration of audio and video components has been central to pronunciation training within multimedia CALL, where software analyzes learners' speech through waveform visualization and provides targeted feedback. This approach leverages speech recognition algorithms to score pronunciation accuracy, with studies showing improvements in segmental and suprasegmental features after regular use. Visual aids, including animations and hypermedia structures, further enhance grammar instruction by making abstract concepts tangible and navigable. Animations visualize grammatical processes, such as sentence formation or tense shifts, through dynamic sequences that depict word order or morphological changes, aiding conceptual grasp for visual learners. Hypermedia environments, prevalent in 1990s CALL software like stacks, allow non-linear navigation via linked nodes of text, images, and audio, enabling users to explore topics at their own pace—such as branching from a vocabulary item to related grammar rules or examples. This design supports individualized paths, reducing cognitive overload by presenting information in interconnected, bite-sized modules. The pedagogical benefits of these multimedia elements are grounded in dual-coding theory, which posits that combining verbal (audio/text) and non-verbal (visual/video) representations strengthens memory retention and recall by creating dual pathways in the brain. In language learning, this manifests in improved vocabulary acquisition and listening comprehension, as learners process information multimodally; for instance, interactive videos that pause for user responses during dialogues reinforce listening skills while associating spoken input with contextual visuals. Empirical evidence indicates that such applications yield moderate effect sizes in skill development, with retention rates up to 20% higher compared to text-only methods. Tools like Hot Potatoes exemplify this by generating multimedia quizzes with embedded audio prompts and image-based matching exercises, facilitating immediate feedback and repetition without network dependency. Recent advancements as of 2025 include AI-enhanced multimedia for personalized pronunciation feedback in CALL apps.

Internet and Web-Based Tools

The advent of the marked a pivotal shift in computer-assisted language learning (CALL) by enabling dynamic, interconnected environments that extended beyond standalone software. In the Web 1.0 era of the , static webpages provided basic access to language resources such as glossaries, drills, and reading materials, often through simple pages that learners navigated independently. This phase emphasized content delivery, with early platforms like early educational portals offering downloadable exercises that built on applications by adding hyperlinks for contextual exploration. The transition to in the mid-2000s introduced interactive and , transforming CALL into a participatory process where learners could create blogs, wikis, and podcasts to practice writing and speaking skills in authentic contexts. Tools like Blogger and allowed students to collaborate on language production tasks, fostering through iterative feedback loops. Collaborative features further revolutionized web-based CALL by facilitating real-time interaction among learners worldwide. Tandem language exchanges, where partners mutually teach each other their native languages, gained prominence through platforms like and online forums, enabling voice and text-based conversations that enhanced oral proficiency and cultural understanding. Studies on web-based tandem programs have shown improvements in willingness to communicate and speaking skills among English as a (EFL) learners, with participants reporting increased confidence after regular virtual exchanges. Forums such as those on language-specific sites or integrated into learning management systems like supported asynchronous discussions, allowing learners to post queries, share resources, and receive peer corrections, which promoted deeper engagement than isolated interactions. Open educational resources expanded access to structured web-based CALL through massive open online courses (MOOCs), which democratized high-quality language instruction. Platforms like and offer courses in languages such as Spanish, Mandarin, and French, featuring video lectures, quizzes, and peer-reviewed assignments that align with principles. Language MOOCs (LMOOCs) on these sites have enrolled millions, emphasizing intercultural competence through interactive modules. These resources provide scalable, self-paced learning, though completion rates are generally low, often around 3-6% due to motivational challenges. While web-based tools offer unprecedented global reach—connecting over 5.3 billion internet users as of 2023—they exacerbate the , limiting equitable participation in CALL. In low-income countries, approximately 24% of the population had in 2022, hindering learners' ability to engage with online exchanges or MOOCs compared to their high-income counterparts. A 2025 review on the in online education highlighted that socio-economic status influences not just access but also , with underserved students in developing countries facing barriers to tools like video conferencing for tandem practice. This disparity underscores the need for hybrid models to bridge gaps in web-based language learning. Recent web-based CALL developments as of 2025 include AI-driven adaptive platforms for personalized tandem interactions. A representative example of web-based CALL is , an inquiry-oriented framework where learners use the to complete task-based projects, such as researching cultural topics and presenting findings. Developed in the late 1990s, WebQuests integrate scaffolded web searches with collaborative reporting, improving and language output in EFL contexts. Empirical studies demonstrate that WebQuest activities enhance and writing skills, as students synthesize online information into coherent narratives. By structuring exploration around real-world tasks, WebQuests exemplify how web tools support constructivist in .

