Using intelligent computer-assisted language learning

Computers and the Humanities 23 (1989) 59--70. © 1989 by KluwerAcademic Publishers.

Using Intelligent Computer-Assisted Language Learning

Carol Chapelle Dept. of English, Iowa State University, Ames, IA 50011, U.S.A.

Abstract: This paper explores uses for intelligent courseware in language classrooms. Definitions are given for three types of intelligent systems: microworlds, intelligent grammar checkers, and intelligent tutoring systems. Uses of these systems are explained for the prewriting, drafting, and revising phases of writing instruction and for second language instruction focusing on formal and functional aspects of the target language. Questions for future research on intelligent language courseware are raised.

Key Words: artificial intelligence, language teaching, language instruction, computer-assisted language learning, intelligent courseware, microworlds, intelligent grammar checkers, intelligent tutoring systems, writing, second language learning

Introduction Current work in Artificial Intelligence (AI) makes appealing promises to educators who wish to transform pervasive computer technology into effective learning tools. Experimental systems using AI techniques have been developed for a number of subjects. Consequently, it is time to consider how they and their descendants will be used in writing and second language programs 1 so that pedagogical intentions and hypotheses can guide research and development. To that end, this paper defines intelligent courseware, describes three types of AI-based systems -- microworlds, intelligent grammar checkers, and intelligent tutoring systems 2 _ and envisions how these systems might improve language instruction. Uses are described for intelligent systems in the three major

Carol Chapelle, ass&tant professor of English at Iowa State University, teaches courses in ESL and Applied Linguistics, develops ESL courseware, and conducts research on students' use of computer-assisted language learning materials.

phases of first language writing -- prewriting, drafting and revising -- and in the two primary foci of second language development - - formal linguistic accuracy and functional language use. Table 1 overviews which of the three systems is discussed for each aspect of language instruction.

Language Instruction

Types of Intelligent CALL

Microworlds

Intelligent Intelligent Grammar Tutoring Checkers Systems

First Language Prewriting X Drafting X Revising

Second Language Functional X Formal

X

X X X

X X X

Defining Intelligence in Courseware A precise definition of "intelligent courseware" has not yet emerged, but it can be considered software which uses encoded information to create a learning environment and to respond to students in a way that appears to be similar to what a person would do. Of course, the degree of sophistication a program needs in order to make such responses will depend on the complexity of the task at hand. For example, a program that, when asked, can tell the correct time, appears to be intelligent within that domain; it responds like a human. However, as the complexity of the program's task increases, so does the variance in

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the degree of intelligence that the program can display (Winograd and Flores, 1986). A program's intelligence must then be viewed in light of the complexity of its objectives.

Three Types of Intelligent CALL for Language Instruction To date CALL programs have made only limited use of AI techniques, and for relatively simple language tasks. In the future, however, AI techniques may enable programs to tackle significant language instruction tasks such as communicating with a student about a meaningful topic, recogniz- ing students' grammatical errors in writing, and providing students with appropriate, individualized instruction based on past performance. The resulting intelligent language courseware can be divided into three kinds of systems: microworlds, intelligent grammar checkers, and intelligent tutoring systems.

Intelligent microworlds provide a partner to converse with students about a given topic. These programs use knowledge representation techniques and natural language processing to transform computers so that communicating with them "can be a natural process . . . like learning French by living in France" (Papert, 1980, p. 6). Papert originally suggested microworlds as environments in which children could work creatively, acquiring concepts of geometry and math. By supplying "simple, concrete models of important things, ideas, and their relationships," these systems are designed for students to gain "powerful authentic knowledge" (Lawler and Yazdani, 1987, p. x). In microworlds for language instruction, the "important things, ideas and their relationships" refer to meaning (semantics/pragmatics) and grammar (syntax/morphology).

Focusing on the syntax of language, intelligent grammar checkers (IGCs) perform an analysis of students' written work to point out errors. These systems use natural language processing techniques to encode a partial grammar of a language and the kinds of errors that students typically make. The program then is able to detect grammatical errors such as faulty subject-verb agreement in students' language. Although, as a genre, intelligent grammar checkers tackle significant language analysis problems, individual systems

have, in fact, varying levels of sophistication in terms of the number and type of errors they find, as well as in their ability to find those errors.

Intelligent tutoring systems (ITSs) attempt to "combine the problem-solving experience and motivation of 'discovery' learning [typified by microworlds and intelligent grammar checkers] with the effective guidance of tutorial interactions" (Sleeman and Brown, 1982, p. 1). An ideal ITS for language instruction would be composed of a microworld or grammar checker as well as an expert system encoding decision-making procedures and instructional strategies of an experienced language teacher (see Neuwirth, this volume).

