(Continuation of "Computers & Language Learning: An Overview")

Intelligent CALL

Those who hope for "Intelligent CALL," by and large, do not feel that a piece of software can be intelligent in the same way as a human being. Rather, the idea is to have software that uses the power of the computer to offer easy interaction with the material to be learned, including meaningful feedback and guidance; comprehensible information in multiple media designed to fit the learning style of individual students; and ways for students to carry communication beyond an individual computer screen.

In our information-rich time, having a way to navigate and manage all of the facts at our fingertips is essential. In the computer arena, "user interface"--the way software is written so that people will understand what to do with it--is a perennial concern. While much has been made of the graphical user interface of the Macintosh and Windows, replacing text with graphical objects (icons) is not enough to make software intelligible, much less intelligent. The best new business software offers users help at every step; good CALL software should do no less.

Learners need help with more than the mechanics of operating a software program. They also need to know how to make the best use of it for their own purposes. Research has indicated that learners don't always know how to fit new information into an appropriate framework (Dole, et al., 1991; Gay, 1987; Van Dijk & Kintsch, 1983), so intelligent software has to give them context as well as data. CALL programs that respond to user input with nothing more than "Right" and "Wrong, try again" are clearly less helpful, thus less "intelligent" in these terms than they should be. Far better is software that tracks learner answers and looks for patterns, responding not only with whether the answer was correct but also why it was right or wrong and offering suggestions for further study--going on to a more advanced level or doing some extra work at the current or a previous level.

Learners also often fall short in their ability to apply appropriate learning strategies to material (Oxford, 1990; Healey, 1993; Rubin, 1987), so intelligent software should be able to take naive learners in hand and help them along in figuring out how to use the software effectively, as a teacher would. This does not mean that learners give up all control over their path and rate through material. Quite a bit of research has explored the idea of learner vs. computer control in computer-assisted instruction (Gay, 1987; Robinson, 1989; Johanesen, K.J. & Tennyson, R.D., 1983; Schaeffer, R.H., 1981; inter alia). The general view is that learners, especially adults, feel better when they have a sense of control over the program. The most dictatorial programs are those that do not let the learner quit when he or she is ready to do so. The most flexible give learners the choice of what to do, when to do it, and for how long. Guided freedom would be a feature of intelligent CALL, where the program would make suggestions, but the learner would make the choices.

Another direction in current software is the integration of media. As computer storage and memory prices have dropped, software developers have been able to add in graphics, sound, animation, and video clips. Foreign language teachers are particularly helped by access to a variety of media to help make the language come alive to students for whom it is largely a distant abstraction. This trend can only accelerate, with faster and more powerful computers making longer video and sound clips practical. Intelligent CALL will fit the medium to the learner, ensuring that the media work in concert to enhance understanding. Developers need to restrain the urge to add anything and everything just to make a fancy-looking product, and instead focus in on selecting media to fit pedagogy, not vice-versa (Kozma, 1991).

The expansion of digital media has meant more data for learners to create their own language hypotheses with. A vast amount of information is available electronically in text form via the Web (see http://www.sil.org/linguistics/etext.html for a place to start in electronic collections of texts), and more high quality collections of text for linguistic analysis than ever are available for sale from Cobuild, Oxford, Longman, and other vendors. While current concordancers work only on text, intelligent CALL will be able to use video concordancing to help clarify vocabulary and grammar usage. Teachers or learners can query the video concordancer with a word or phrase, and it will cue up video clips containing that word or phrase for playback. Prototype video concordancing exists now, fed by products that capture the output from closed caption decoders (Price & Imbier, 1993; Spanos & Smith, 1990).

Many current businesses, especially high-tech companies, use a team approach that encourages collaboration on projects. It could be said that business is just catching up to language teaching in this regard. The benefit to language teachers is that software developed to make business collaboration easier, such as Lotus Notes for Workgroups, can also be used to help students within a class share their ideas and writing more easily. With networked computers increasingly common, software publishers are encouraged to market products that take advantage of the interaction a network offers. For example, there is no reason software on CD-ROM or on disk should not also link to the Web in order to add current content to a writing or grammar lesson, or to let students e-mail their writing for others outside the class to read. Intelligent CALL will not be limited to its self-contained information or interaction with a single user, but will take advantage of local network and online possibilities for information retrieval.