Corpora and Concordancers

In computer-assisted language learning (CALL), corpora refer to large, structured collections of authentic language data, typically comprising millions of words from written and spoken sources, which provide learners with real-world examples of language use. A prominent example is the (BNC), a 100-million-word database of late 20th-century drawn from diverse genres, including newspapers, books, and conversations, enabling analysis of natural linguistic patterns. These resources support data-driven learning (DDL), where learners explore corpus evidence to discover rules and usages inductively rather than through direct instruction. Concordancers are specialized software tools that query corpora to generate keyword-in-context (KWIC) lines, displaying search terms amid surrounding text to reveal collocations, frequencies, and . For instance, AntConc, a concordancer developed by Laurence Anthony, allows users to perform rapid searches on uploaded texts or corpora, facilitating the study of vocabulary patterns such as verb-preposition pairings in English. In CALL applications, concordancers aid error correction by highlighting low-frequency or atypical usages in learner writing, such as inappropriate collocations, and support classroom activities like creating gap-fill exercises from authentic corpus extracts to reinforce grammatical awareness. The evolution of these tools in CALL began in the 1990s with CD-ROM-based corpora, which offered limited but accessible data for early DDL experiments in and . Since the , web-based platforms like have provided intuitive interfaces for querying massive, multilingual corpora and generating word sketches—summaries of a term's typical associations—enhancing mobile and environments. This progression has broadened access, with systematic reviews showing increased integration of corpora in CALL research from 2011 to 2015, emphasizing their role in personalized language analysis. Pedagogically, corpora and concordancers promote inductive learning by encouraging learners to infer patterns from evidence, shifting from rote memorization to exploratory discovery of authentic features like idioms and register variations. This approach fosters deeper comprehension and autonomy, as evidenced in EFL contexts where concordancer use improved accuracy by up to 20% in controlled studies, while integrating DDL into curricula enhances motivation through hands-on data exploration.

Immersive and Interactive Tools

Flashcards and Adaptive Systems

Digital flashcards have become a cornerstone of computer-assisted language learning (CALL), evolving from traditional paper-based systems to sophisticated mobile and web applications that leverage algorithmic scheduling for efficient vocabulary acquisition. The foundational , introduced in 1972, organizes flashcards into boxes based on learner performance, promoting spaced review by advancing cards to less frequent intervals upon successful recall and regressing them otherwise; this approach has been digitized in modern tools, allowing automated management of thousands of items. Applications like Anki, released in 2006, exemplify this transition by implementing an adapted version of the through customizable decks tailored for (L2) vocabulary, enabling learners to create or import content for targeted practice in areas such as academic word lists. Adaptive learning systems in CALL build on these foundations by dynamically adjusting flashcard presentation based on individual performance, often incorporating principles from the Ebbinghaus forgetting curve to optimize retention. This curve, derived from Hermann Ebbinghaus's 1885 experiments, models memory decay as an exponential function: R=e−t/sR = e^{-t/s}, where RR represents retention probability, tt is the time elapsed since learning, and ss denotes memory strength influenced by factors like repetition and item difficulty. In practice, algorithms in apps like Anki use user ratings (e.g., on a 1-5 ease scale) to estimate ss and schedule reviews just before anticipated forgetting, thereby extending intervals for well-mastered items while intensifying exposure for challenging ones; advanced models further adapt by factoring in linguistic features such as word frequency or concreteness to predict recall more accurately. Contemporary digital flashcards enhance engagement through multimedia integration and elements, transforming rote memorization into interactive experiences. Many platforms support embedding audio clips for practice, images for visual association, and even cloze deletions for contextual sentence building, allowing learners to process multimodally and reinforce multiple skills simultaneously. features, such as points awarded for correct answers, badges for milestones, and competitive leaderboards, further motivate sustained use; for instance, incorporates game modes like "Match" and "" to simulate timed challenges, while Brainscape employs confidence-based repetition with progress tracking to foster a sense of achievement. Empirical studies demonstrate the efficacy of these systems in boosting vocabulary retention compared to traditional methods. In one investigation with college-level English as a (ESL) learners using Anki for daily 10-minute sessions over three weeks, pretest scores on an averaged 19.3, rising significantly to 23.6 post-intervention (p = 0.002), representing approximately a 22% gain and enabling all participants to master at least 50% of targeted items. Similarly, low-proficiency Thai EFL students employing Quizlet's gamified flashcards over 10 weeks improved vocabulary test scores from a mean of 10.94 to 13.11 out of 15 (p < 0.001), underscoring spaced repetition's role in achieving 20-30% retention advantages through . Despite these benefits, digital flashcards in CALL have limitations, particularly their tendency to overemphasize isolated acquisition at the expense of integrated language skills like or . While effective for such as word forms and meanings, they often present items decontextualized, potentially hindering learners' ability to apply in communicative contexts without supplementary activities. Additionally, reliance on user motivation can lead to inconsistent engagement if elements fail to sustain interest over time.