Each of the above three systems uses AI techniques for language instruction; however, use of such techniques alone does not guarantee that programs will be beneficial for teaching. It is necessary to pinpoint areas in which AI techniques can tackle more important aspects of language teaching with greater success than what can be attempted with simple, unintelligent programs, or classroom instruction. Some educators have begun to envision the capabilities of these techniques, and others have begun putting them to work in software for first and second language instruction.

First Language Writing Skills Computer programs have been used for years to provide out-of-class assistance with all aspects of the writing process. However, due to their inadequate language handling capabilities, they can assist in only limited learning tasks. Consequently, an instructor will often find them an unreliable method of providing individualized instruction. Despite the limitations of these programs, the pioneers who developed them laid the ground- work for more complex, intelligent systems at each stage of the writing process.

Prewriting At the prewriting stage, instructors use classroom discussions and readings to help students develop and detail their thoughts. However, ideas derived from these activities are sometimes forgotten when students actually have to write, and it has been suggested that computer-student conversations used as prewriting tools offer a solution (Burns and Culp, 1980). Such exercises allow students to

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explore a writing topic by having the computer ask questions to help generate ideas, focus on a topic, and stimulate detailed thinking. For example, the following student-computer dialog (from Connor and Cerniglia, 1988) is a prewriting exercise to prepare the student for writing a paper. The italicized portions are what is unique for each student. (Computer output is in boldface.)

During this exploration process, you will be asked to clarify the purpose of your paper on international awareness. So now would you briefly describe what the purpose of your paper is by completing this statement: The purpose of this paper is t o . . . (limit one line) The university should add more courses with international content and a bigger variety of foreign languages should be offered. Relax now, Chris, and enjoy this brainstorming session. What objects do you associate with international awareness? How might they be included in your theme? Crosscultural awareness, because you need this awareness to establish international trade relations with other countries. Knowing a foreign language so that it will be possible communicate on the international market.

At this point the computer goes into a loop so that anything Chris types will receive one of the following messages, sometimes with an additional general tip:

Good, Chris, I like your reasoning. Add to your response now. Great, Chris. Anything else? (You can add more information, ask a question, or give a command -- whatever you wish.)

This is one example of a number of very clever programs which use theoretical principles of writing heuristics to elicit language from students -- language that will help them to begin their writing (for more examples see Rodriguez and Rodriguez, 1984). In the example, all the responses and questions produced by the computer follow a rigid script (beginning with "During this exploration process.. ."). Typically, such systems operate

with primitive language processing capabilities and without any knowledge of the topic of the student's essay. Because the systems lack intelligence, their usefulness is necessarily limited: they simply prompt the student rather than converse about a topic.

Prewriting interaction focusing on a particular topic could be achieved using knowledge representation techniques (e.g., Chamiak and McDermott, 1985) to encode information about a writing topic in order to create a microworld program. A number of examples of such microworlds are found in intelligent courseware for other subjects. One contains information about the forces affecting weather patterns such as the relationships between the wind, oceans, mountains, etc. (Stevens, Collins, and Goldin, 1982); another maintains a model of electrical circuits and their faults (Brown, Burton, and de Kleer, 1982); another contains geographic and demo- graphic information about countries in South America (Carbonell and Collins, 1973). These intelligent programs are intended to provide instruction through computer-student dialogs on weather, circuits, and geography, respectively. For example, a student working with "WHY," the weather program, might have a dialog such as the following:

Do you think the Amazon jungle has heavy rainfall or light rainfall? Heavy rainfall Why does the Amazon jungle have heavy rainfall? Because it is near the Atlantic Yes, the Amazon jungle is near the equatorial Atlantic. How does that affect rainfall in the Amazon jungle? The water in the current is warm

This student-computer dialog (Barr and Feigenbaum, 1982, pp. 243--44) about weather patterns is made possible by the knowledge representation that the program contains and the natural language processing capabilities it has for parsing the student's input in order to find meaning. Used as a prewriting activity, programs of this type could allow a student to explore a specific topic before writing a paper.

An ITS for a prewriting exercise could exploit the microworld's knowledge of the topic and guide

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the student-computer dialog in a restricted subject domain. Just as an instructor might introduce variety and depth into a classroom discussion, an expert instructor strategy would intervene to guide the dialog rather than simply follow the student's train of thought. Intervention would be motivated by the system's qualitative analysis of the student's input to determine where the material generated was inadequate for a well-developed paper. The weather program illustrates how an expert system evaluates and directs students' reasoning during the course of the conversation. When the student responds to the computer's general question with "I don't know," the computer rephrases the same idea using a specific case. When the student answers with a simple "yes," the computer probes that topic for more development.