John Underwood's "On the edge: Intelligent CALL in the 1990s" (1989) hoped for advances in artificial intelligence, hypermedia, and simulations to create new ways of approaching language teaching with computers. Natural language processing, or the computer's ability to extract something approaching meaning from text or speech, is one of the elements Underwood feels is essential to the intelligent tutoring systems, the hypermedia rich in helpful navigational aids, and the realistic interactive microworlds he describes. A computer able to parse a typed or spoken sentence and respond to it can allow learners much more flexibility in the types of activities they can do and get help with. In fact, a student using an intelligent tutoring system should be able to ask the computer for help in something approaching natural language rather than by choosing elements from a programmer-created menu. A system that could evaluate an answer and respond with guidance would be more more teacher-like than anything currently available.

The key to CALL software that responds intelligently to what the learner speaks or types is high-quality natural language processing. This has been one of the key areas within artificial intelligence research for years, and much progress has been made. However, substantial challenges must be overcome before the computer can make sense of learner input. Sells et al. (1991) point out that four related areas of study are involved in natural language processing:

(1) investigating the psychological processes involved in human language understanding;

(2) building computational systems for analyzing natural language input (and/or producing natural language output);

(3) developing theories of natural language structure; and

(4) determining the mathematical properties of grammar formalisms. (p. 1)

Bates, et al. (1993) are optimistic that natural language processing, at least where the topics are somewhat constrained and users are careful about how they interact with the computer, will be advanced enough within the next ten years to make a "revolutionary" impact on society. They feel that software systems will improve in three key areas: (1) "Knowledge acquisition from natural language (NL) texts of various kinds, from interaction with human beings, and from other sources" to create vary larger knowledge bases than those that currently exist (p. 1, italics in original); (2) "Interaction with multiple underlying systems" to make NL systems more flexible and user-friendly (p. 1; italics in original); and (3) Partial understanding gleaned from multi-sentence language, or from fragments of language" because human input is typically imperfect (p. 2; italics in original).

Microsoft has been working on a system called MindNet that they hope to ship in the next couple of years. This system would work with Microsoft Office and allow users to translate documents into different languages, create executive summaries of documents, and do more efficient searches for information in stored documents. Whether it will do what it claims or not, Microsoft's interest indicates that businesses see the goal of natural language processing as a realistic one.

Speech recognition has also made great advances in the last few years. Dragon System's Naturally Speaking and IBM's ViaVoice convert clear but continuous speech to text with an 80-90% accuracy rate. The higher accuracy rate comes after the programs have been "trained" by listening to the user speak about 250 key words. Previous programs required users to pause after each word, making the speech highly unnatural. Girard and Dillon (1997) estimate that 30% of business users will be taking advantage of text to speech products by the year 2001 as a result of current developments in the technology. Several programs for language teaching now incorporate speech recognition, including The Learning Company's Learn to Speak series; Triple Play Plus from Syracuse Language Systems; Courseware Publishing International's See It, Hear It, Say It; English Vocabulary; and Traci Talk; and Dynamic English from DynEd. These programs are not capable of dealing with freely generated speech, but rather recognize a correct multiple choice answer. By limiting the domain, the speech recognition program can work with a relatively broad range of accent and speech styles. By all indications, the use of speech recognition technology will improve and increase as time goes on and computers become faster and more able to do the complex calculations required of natural language processing.

Electronic conversations with the computer, popularized by Joseph Weizenbaum's Eliza program, will become more sophisticated with spoken as well as typed input. Current adventure games have made great strides in their ability to process natural language in typed form, with games like Myst leading the way in setting up more natural interactions between the user and the computer. To be more useful in a language teaching context, an intelligent CALL form of these games could let the learner choose to have the program evaluate and comment on his or her grammar and vocabulary, as well as respond to the content of what was said or typed.

While we're coming closer, advancing in all the areas mentioned above--user interface, learner feedback, integrated media, communication within and outside the classroom, and natural language processing--we've still got a very long way to go before CALL can be accurately called "intelligent." What teachers can do now, however, is to work with each other to improve teaching with technology. Linking via the Web, workshops, or conferences, teachers can encourage the intelligent use of CALL. That will be a very large step forward.