Virtual Worlds and Simulations

Virtual worlds and simulations represent a significant in computer-assisted language learning (CALL), providing immersive environments where learners can engage in authentic, interactive language practice beyond traditional classroom constraints. These technologies enable users to inhabit digital spaces that mimic real-world scenarios, fostering through and contextual interactions. Early adoption in the 2000s focused on platforms like , which allowed for collaborative language activities in a persistent virtual universe. By simulating social and cultural contexts, these tools shift language learning from rote memorization to experiential engagement, drawing on foundations for enhanced . Platforms such as , launched in the early 2000s, have been widely used for role-playing activities in , enabling learners to create avatars and participate in simulated conversations, debates, and cultural exchanges. Research highlights how supports task-based language learning by facilitating spontaneous interactions among global users, promoting vocabulary acquisition and pragmatic skills in low-stakes environments. In more recent developments, modern (VR) systems like Oculus (now Meta Quest) have expanded these capabilities, offering head-mounted displays for fully immersive conversational scenarios. For instance, applications built for allow learners to navigate virtual classrooms or social settings, practicing dialogues with AI or human interlocutors in 360-degree environments. Simulations within these virtual worlds emphasize scenario-based training, such as virtual travel experiences that immerse learners in target-language cultures, from navigating foreign markets to participating in historical events. These setups encourage practical application of language skills, like negotiating in a simulated or ordering in a , thereby building fluency through repeated, contextual exposure. Key benefits include reduced speaking anxiety, as learners practice without real-world repercussions, leading to increased confidence and willingness to communicate. Spatial audio features in VR further aid by providing directional sound cues that replicate natural conversations, enhancing listening comprehension and phonetic accuracy. In the 2020s, advances in (AR) have complemented VR by overlaying digital translations, subtitles, or interactive prompts onto real-world views via mobile devices or glasses, enabling seamless during everyday activities. AR apps, such as those integrating with smartphones, allow users to scan objects for instant vocabulary feedback or engage in mixed-reality dialogues, bridging virtual simulations with physical environments. As of 2025, ongoing research emphasizes gamified VR applications, demonstrating enhanced motivation and in through platforms compatible with advanced headsets like Meta Quest 3. Meta-analyses of VR and AR in language learning confirm improved oral and overall proficiency, with effect sizes ranging from moderate to large (e.g., 0.825 for interventions), though high setup costs for hardware and development remain a barrier to widespread , particularly in resource-limited settings. These findings underscore the potential of virtual simulations to transform CALL, despite ongoing challenges in accessibility.

Advanced and Emerging Technologies

Human Language Technologies

Human language technologies (HLT) in computer-assisted language learning (CALL) encompass (NLP) techniques and systems designed to support by analyzing, generating, and correcting linguistic input. These technologies enable automated feedback on , , and writing, allowing learners to practice independently without constant human intervention. Early HLT applications in CALL emerged in the , relying on rule-based systems that used hand-crafted linguistic rules to parse sentences and detect errors, such as early grammar checkers integrated into writing software. By the , the field shifted toward statistical models, which leveraged large corpora to probabilistically identify patterns in language use, improving accuracy and adaptability for diverse learner needs. This evolution allowed HLT to move beyond rigid rules to data-driven approaches, enhancing tools for (L2) instruction. Speech recognition technologies have been pivotal in CALL for pronunciation and dictation practice, enabling learners to receive immediate feedback on spoken output. Tools like , a speaker-dependent system, were adapted in the late for L2 English learners, where users train the software on their voice to improve accuracy in transcribing speech. Studies showed that while initial accuracy was lower for non-native speakers due to accents, repeated use facilitated self-correction and boosted oral fluency. These systems operate by converting audio to text via acoustic modeling and language models, providing phonetic feedback to target specific errors like shifts or intonation. In CALL environments, such tools integrate with exercises simulating real conversations, though limitations in handling varied dialects persist. NLP techniques form the core of writing aids in CALL, using and to identify and explain grammatical issues. For instance, precision grammars augmented with "mal-rules"—inverted rules that detect common learner errors—power tools like , which analyzes English sentences for issues such as subject-verb agreement or preposition misuse. This rule-based , rooted in 1990s , tags words (e.g., , ) and builds syntactic trees to pinpoint deviations from target norms, offering tailored explanations like "The should agree with the plural subject." Transitioning to statistical NLP in the , these checkers incorporated probabilistic models trained on learner corpora, achieving higher recall for idiomatic errors without exhaustive rule sets. Such applications promote conceptual understanding by highlighting patterns rather than rote correction. Machine translation (MT) integration in CALL is approached cautiously to prevent over-reliance, which could hinder deep language processing. Basic MT tools, like early versions of , are critiqued for producing literal translations that ignore context or idiomatic expressions, potentially reinforcing misconceptions in L2 output. Research indicates that while MT aids lookup or , excessive use correlates with reduced grammatical accuracy in learner writing, as students may copy flawed outputs without analysis. In limited applications, MT supports exercises, where learners compare human and machine versions to critique errors, fostering critical evaluation skills. This restrained use aligns with pedagogical goals, emphasizing MT as a supplementary resource rather than a crutch. Applications of HLT extend to automatic essay scoring (AES), where NLP evaluates L2 writing against rubrics for coherence, vocabulary, and . Systems like e-rater employ statistical features—such as n-gram usage and error rates—to assign scores correlating 0.7-0.9 with human graders, focusing on holistic traits like development and mechanics. In CALL, AES provides scalable feedback for large classes, using rubrics to break down scores (e.g., 1-6 scale for organization), helping learners revise iteratively. Early rule-based AES in the 1990s flagged surface errors, but 2010s statistical models analyzed semantic content via , better capturing L2-specific challenges like topic relevance. This technology democratizes assessment, though it requires human oversight for cultural nuances.