What happens after the moisture-filled air reaches the land? I don't know Do you think the Andes mountain range has any effect on the rainfall in the Amazon jungle? Yes How? The prevailing wind hits the Andes and the air cools How does the fact that the prevailing wind hits the Andes lead to air cooling?

Then, when the student displays a general misunderstanding, the program asks about a specific case designed to spotlight the misconception for the student.

Do you think that any place with mountains has heavy rainfall? Yes. Southern California has mountains. Why doesn't Southern California have heavy rainfall?

This guidance, based on a content analysis of students' input, would be beneficial to some writers who were not knowledgeable about subject matter and/or the strategies for developing effective support. Thus, use of expert system technology would help a student to explore specific content before writing.

Drafting As students draft a piece of writing, it is impossible -- and probably undesirable -- to have the instruc-

tor stand over them to provide information and prompt them; instead, instructors suggest ways to make students' writing more fluent and effective. Some of these suggestions have been incorporated into instructional word processing software. For example, it is possible in the WANDAH system to inhibit the display of text on the screen so that students will not be tempted to stop writing to make corrections, and hence they will presumably write with greater fluency (Blum and Cohen, 1984). Other systems include tools such as spellers and thesauri that the student can access.

Useful as these general writing tools might be, they cannot provide specific assistance with particular assignments since they lack "knowledge" of the writer's topic and have primitive natural language processing capabilities. On the other hand, an intelligent microworld containing information about a writing topic would allow students to ask for the specific information they require. The system would exploit knowledge representation techniques to encode the database and include a natural language front-end for accessing information. AI researchers devise efficient methods for coding and accessing databases using English questions and commands. One system, for example, allows the user to direct questions to a database containing information about American naval ships (Hendrix, Sacerdoti, Sagalowicz, and Slocum, 1978). The program understands the following kinds of questions:

What kind of information do you know about? Is there a doctor on the Biddle? Display all the American cruisers in the North Atlantic. What is the name and location of the carrier nearest to New York? What is the commanding officer's name? Who commands the Kennedy?

The system parses the user's input to form a semantic representation, and then searches the database for the answer, which it then returns to the user.

Such a system would be of benefit to those students who attempt to develop their writing without the necessary specific information, as well as to those who recognize the importance of development with detail but simply do not know the facts they wish to include. Other students,

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however, may be unaware of the need to use detail in their writing. Since students at this stage have little or no writing skills it may be more effective to offer them information rather than wait for them to ask.

An ITS would add to the microworld's database an expert strategy to assess students' work in progress and provide suggestions for revisions. In a manner similar to the example from the "WHY" program, an ITS would be able to assess the adequacy of a paper's content. The student's strategies might also be assessed and advised using procedures like those in an AI teaching experi- ment called WEST (Burton and Brown, 1982). However, ITSs of this sort would require an extremely sophisticated semantic component.

Revising, Editing, and Remediation After writing a preliminary draft, students are guided by teachers and classmates as they revise its substantive organization and development, edit the surface features of its language, and seek remedial help in areas of weakness. Attempts have been made to write programs that analyze linguistic surface features and offer remedial practice. Grammatical analysis of students' writing is an inherently ambitious undertaking. Consequently, the degree of intelligence that grammar checkers display varies depending on the techniques used for language analysis.

Programs with relatively simple analysis techniques yield quantitative information about writing that may help students make revisions. For example, WANDAH analyzes "average number of words per sentence and per paragraph and the average number of prepositions per sentence" and some features of style such as abstract words, prepositional phrases, selected gender-specific (and possibly sexist) nouns, "be" verbs and possible nominalizations (Blum and Cohen, 1984, pp. 167--68). Many remediation programs that focus on troublesome features of sentence level grammar use simple error anticipation techniques for judging students' attempts to identify parts of speech, verb forms, or formation of subordinate clauses. These programs have been used for years to provide valuable instruction; however, the sophistication of their analysis has necessarily been limited by the techniques used to perform linguistic analysis.

More sophisticated systems attempt to identify many significant grammatical errors. This require- ment exacerbates the language-recognition problem; nevertheless, using natural language processing techniques, style checking programs have been developed to provide writers with information about syntactic correctness (Heidorn, Jensen, Miller, Byrd, and Chodorow, 1982; Hull, Ball, Fox, Levin, and McCutchen, 1987). These systems analyze faulty subject-verb agreement, comma splices, or sentence fragments, for example. In the following paragraph, the system developed by Hull, et al. (1987) would recognize the sentence fragment and possessive error:

Mutual funds, bonds, and certificates of deposit are all instruments of relative or assured safety for investors after the stock market has proven vicarious. Remaining in the stock market in the current economic situation. The investors opportunity of breaking even is much greater now than it was in 1929.