The role of computers in language teaching has changed significantly in the last 30 years. Previously, computers were used principally for drills and exercises. Technological and pedagogical developments now allow us to better integrate computer technology into the language learning process. Multimedia programs incorporating speech-recognition software can immerse students into rich environments for language practice. Concordancing software and large language corpora provide students' the means to investigate language use in authentic context. And the Internet allows for a myriad of opportunities to communicate in the target language, access textual and multimedia information, and publish for a global audience.

Future developments in networked communication, multimedia, and artificial intelligence will likely converge, creating a potentially more central role for the computer as a tool for authentic language exploration and use in the second language classroom. As our focus of attention gradually shifts from the computer itself to the natural integration of computers into the language learning process, we will know that computer technology has taken its rightful place as a important element of language learning and teaching.

Selected CALL Resources

Recent Books

Athelstan. (1997). Technology and language learning yearbook, vol. 8. Houston, TX: Athelstan.

Boswood, T. (1997). New ways of using computers in language teaching. Alexandria, VA: TESOL Publications.

Bush, M. (1996). Technology enhanced language learning. Lincolnwood, IL: National Textbook Company.

Debski, R., Gassin, J., & Smith, M. (Eds.). (1997). Language learning through social computing. Melbourne: Applied Linguistics Association of Australia.

Healey, D. (1995). Something to do on Tuesday. Houston: Athelstan.

Healey, D., & Johnson, N. (Eds.). (1997). 1997 TESOL CALL Interest Section software list. Alexandria, VA: TESOL Publications.

Levy, M. (1997). Computer-assisted language learning: context and conceptualization. Oxford: Oxford University Press.

Pennington, M. (Ed.) (1996). The power of CALL. Houston: Athelstan.

Sperling, D. (1997). The Internet guide for English language teachers. Upper Saddle River, NJ: Prentice Hall Regents.

Thompson, J, & Parsons, J. (1995). ReCALL software guide #4, 1995. Hull, UK: CIT Centre for Modern Languages, University of Hull.

Warschauer, M. (1995). E-Mail for English teaching. Alexandria, VA: TESOL Publications.

Warschauer, M. (Ed.) (1995). Telecollaboration in Foreign Language Learning. Honolulu, HI: University of Hawaii Second Language Teaching and Curriculum Center (University of Hawaii Press).

Warschauer, M. (Ed.) (1996). Virtual Connections: Online Activities and Projects for Networking Language Learners. Honolulu, HI: University of Hawaii Second Language Teaching and Curriculum Center (University of Hawaii Press).


CALICO Journal
The Computer Assisted Language Instruction Consortium
Southwest Texas State University
317 Liberal Arts
San Marcos, TX 78666

CÆLL (Computer-Assisted English Language Learning) Journal
1787 Agate St.,
Eugene OR 97403 U.S.A.


Computer Assisted Language Learning
P.O. Box 825
2160 SZ Lisse
The Netherlands

Internet TESL Journal

Language Learning & Technology

Language Centre
Bond University
Gold Coast
Queensland 4229

ReCALL Newsletter
http://www.cti.hull.ac.uk/pubs.htm#ReCALL Newsletter

Elsevier Science Ltd, The Boulevard
Langford Lane
Kidlington, Oxford OX5 1 GB, UK




ATELL (Australian Technology Enhanced Language Learning Consortium)

CALICO (Computer Assisted Language Instruction Consortium)
Southwest Texas State University
317 Liberal Arts
San Marcos, TX 78666

CTI Centre for Modern Languages
University of Hull

JALT CALL N-SIG (Japan Association for Language Teaching CALL National Special Interest Group)

JALT Central Office

Urban Edge Building 5th Floor

1-37-9 Taito

Taito-ku, Tokyo 110 Japan

MUESLI (Micro Users in ESL Institutions)
3 Kingsdown Park
Whitstable, Kent
England CT5 2DJ

TESOL CALL Interest Section
1600 Cameron St., Suite 300
Alexandria VA 22314 U.S.A.


Internet Resources


Dave's ESL Cafe on the Web


LLTI (Language Learning and Technology International)

NETEACH-L (Using the Internet for teaching ESL)

OPPortunities in English

TESL-L (Teachers of English as a Second Language)
TESLCA-L (Computer-Assisted sub-branch of TESL-L)
(send message subscribe tesl-l yourfirstname yourlastname)

International Student E-Mail Discussion Lists
Nine lists for ESL/EFL college and university students

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