Artificial Intelligence and Mobile Learning

Artificial intelligence has significantly advanced computer-assisted language learning (CALL) by enabling adaptive, interactive experiences that personalize instruction and enhance engagement, particularly through integration with mobile platforms known as mobile-assisted language learning (MALL). In the 2020s, AI-driven tools have shifted from rule-based systems to generative models, allowing for dynamic content generation and real-time interaction that simulates human-like tutoring. This evolution builds briefly on foundational human language technologies by incorporating large language models (LLMs) for more nuanced language processing. AI tutoring systems, such as chatbots, have become central to conversational practice in CALL, providing scalable opportunities for speaking and writing skills development. For instance, Duolingo's AI features, powered by , include the "" mode, where learners engage in simulated dialogues with an AI conversation partner that adapts to their proficiency level, offering contextually relevant responses to build . Similarly, the "Video Call" with the AI character Lily enables realistic video-based conversations, with studies showing improvements in speaking skills among Japanese English learners after regular use. Large language models like GPT variants further support personalized feedback by analyzing learner inputs and generating tailored explanations, such as breaking down grammatical errors or suggesting idiomatic alternatives, which has been shown to increase learner motivation and autonomy in empirical studies from 2023-2024. Mobile apps exemplify MALL by leveraging device portability and connectivity features to facilitate anytime learning. Babbel's app, for example, incorporates offline modes that allow users to download and complete lessons without , ensuring continuity during or in low-connectivity areas, while push notifications serve as reminders to maintain daily practice habits. This combination supports and contextual review, with research indicating that such features enhance retention in mobile environments compared to traditional desktop tools. Gamification in AI-enhanced CALL apps boosts engagement through adaptive mechanics that respond to user performance. Memrise employs AI to create personalized review plans with gamified elements, such as interactive tests and mnemonic-based challenges, where difficulty levels adjust in real-time based on learner progress, turning vocabulary acquisition into engaging, quest-like experiences. These adaptive games have been linked to higher retention rates, as AI algorithms prioritize weak areas while incorporating to reinforce . Recent trends in the 2020s emphasize AI for real-time correction and blended models, accelerated by post-pandemic shifts toward hybrid education. AI systems now provide instant feedback on pronunciation and syntax during live interactions, using to detect and suggest corrections, which accelerates error resolution and builds confidence, as evidenced in reviews of generative AI applications from 2023-2024. Blended AI-human , where AI handles routine practice and teachers focus on complex guidance, has gained traction post-2020, with studies showing improved learning outcomes in K-12 settings through this synergistic approach. Despite these advancements, challenges persist, including bias in AI responses and inequities in mobile access. AI language tutors can perpetuate cultural or linguistic biases from training data, such as reinforcing in generated dialogues or undervaluing non-standard dialects from learners, potentially disadvantaging diverse users. Additionally, equity issues in mobile access remain stark; as of 2025, approximately 32% of the global population—over 2.6 billion people—lacks reliable , disproportionately affecting rural and low-income learners in programs.

Impact and Evaluation

Educational Outcomes

Empirical evidence from meta-analyses conducted between 2010 and 2025 demonstrates that computer-assisted language learning (CALL) yields moderate positive effects on language proficiency, particularly in vocabulary and grammar acquisition. A 2013 meta-analysis of 37 studies encompassing 52 effect sizes found that CALL interventions produced an overall effect size of d = 0.56, indicating CALL is at least as effective as traditional methods and superior in more rigorous experimental designs, with notable gains in vocabulary through tools like glosses and interactive tasks. For vocabulary specifically, a 2018 meta-analysis reported a medium effect size of d = 0.745 for computer-assisted approaches across various L2 contexts, while a 2021 analysis of technology-assisted vocabulary learning confirmed a moderate overall effect of d = 0.64, with stronger impacts from incidental exposure (d = 1.04) compared to intentional instruction (d = 0.57). Grammar outcomes show similar moderate benefits, as evidenced by studies integrating CALL feedback mechanisms, where elaborative feedback yielded an effect size of d = 0.49, outperforming simpler corrective types. CALL's impact varies by language skill, with stronger effects observed for receptive skills such as reading and compared to productive ones like speaking and writing. In , receptive knowledge (e.g., recognition tasks) achieved a medium of d = 0.69, whereas productive knowledge (e.g., or production tasks) showed a smaller d = 0.47, highlighting CALL's advantage in input-based processing over output generation. A 2024 meta-analysis on AI-assisted L2 learning further supported this, noting significant improvements in receptive skills through automated input enhancement, though productive skills benefited more from interactive AI features like real-time conversation practice. Key factors influencing CALL effectiveness include learner age and initial proficiency level, with younger learners and those at intermediate levels often showing greater gains. For vocabulary, children (≤12 years) exhibited a larger (d = 0.85) than adults (d = 0.79), and gamified CALL applications amplified benefits for children by enhancing engagement and motivation. Intermediate proficiency learners gained more (d = 0.95) than elementary (d = 0.54) or advanced (d = 0.53) groups, suggesting CALL is particularly suited for building foundational to mid-level competencies. Longitudinal studies underscore CALL's role in long-term retention, especially with AI-integrated tools that promote sustained engagement. Research on and gamified CALL showed improved vocabulary and retention over months, with AI-driven adaptive systems maintaining gains through personalized , as seen in interventions where learners retained up to 80% of acquired items after 60 days. As of 2025, (ITU) data confirm ongoing challenges in retention due to access disparities, but hybrid CALL models show promise in sustaining gains post-intervention. Quantitative metrics from CALL interventions often align with Common European Framework of Reference for Languages (CEFR) improvements, with meta-analytic evidence indicating average advancements of 0.5 to 1 CEFR level in targeted skills after 20-40 hours of use. For instance, mobile-assisted CEFR-aligned vocabulary programs led to measurable shifts from C1 to higher proficiency levels in experimental groups.