The authors report that their grammar checker would identify sentences such as the one beginning "Remaining.. ." as a "fragment error," although it would also incorrectly flag some valid sentences which begin with gerund subjects. In addition, the checker would identify the possessive error "investors" by means of a procedure which searches for the "pattern 'plural noun' plus 'singular or plural noun' or 'plural noun' plus 'adjective' plus 'singular or plural noun'" (p. 113). Once again, this procedure would identify some correct constructions as possible possessive errors, particularly 'plural noun' plus 'verb' constructions when the verb can also be used as a noun.

These IGCs offer a sophisticated analysis of students' errors before they turn in papers. Some students undoubtedly sharpen their awareness of grammatical problems by working with these programs on their own; other students continue to have the same problems. For the latter, remedial practice with troublesome grammar points is needed -- practice which is efficiently generated using grammar checkers focusing on a given problem element of syntax. For example, a persis- tent writing problem for speakers of non-standard dialects of English is their incorrect use of verb forms. Consequently, a program called VERBCON was developed to give students practice using correct verb forms in context (Bailin and Thomson, 1988). VERBCON's grammar rules allow for a

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precise analysis of students' verb forms alone. Another problem of some remedial writers is their inability to use the metalanguage of linguistics to discuss their writing problems. Giving practice with grammatical terminology, a program called PARSER generates well-formed English sentences from which students are asked to find examples of particular constituents (Bailin and Thomson, 1988).

Students' use of these remedial programs would be directed by an instructor. However, if an IGC were accompanied by a student model and a teaching or coaching strategy, the program could assess students' needs and make recommendations for additional work. The student model would be a representation of students' grammatical errors; the teaching strategy would use this information about the student to produce necessary advice and remediation. Accordingly, an ITS for grammar instruction might infer (from performance) students' misconceptions in use of the grammatical conventions of written English -- conventions such as spelling, using apostrophes, forming complete sentences, or adhering to verb-form rules.

For example, in the plans for an ITS for writing effective sentences, the program will construct a model of the students' knowledge of the principles of sentence combining on the basis of their performance on sentence combining tasks offered by the program. Neuwirth (1986) suggests that the tutorial strategy might then address the student's misconception in several different ways: generate tasks that would allow the student to discover the principle, tell the student directly what principle was violated, or simply indicate to the student that s/he could improve the sentence without indicat- ing specifically how such an improvement might be accomplished (Neuwirth, 1986, p. 12). As Neuwirth points out, the superiority of one or another tutorial strategy has not been established.

Similarly, if syntactic errors had been found in a student's paper, those errors would be coded in a student model on which a tutorial strategy would then draw to choose further instruction. The student's problems may be that he or she uses participial phrases as sentences, and forms posses- sives incorrectly. For a given student, these two errors may be random occurrences, perhaps due

to inadequate proofreading. For others, these same errors may be found consistently throughout papers, regardless of the fact that the IGC flags every occurrence. If an ITS formed a student model over time on the basis of errors found in each assignment, it could use its expert system to decide when the student might benefit from explicit instruction on consistent problems. Such systems would be valuable in writing classes, where the typical situation dictates that all students receive the same instruction, regardless of their individual problems.

Summary The AI techniques just discussed hold out the promise of programs that address complex tasks in writing instruction and accomplish those tasks with a greater degree of success than was hereto- fore possible. Consequently, intelligent programs offer new options for instruction in prewriting, drafting, and revising and remediation.

Intelligent prewriting programs can be used to prepare students for class discussion on a writing topic by giving them access to factual information. An intelligent system would provide students with a large amount of seemingly unstructured materials to explore, whereas a reading passage would have a more rigid structure of both form and content. Alternatively, as a follow-up to a class discussion introducing a writing topic, a program could be used to generate more detailed thinking and to help students transfer some ideas to paper - - or to disk.

Intelligent drafting programs, unlike teachers, could provide assistance for a writer during the drafting process. The instructor's drafting help is necessarily limited to general guidelines and suggestions rather than the on-the-spot specific information and assistance that an intelligent program would provide. This kind of assistance with drafting would be particularly useful for students who, for example, have difficulty getting started, or do not use supporting details.