Challenges and Future Directions

One major challenge in computer-assisted language learning (CALL) is the , which exacerbates unequal access to essential for effective implementation. As of , approximately 2.6 billion people—representing 32% of the global population—lack , limiting opportunities for CALL tools in low-resource contexts, particularly in developing regions where relies heavily on digital resources. Furthermore, only about 5% of students worldwide are fully engaged with ed-tech learning tools, highlighting how socioeconomic disparities hinder widespread adoption of CALL platforms. Over-reliance on technology in CALL can diminish human interaction, a core element of language acquisition that fosters nuanced communication skills. AI-driven CALL applications often lack the emotional and contextual depth of human exchanges, leading to reduced opportunities for spontaneous dialogue and cultural exchange. Pedagogical issues compound these concerns, including screen fatigue from prolonged digital exposure, which impairs concentration and increases motivational barriers during CALL sessions. Additionally, low-motivation learners experience unequal outcomes in CALL environments, as adaptive systems may fail to sustain engagement without personalized human support, resulting in lower retention and skill development compared to motivated peers. Looking ahead, the integration of technologies promises to enhance VR-based CALL by creating immersive, interactive environments that simulate real-world language use and boost learner engagement. Ethical AI development in CALL emphasizes mitigation to ensure equitable learning experiences, addressing issues like underrepresented dialects in through techniques such as diverse curation and algorithmic fairness audits. Predictions for the point to neuro-adaptive technologies, leveraging insights like brain-computer interfaces to tailor language instruction to individual cognitive patterns and improve retention. Post-2025, hybrid models combining CALL with traditional methods are expected to dominate, blending digital personalization with face-to-face interaction for more inclusive outcomes. To support these advancements, policy recommendations stress comprehensive teacher programs focused on CALL integration, including hands-on workshops and ongoing to build technological proficiency and pedagogical adaptability.

Professional Aspects

Associations and Communities

The European Association for Computer Assisted Language Learning (EUROCALL), established in 1993 with origins tracing back to collaborative initiatives in the mid-1980s, serves as a primary professional organization dedicated to advancing research, development, and practical applications in computer-assisted language learning across Europe and beyond. It organizes annual international conferences, beginning with its inaugural event at the in 1993, which facilitate the exchange of innovative ideas through presentations, workshops, and networking sessions focused on CALL technologies and pedagogies. These conferences, held each year in a different European host city—such as the 2025 event held August 27–30 in Milan, Italy—emphasize collaborative for educators and researchers. In , the Computer Assisted Language Instruction Consortium (), founded in 1983, functions as a leading scholarly organization promoting the integration of technology in language instruction, with a particular emphasis on North American contexts and global outreach. hosts annual conferences featuring workshops, paper presentations, and demonstrations of CALL tools, providing hands-on opportunities for language educators, researchers, and developers. Its 2025 conference, held May 27–31 in , , continued this tradition by highlighting advancements in technology-enhanced language learning. Both EUROCALL and CALICO foster vibrant communities through special interest groups (SIGs) and online resources that enable ongoing collaboration among members. CALICO's SIGs, for instance, cover specialized areas such as , gaming in language learning, and virtual worlds, offering forums for discussion, resource sharing, and tailored to CALL practitioners. Membership in these associations provides key benefits, including full-text access to prestigious journals—ReCALL for EUROCALL, published by , and the thrice-yearly CALICO Journal for CALICO—which disseminate cutting-edge in the field. Additional perks encompass discounted conference registrations, eligibility for funding opportunities like CALICO's annual awards for outstanding graduate students and innovative projects, and reciprocal membership discounts that enhance cross-Atlantic networking. These structures support over 400 EUROCALL members across more than 30 countries as of early records (with 288 members reported as of 2019), alongside CALICO's diverse international membership, underscoring their role in sustaining a global CALL community. Recent research trends in computer-assisted language learning (CALL) from 2020 to 2025 have increasingly emphasized the integration of (AI), with a particular focus on ethical considerations such as mitigation, data privacy, and equitable access in tools. Studies highlight growing concerns over AI-driven assessments and chatbots potentially perpetuating cultural biases in language learning platforms, prompting calls for ethical frameworks that prioritize transparency and inclusivity. Additionally, mobile-assisted language learning (MALL) has gained traction in low-resource settings, where affordable smartphones enable self-paced vocabulary and grammar practice for underserved learners in public schools and remote areas. Topic modeling analyses of CALL journals reveal a marked rise in approaches, combining online tools with traditional instruction to enhance engagement and outcomes, especially post-pandemic. Key journals dedicated to CALL research include Computer Assisted Language Learning, published by , which covers advancements in language teaching technologies and empirical studies on digital tools. The CALICO Journal, from the Computer-Assisted Language Instruction Consortium, focuses on innovative applications of technology in language instruction and assessment. These outlets frequently feature meta-analyses and experimental research on AI and mobile integrations. Databases such as (Education Resources Information Center) and provide comprehensive access to CALL meta-studies, indexing thousands of peer-reviewed articles on educational technologies and outcomes. , sponsored by the U.S. Department of Education, emphasizes practical applications in teaching, while offers broader interdisciplinary coverage for tracking citation impacts in CALL. Funding for AI-CALL projects has been supported by grants from the (NSF) in the U.S., including programs advancing AI in and STEM workforce development. In , the European Union's and Digital Europe Programme allocate resources for AI-enhanced language tools, with calls targeting ethical AI and innovations. Open resources for CALL include repositories like the CALL-EJ archive, which offers free access to articles on and practice in technology-assisted learning. For evaluation tools, the International Virtual Exchange Academy (IVEA) provides frameworks and instruments for assessing virtual exchanges and digital pedagogies. These resources facilitate collaborative development and testing of CALL materials without institutional barriers.

References

  1. https://www.oxfordbibliographies.com/display/document/obo-9780199756810/obo-9780199756810-0305.xml
  2. https://www.sciencedirect.com/science/article/pii/B0080448542009731
  3. https://www.cambridge.org/core/journals/recall/article/fortytwo-years-of-computerassisted-language-learning-research-a-scientometric-study-of-hotspot-research-and-trending-issues/6A18E83D2D3DCFA3E42AA420BB1FF018
  4. https://onlinelibrary.wiley.com/doi/10.1111/ejed.70051
  5. https://www.sciencedirect.com/science/article/pii/S2666920X25000852
  6. http://www.ict4lt.org/en/en_mod1-4.htm
  7. https://www.sciencedirect.com/topics/social-sciences/computer-assisted-language-learning
  8. https://web.stanford.edu/~efs/callcourse2/CALL4.htm
  9. https://files.eric.ed.gov/fulltext/EJ1059921.pdf
  10. https://web.stanford.edu/~efs/callcourse2/CALL1.htm
  11. https://c3superstars.wordpress.com/2010/01/25/cai-call/
  12. https://www.scribd.com/doc/212691740/f-computer-assisted-instruction-in-lang-teaching
  13. https://www.cambridge.org/core/journals/language-teaching/article/computerassisted-language-learning-mapping-the-territory-michael-levy-computerassisted-language-learning-context-and-conceptualization-oxford-clarendon-press-1997-pp-xv-298-isbn-9780198236313-paperback-carol-a-chapelle-computer-applications-in-second-language-acquisition-foundations-for-teaching-testing-and-research-cambridge-applied-linguistics-series-cambridge-cambridge-university-press-2001-pp-xvii-215-isbn-9780521626460-paperback-mike-levy-glenn-stockwell-call-dimensions-options-and-issues-in-computerassisted-language-learning-mahwah-nj-lawrence-erlbaum-2006-pp-xviii-310-isbn-080585634x-paperback/587766A91EEF22B67292AD5D8BAB23F2
  14. https://files.eric.ed.gov/fulltext/EJ1004355.pdf
  15. https://oro.open.ac.uk/57023/1/__userdata_documents5_ak35_Desktop_Accepted%2520Manuscript_Concise%2520Encyclopedia.pdf
  16. https://www.tirfonline.org/wp-content/uploads/2013/10/TIRF_MLL_Papers_StockwellHubbard_Final.pdf
  17. https://web.stanford.edu/~efs/callcourse2/Invitation-to-CALL-Unit1.pdf
  18. https://www.idpublications.org/wp-content/uploads/2020/10/Full-Paper-TECHNOLOGY-ASSISTED-FOREIGN-LANGUAGE-LEARNING-TALL-IN-THE-DIGITAL-AGE.pdf
  19. https://itlib.cvtisr.sk/wp-content/uploads/docs/46_a%2520new.pdf
  20. https://calico.org/sigs/icall/
  21. https://files.eric.ed.gov/fulltext/ED578314.pdf
  22. https://utppublishing.com/doi/10.1558/cj.v25i3.510-527
  23. https://www.ict4lt.org/en/History_of_CALL.pdf
  24. https://education.uci.edu/uploads/7/2/7/6/72769947/computers_and_language_learning-_an_overview.pdf
  25. https://eurocall-languages.org/about-us/history-of-eurocall/
  26. http://www.ict4lt.org/en/warschauer.htm
  27. http://www.camsoftpartners.co.uk/docs/LLAS_Web_Guide.htm
  28. https://medium.com/old-media-when-it-was-new/totale-how-rosetta-stone-transformed-language-learning-in-2009-f40f073697df
  29. https://www.researchgate.net/publication/241028382_Chapter_2_Technological_Change_and_the_Future_of_CALL
  30. https://www.taalhammer.com/computer-assisted-language-learning/
  31. https://www.researchgate.net/publication/313346126_A_review_of_mobile_language_learning_applications_trends_challenges_and_opportunities
  32. https://files.eric.ed.gov/fulltext/ED590366.pdf
  33. https://www.researchgate.net/publication/394190252_A_systematic_review_of_generative_AI_in_CALL_published_research_in_scholarly_journals
  34. https://formative.jmir.org/2021/4/e23233/
  35. https://books.google.com/books/about/Computer_Assisted_Language_Learning.html?id=RRGgrjteVjUC
  36. https://eclass.uoa.gr/modules/document/file.php/ENL110/callpresentpastandfuture.pdf
  37. https://iris.landsbokasafn.is/en/activities/new-periodisation-of-call/
  38. https://files.eric.ed.gov/fulltext/ED673561.pdf
  39. https://link.springer.com/article/10.1007/s10639-025-13388-w
  40. https://www.simplypsychology.org/zone-of-proximal-development.html
  41. https://elearningindustry.com/guide-to-vygotskys-zone-of-proximal-development-and-scaffolding
  42. https://www.mdpi.com/2227-7102/15/2/257