Intelligent programs for editing, revising and remediation provide checkers for students to correct surface language features, thereby reliev- ing the instructor of the task. Remedial programs provide controlled practice with reliable linguistic analysis of students' input. An ITS for editing and


revising would obviate the need for the instructor to spend significant amounts of time discussing and illustrating fundamental grammar problems. Because of the idiosyncrasy of language errors and the importance of addressing only those that create difficulties for the student, this capability to individualize instruction is extremely valuable.

Acquiring Communicative Competence in a Second Language As in writing instruction, the objective of second language instruction is to enable students to use language to communicate effectively; however, different factors come into play because learners begin at a much lower level of ability. In the initial stages, they work to encode simple ideas into the target language. Subsequently, they strive to attain communicative competence. The competencies and skills included in communicative competence have been detailed somewhat differently by a number of second language researchers (Canale and Swain, 1980; Savignon, 1983; Bachman, 1988). Nevertheless, it is generally agreed that the primary components of the construct are, amongst others, formal linguistic competence -- knowledge of morphology, syntax, etc. -- and functional competence -- the ability to use the language to express meaning.

For at least the past twenty years, developers of second-language courseware have sought techniques for evaluating constrained and free student input to the computer. Evaluation of a given syntactic form has been accomplished reasonably well by programs that define a limited context; however, recognition of free responses has just recently been given serious attention because of the development of AI techniques. Like developers of writing software, pioneers of second language software have attempted to tackle complex aspects of second language learning and teaching. The result has been some prototype programs as well as an expanded vision of the use of the computer for developing both functional and formal second language competence.

Functional Language Competence Numerous techniques have been used to help second language students gain fluency to express and respond to a message, regardless of its gram-

matical accuracy (Savignon and Berns, 1983). Because these activities are time-consuming and class time is limited, it may be useful to have students practice the target language by convers- ing with a computer program outside of class. Higgins and Johns (1983) suggested this approach using an English as a second language (ESL) program which, in principle, acts like a microworld, encompassing the semantic domain of a "room" containing two "people" and the syntactic domain of simple sentences, commands, and questions. The student-computer dialog might go something like this:

Is Mary here? No Where is Mary? In the kitchen. Bring Mary in. OK. Send John out. OK (Higgins and Johns, 1983, pp. 76--77)

Each time the computer is given an appropriate command, it changes the drawing on the screen, so the students' language has immediately observable effects on the conversation. This program allows students to practice functional language use. How- ever, the complexity of the task mad the amount of intelligence it displays are limited by the techniques used to represent semantics/pragmatics and syntax. As a consequence, it may be unable to interpret and respond to some ungrammatical and illogical forms that second language students are likely to type.

• In order to facilitate this kind of computer- student interaction, intelligent microworlds would use natural language processing and knowledge representation techniques for interpreting and generating natural language. As students worked with such programs, they would make errors to which an intelligent language processor could respond naturally so as not to interrupt the interaction (Underwood, 1984). Researchers in AI work toward systems that participate in purposeful conversations. The following illustrates a conversation between a person (the client) and a program (GUS) which accesses a database con- taming information about airline schedules:

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Hello. My name is GUS. I can help you plan a simple trip by air. Where do you want to go? I want to go to San Diego on May 28. What time do you want to leave? I must be in San Diego before 10 am. Would you like P.S.A. flight 102 that arrives at 9:15 am? What is the next flight? Air California flight 310 that leaves at 8:30 am and arrives at 10:00 am. I'll take the first one. I have confirmed the following f l ight . . . (Bobrow, Kaplan, Kay, Norman, Thompson, and Winograd, 1977, p. 597).

A program such as this would provide a language student with functional language practice in a limited real-world domain.

These AI approaches have, in fact, been used for functional language programs. Most notable are second language adventure games which place students in a problem situation from which they have to try to escape. Such an adventure game places students in a mystery house which requires use of the target language (French or Latin) to instruct the computer what actions to take (e.g., "Entrez dans le chfiteau") and ask questions about the situation (e.g., "Demandez au berger s'il sait off est le duc"). The program is able to recognize and respond appropriately to students' linguistic input (whether or not it is perfectly formed) because it has both encoded knowledge of the world of the adventure game and a partial set of rules of the target language (Culley, Mulford and Milbury- Steen, 1986). This type of adventure-game may interest students who are bored by typical language learning activities and who wish to play. However, the game format is not the only one which can be developed with microworld technology.

Other students may like to work with the computer in the target language to retrieve information that they can use for some other purpose. As described above, natural language interfaces for databases allow a user to retrieve information by asking a program questions. For example, with such a database containing information on all graduate programs in engineering in the US, an undergraduate ESL engineering student may wish

to practice asking for information using that program.