  43. https://www.nngroup.com/articles/usability-101-introduction-to-usability/
  44. https://www.w3.org/WAI/fundamentals/accessibility-principles/
  45. https://www.levelaccess.com/blog/usability-vs-accessibility/
  46. https://web.stanford.edu/~efs/calleval.pdf
  47. https://files.eric.ed.gov/fulltext/EJ1113897.pdf
  48. https://www.tesl-ej.org/pdf/ej94/a4.pdf
  49. https://www.sciencedirect.com/science/article/abs/pii/S0023969025000396
  50. https://files.eric.ed.gov/fulltext/EJ1288929.pdf
  51. https://www.researchgate.net/publication/390288924_Understanding_Data_Privacy_and_Ethical_Considerations_in_Learning_Analytics
  52. https://thereader.mitpress.mit.edu/privacy-and-paternalism-the-ethics-of-student-data-collection/
  53. https://studentprivacycompass.org/wp-content/uploads/2021/10/Student-Data-Privacy-Scenarios_Combined.pdf
  54. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2023.1210187/full
  55. https://www.sciencedirect.com/science/article/abs/pii/S0045790622000325
  56. https://pmc.ncbi.nlm.nih.gov/articles/PMC7644889/
  57. https://www.mdpi.com/2227-7102/9/3/210
  58. https://hotpot.uvic.ca/
  59. https://www.researchgate.net/publication/362671097_Web-based_English_Language_Learning_A_Review_from_Web_10_to_Web_30
  60. https://scholarspace.manoa.hawaii.edu/bitstreams/1b0e7f31-7150-401e-9b26-dc6aedbb0096/download
  61. https://digitalcommons.odu.edu/cgi/viewcontent.cgi?article=1016&context=stemps_fac_pubs
  62. https://pmc.ncbi.nlm.nih.gov/articles/PMC10213543/
  63. https://www.sciencedirect.com/science/article/abs/pii/S0360131599000512
  64. https://files.eric.ed.gov/fulltext/EJ1275401.pdf
  65. https://www.tandfonline.com/doi/full/10.1080/09588221.2022.2038632
  66. https://www.irrodl.org/index.php/irrodl/article/view/6294
  67. https://www.sciencedirect.com/science/article/pii/S2451958825000089
  68. https://www.itu.int/itu-d/reports/statistics/2023/10/10/ff23-internet-use/
  69. https://files.eric.ed.gov/fulltext/EJ1463252.pdf
  70. https://www.tandfonline.com/doi/abs/10.1080/09588221.2015.1061019
  71. https://www.researchgate.net/publication/266447292_Using_WebQuests_to_Facilitate_Task-based_English_Reading_Instruction_for_Graduate_Students
  72. https://www.castledown.com/journals/jaltcall/article/view/jaltcall.v7n3.128/616
  73. https://onlinelibrary.wiley.com/doi/10.1111/flan.12464
  74. http://www.natcorp.ox.ac.uk/
  75. https://www.researchgate.net/publication/319774344_Corpora_in_language_teaching_and_learning
  76. https://www.laurenceanthony.net/software/antconc/
  77. https://www.researchgate.net/publication/365936201_Corpora_and_Concordancing_in_Second_Language_Vocabulary_Learning
  78. https://www.sketchengine.eu/wp-content/uploads/2015/04/2015-corpora-submitted.pdf
  79. https://www.tandfonline.com/doi/full/10.1080/09588221.2019.1667832
  80. https://www.tandfonline.com/doi/abs/10.1076/call.16.1.83.15528
  81. https://files.eric.ed.gov/fulltext/EJ1201649.pdf
  82. https://www.researchgate.net/publication/342677560_Adaptive_Forgetting_Curves_for_Spaced_Repetition_Language_Learning
  83. https://old.callej.org/journal/22-1/Waluyo-Bucol2021.pdf
  84. https://files.eric.ed.gov/fulltext/EJ1065005.pdf
  85. https://pmc.ncbi.nlm.nih.gov/articles/PMC9485613/
  86. https://files.eric.ed.gov/fulltext/EJ1098342.pdf
  87. https://www.researchgate.net/publication/316100501_Second_Language_Acquisition_via_Second_Life
  88. https://conference.pixel-online.net/files/ict4ll/ed0012/FP/1232-ICT4325-FP-ICT4LL12.pdf
  89. https://www.cs.cornell.edu/~eland/papers/chi2017_vr.pdf
  90. https://www.researchgate.net/publication/365139006_Second_Life_as_a_Virtual_Playground_for_Language_Education_A_Practical_Guide_for_Teaching_and_Research
  91. https://www.nature.com/articles/s41599-025-05030-4
  92. https://www.tandfonline.com/doi/full/10.1080/17501229.2025.2504706
  93. https://academic.oup.com/eltj/advance-article/doi/10.1093/elt/ccaf025/8186115
  94. https://pmc.ncbi.nlm.nih.gov/articles/PMC9533065/
  95. https://www.mdpi.com/2071-1050/14/6/3147