These microworlds offer students an environment in which they can practice the target language by using it for a specific purpose, such as reaching a goal in a game, or retrieving some information. Some students are able to take advantage of these kinds of environments; however, as research in second language acquisition has shown (e.g. Brown, 1987), individuals vary in the degree to which they can take the initiative to create useful learning activities for themselves. Students who are not good at self-direction may need coaching from an ITS while they work.

An ITS for functional language practice could add to the computer-student dialog a coaching strategy that would help students to initiate and maintain purposeful interaction. While students worked, the system would create a model of their strategies. The expert system would use that model for making suggestions concerning directions for the dialog. Just as the arithmetic game, WEST, advises children as they play (Burton and Brown, 1982), a functional language coach could guide second language learners who are working toward a goal in an adventure game or who are retrieving information.

In an adventure game, some students may continually use strategies that take them into dead-ends, never progressing through the game, or they may fail to make moves that are linguistically and culturally appropriate. In retrieving information, the undergraduate engineering student may collect some information about schools, never inquiring about entrance requirements, assistance- ships, and application deadlines. In each of these cases, the student's strategic performance could be improved to better accomplish the objectives. In addition, the ITS could handle syntactic errors. For example, if the microworld contained an IGC component, it could note whether the student were consistently using the wrong preposition or verb conjugation for a particular type of verb. The system could then provide coaching on that point. An ITS that noted these shortcomings in students' language use could provide suggestions for improving communicative competence in these situations.

Through natural language processing and

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knowledge representation, these systems attempt the complex task of providing practice in achiev- ing functional goals with a second language. As students work toward that goal their focus is on the communicative functions of language rather than its linguistic forms. While control of these functions is an essential element of communicative competence, students, particularly those in an academic setting, also need to use correct grammatical forms.

Formal Language Competence Formal second language instruction typically includes teaching the syntactic rules of the target language. However, the varied abilities of second language students often result in frustration for the instructor who "teaches" a point, such as the use of the present perfect aspect of the verb, only to find that some students are able to use it correctly most of the time; some others use it correctly only in exercises and in careful writing; still others appear to have no control of the point at all. 3 Should the present perfect be taught again?

Much existing language software addresses this need for individualized grammar instruction in a second language. However, these programs limit each exercise to one or a few grammar points, such as formation of the present perfect and use of adverbs. Because these programs limit the possible student input, they can use relatively simple answer judging procedures, such as listing anticipated wrong answers. For example, a lesson on the present perfect might require the student to fill in the verb "increase" in the following sentence: "Ford Motor Company __ automation in its factories to become more efficient." Student answers can be judged adequately by providing the program with a list of possible wrong answers including "has increase, have increased, have increase, increases, etc." With an additional routine for detecting misspellings (e.g., "has incraese"), this approach will allow a program to appear intelligent provided its sole task is to judge whether input is in the present perfect form. This type of program certainly provides students with practice on individual grammar points; however, greater sophistication would lead to greater usefulness.

Intelligent grammar checkers are able to

address more complex grammar instruction tasks. With a parser which can recognize student input, it is possible to check several grammar points in a given lesson. The AI techniques used to accomplish these tasks are similar to those for first language grammar checkers; however, these techniques in second language courseware can result in programs that appear quite intelligent. Because of the limited syntactic competencies of low-level non-native speakers, writing procedures to identify their errors may not be as difficult as writing procedures to find errors made by native speakers.

Since beginning language learners work with only a subset of the syntax and discourse of the target language, some intelligent grammar checkers use a subset of the grammar. One program that uses an augmented transition network (Woods, 1970) works at the phrase level to check ESL learners use of time expressions (Sampson, 1986). At the sentence level, an ESL student who is learning simple sentences might use a grammar checker such as the one described by Loritz (1987). Imlah and du Boulay (1985) and Barchan, Woodmansee, and Yazdani (1986) also evaluate isolated sentences using a partial grammar of French. If the French system were given the sentence "Je donnera" to judge, it would return the message "The verb 'donnera' does not agree with the subject 'je.' It should be 'donnerai'" (p. 46). Sentences with errors in prepositions, article and adjective agreement, and other syntactic forms can all be detected by the same set of French grammar rules, rather than by individually anticipated wrong answers. Using a greater number of grammar rules, Sanders and Sanders (1987) are developing a program that checks for grammar errors in German discourse.

Each of these systems offers substantial answer- judging capabilities. They also provide students with options for their use; consequently, students will use them with varying degrees of success. Some students are likely to work efficiently as they test their grammatical hypotheses; others need the guidance of an ITS.

An ITS would not only note errors in student input; it would also keep a record of those errors, and use that information to provide help and additional instruction. Some writers have already considered how to employ ITS concepts in


Spanish (Feuerman, Marshall, Newman, and Rypa, 1987), ESL (Vernik and Levin, 1986) and German (Hart, 1980). Hart, for example, describes how the learner model could be constructed and used efficiently:

The goal of the program is to construct in the 'learner model' the most complete and accurate description possible of the student's FL grammar competence. This information would then be returned to the student or his instructor as a written profile of strong points and problem a reas . . . As a tool for precise diagnostic assess- ment, [the computer] is potentially far superior to paper- and-pencil instruments. It is capable of recording, sorting, retaining information about performance on thousands of error categories (Hart, 1980, p. 10).

This precise information could serve as a guide to the expert tutor in suggesting specific kinds of exercises. 4 The exercises themselves could use natural language processing techniques for generating exercise items and/or identifying mistakes.

The use of this kind of individualized instruction to accompany the activities of the second language classroom will make it possible to accomodate many learning needs and styles. The differences between individual language learners and the importance of those differences have been recognized for some time. Unsophisticated courseware and typical group instruction are insufficient to address those individual differences.

Summary Intelligent second language learning programs can mediate complex tasks including computer- student dialogs about real topics and can recognize novel linguistic input. Microwoflds and IGCs allow students to spend as much time as they wish testing their target language expressions and vocabulary. Such activities could encourage functional language use on topics such as family relations (Underwood, 1984). In class the instructor might use a short reading or listening passage to introduce key vocabulary and expressions related to the family. Students would have the opportunity to see the material in context and ask questions about it. The instructor could then send students to the computer lab to try a conversation with the program. At this stage, when students' use of vocabulary and expressions would be very hesitant, their lack of fluency could make class-

room use of the new language uncomfortable and time-consuming. After students have engaged in a conversation with the computer about family relations, they should return to class better pre- pared to begin, for example, oral role playing. Because of varying abilities, some students at this point may have improved their fluency considerably, while others would still experience difficulties. The latter could be sent back to the lab to continue work with the program, or to meet in conversation groups. Microwoflds and databases could provide individual discussions on a variety of topics to meet the interests of different students.

Intelligent grammar checkers offer second language students powerful out-of-class exercises and writing aids. These programs might be used as homework exercises to reinforce syntactic struc- tures introduced in class. After an introduction to the placement of indirect object pronouns in French, for example, the student might use a grammar checker to test some sentences using these constructions. The system developed by Sanders and Sanders (1987) allows students to check their essays before handing them in to the instructor. In either case, out-of-class use of intelligent programs will save class time as well as the instructor's time for the multiple other aspects of the language that need to be covered.

For the many students who need guidance in second language study, an ITS for assistance with grammar could significantly aid the second language instructor. By providing appropriate individualized instruction to meet the variety of student needs in the second language classroom, an ITS provides for individuality in language instruction that the dynamics of a classroom situation rarely allow.

Intelligent CALL: Where Will It Come From? The vision of today's AI in tomorrow's language classes is enticing but we must ask how AI research and intelligent language prototypes will emerge into microworlds, IGCs, and ITSs that individualize language instruction in significant ways. The answer is not surprising: building effective learning tools requires research on student learning.

Development of intelligent courseware requires an understanding of the process of language


development. To this end, research is conducted to better understand the cognitive processes and the incremental development of different kinds of learners. Research on writing examines protocols (Ericsson and Simon, 1984) of successful writers to describe their strategies (Swarts, Flower, and Hayes, 1983). Likewise, second language acquisition research contrasts strategies and defines characteristics of successful and unsuccessful language learners (Brown, 1987; Wenden and Rubin, 1987). In addition, researchers systemati- cally describe the stages of development learners go through in terms of internalized linguistic systems at progressive stages (Hatch, 1983) and the effects of instruction (Long, 1983). Results from this research are essential for planning appropriate intelligent language software.

With respect to intelligent microworlds and grammar checkers important issues of design and user interface need to be addressed. For example, it remains to be specified which natural language processing formalisms 5 are best suited to instructional applications whose requirements differ from those of other language analysis applications. 6 What is the minimum subset of a grammar that needs to be encoded for second language learners at particular stages of students' development? 7 What kinds of errors should a parser identify? 8 Should all identified errors be pointed out to students? How should error messages be phrased? How can the effects of student interaction with microworlds be documented? 9 The intelligent microworld offers developers the capability to provide students with a great deal of information about their language and strategies. Therefore, it is necessary to make decisions about the quantity and form of information likely to be useful to students; systematizing intelligent teaching practices forces an examination of how instructors work with students intuitively and a definition of which practices facilitate learning for particular students.