  96. https://www.researchgate.net/publication/2875422_Arboretum_Using_a_precision_grammar_for_grammar_checking_in_CALL
  97. https://www.isca-archive.org/icall_2004/bender04_icall.html
  98. https://arxiv.org/abs/2505.02032
  99. https://blog.duolingo.com/duolingo-max/
  100. https://duolingo-papers.s3.amazonaws.com/reports/Duolingo_whitepaper_language_video_call_improves_speaking_2025.pdf
  101. https://support.babbel.com/hc/en-us/articles/19176178120722-Offline-mode
  102. https://support.babbel.com/hc/en-us/articles/360037499092-Learning-reminders
  103. https://www.researchgate.net/publication/380861234_Investigating_Mobile-Assisted_Language_Learning_Apps_Babbel_Memrise_and_Duolingo_as_a_Case_Study
  104. https://www.memrise.com/en-us/
  105. https://justlearn.com/blog/5-ai-tools-for-faster-language-learning
  106. https://www.mdpi.com/2673-4591/107/1/7
  107. https://arxiv.org/html/2312.11274v3
  108. https://arxiv.org/html/2505.10643v1
  109. https://www.unesco.org/en/articles/unesco-spotlights-how-digital-learning-can-promote-equity-low-resource-contexts
  110. https://www.cambridge.org/core/journals/recall/article/metaanalysis-of-effectiveness-studies-on-computer-technologysupported-language-learning/4C59D3E86376FE1CC3527CF052884B7C
  111. https://sfleducation.springeropen.com/articles/10.1186/s40862-018-0062-2
  112. https://www.cambridge.org/core/journals/recall/article/metaanalysis-examining-technologyassisted-l2-vocabulary-learning/08A549A6CFD1078406E6A4F8AFE28184
  113. https://www.sciencedirect.com/science/article/abs/pii/S0346251X2400280X
  114. https://www.researchgate.net/publication/338372595_A_Meta-Analysis_of_Selected_Studies_on_the_Effectiveness_of_Gamification_Method_for_Children
  115. https://core.ac.uk/download/pdf/287586011.pdf
  116. https://www.itu.int/en/ITU-D/Statistics/pages/stat/default.aspx
  117. https://files.eric.ed.gov/fulltext/EJ1347616.pdf
  118. https://www.forbes.com/sites/raymondpierce/2024/06/17/only-5-of-students-in-ed-tech-learning-adds-to-the-digital-divide/
  119. https://www.researchgate.net/publication/328497099_The_Human_Intelligence_vs_Artificial_Intelligence_Issues_and_Challenges_in_Computer_Assisted_Language_Learning
  120. https://pmc.ncbi.nlm.nih.gov/articles/PMC9108193/
  121. https://www.tandfonline.com/doi/abs/10.1080/10494820.2021.2021246
  122. https://www.sciencedirect.com/science/article/abs/pii/S1747938X25000703
  123. https://www.researchgate.net/publication/390513930_Ethical_and_Privacy_Considerations_in_AI-Driven_Language_Learning
  124. https://elearningindustry.com/neuro-learning-how-neuroscience-is-shaping-elearning-technologies
  125. https://www.emerald.com/sjls/article/5/1/1/1244010/A-systematic-review-of-current-trends-in
  126. https://files.eric.ed.gov/fulltext/EJ1324755.pdf
  127. https://eurocall2025.com/call-for-papers/
  128. https://calico.org/home/about-calico/
  129. https://www.facebook.com/groups/255577856335/posts/10163699421871336/
  130. https://calico.org/home/about-calico/member-benefits/
  131. https://eurocall-languages.org/join-us/
  132. https://register-of-charities.charitycommission.gov.uk/charity-search/-/charity-details/1041889/full-print
  133. https://www.sciencedirect.com/science/article/abs/pii/S0160791X25000260
  134. https://think.taylorandfrancis.com/special_issues/beyond-automation-ai-driven-language-assessment-in-call-through-pedagogical-ethical-and-ai-literacy-lenses/
  135. https://gisconf.com/index.php/GTERD/article/view/2175
  136. https://www.researchgate.net/publication/386202132_Mobile-Assisted_Language_Learning_MALL_Revolutionizing_Language_Education
  137. https://pmc.ncbi.nlm.nih.gov/articles/PMC9169034/
  138. https://www.researchgate.net/publication/394589957_Twenty-five_years_of_computer-assisted_language_learning_A_topic_modeling_analysis
  139. https://www.tandfonline.com/journals/ncal20
  140. https://eric.ed.gov/
  141. https://www.nsf.gov/focus-areas/ai
  142. https://digital-strategy.ec.europa.eu/en/policies/genai4eu
  143. https://eufundingportal.eu/ai-technology/
  144. https://callej.org/
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