Design of an ITS demands an even more explicit formalism of teaching practices because, by definition, it includes strategies for instructing students. However, expert system technology provides no formulae for effective teaching; instead, it provides a means for encoding what are known to be effective teaching practices. The

problem is that there are few principles, and fewer specific techniques, that can be categorically identified as effective teaching practices. A second problem is the need to decide on the amount and kind of information included in the student model maintained by the ITS. How should that information be represented, 1° and how should the teaching strategy interpret it? The formalization of the teaching process requires a careful analysis of teaching and learning and a statement of explicit hypotheses as to how they might best be accomplished.

Aside from questions of system design, there are empirical questions concerning how these systems might be put to the best use. This article has described a number of general ideas on this topic; however, for maximum effectiveness the right programs must be assigned to the right students at the right times. Jamieson and Chapelle (1988) point out five learner variables -- age, expectations, ability, cognitive style, and affect -- that may be significant in making appropriate matches between software and students. In this respect, intelligent courseware differs little from any other courseware used and misused in the past. However, ITSs may be capable of assessing relevant characteristics of students to help in assignment of appropriate activities on the basis of their language performance and strategies for interacting with the materials. Work is just beginning to assess learner strategies as they work on computer materials (Jamieson and Chapelle, 1987; O'Mera, 1986); consequently, it is not known how much relevant information can be inferred about a writer or a language learner on the basis of computer-collected data.

Until ITSs are able to create and make effective use of student models, instructors must carefully evaluate software and help some students use it. Past research provides some clues about the learning preferences and success of good versus poor learners (Steinberg, 1977), motivated versus unmotivated students (Chapelle and Jamieson, 1986), field independent versus field dependent students (Chapelle and Jamieson, 1986; Abraham, 1985) in a computer-assisted learning environment. However, this research is in its early stages and it would therefore be premature to make definite recommendations. Nevertheless, such


research is needed to offer insights into future practices in intelligent courseware.ll

Preparation for future courseware thus requires careful attention to research and developments in AI, the psychological and linguistic aspects of language development, and research on current CALL practices. As research and practice progress, instructors need to make intelligent use of existing software: they need to understand computer learning activities, make assignments on the basis of what they believe will be beneficial to individual learners, and then observe the extent to which their hypotheses are fulfilled. Use of current courseware will enable instructors not only to use future intelligent microworlds, IGCs, and ITSs but also to provide practical advice for their development.

NOTES

In speculating about intelligent systems, we must envision implementations that incorporate experimental AI techniques which will not be seen in routine educational applications for many years. 2 The distinction traditionally made in the education litera- ture is between microworlds and intelligent tutoring systems (Lawler and Yazdani, 1987); however, in discussing intelligent CALL it is appropriate to add the third type of system: an intelligent grammar checker. 3 For a discussion of the variance in students' performance on different tasks, see Tarone (1984).

4 However, research on second language acquisition points out that it is insufficient to examine students' errors to describe their linguistic knowledge because they often avoid forms that they are uncomfortable with (Schachter, 1974). 5 The term "formalism" refers to a defined method for specifying information on a particular topic. The use of computers to analyze language has prompted the development of formalisms for precise definition of linguistic relations. The concept of a formalism is described eloquently in Chapters 1 and 2 of Hofstadter (1979). 6 Many applications require only that the analysis yield a semantic interpretation from well-formed input. This can be less complex than describing errors in possibly ill-formed input. 7 This question is also an interesting one for second language researchers who attempt to define learners' grammatical competence (interlanguage) at various stages of development (Hatch, 1983). A program containing a grammar that can interpret all student input at a particular stage provides an explicit formal account of the limits of the learners' language. s Imlah and du Boulay (1985) exemplify some of the things that must be considered in the parser's decision of what to count as an error.

Culley, Mulford, and Milbury-Steen (1986) describe how they examined the interaction between a student and teacher within the domain of their program in order to provide developers with an insight into the best types of information to present to students. 10 Many suggestions have been made concerning the best way to represent misconceptions revealed by students' errors (Brown and Burton, 1978; Putnam, Sleeman, Baxter, and Kuspa, 1986). 1~ There is a growing body of research on current CALL that should be considered for design of intelligent systems (Dunkel, forthcoming; Bridwell, Nancarrow, and Ross, 1983).

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Using intelligent computer-assisted language learning