Computers and the Humanities 23 (1989) 3--11. © 1989 by Klu wer Academic Publishers.

INTRODUCTION: Intelligent Computer-Assisted Language Instruction

A l a n Bail in

Effective Writing, The University of Western Ontario, London, Ontario, N6A 3K7, Canada

and

L o r i L e v i n

Center for Machine Translation, Carnegie Mellon University, Pittsburgh, PA 15213, U.S.A.

Abstract: This introduction to the special issue of Com- puters and the Humanities (CHum) on intelligent computer- assisted language instruction (ICALI) presents an overview of ICALI research. It begins by discussing ICALI as a kind

Alan Bailin (Ph.D. English, McGill) is an English Usage Specialist at The University of Western On- tario. His research focuses on semantics/pragmatics and ICALL He has been principal researcher in a number of CALl software projects. Among his publications are "Metaphorical Extension," "Fact and Fiction," and "Natural Language Processing and Computer-Assisted Instruction." He is pre- sently engaged in writing a book on the semantics/ pragmatics of metaphor.

Lori Levin (Ph.D. Linguistics, MIT) served on the faculty of the Linguistics Department at the Uni- versity of Pittsburgh. Currently she is a Research Associate at the Center for Machine Translation at Carnegie Mellon University, and is on the core faculty of the Pitt-CMU Joint Program in Com- putational Linguistics. Levin has worked on two ICALI projects: the MINA writing tutor project and on a project aimed at developing a grammar tutor for English as a Second Language. For her current position at CMU's Center for Machine Translation, Levin supervises the implementation of syntactic grammars, and contributes to the design of lexicons and of rules which map syntactic representations onto semantic representations.

of Artificial Intelligence research, and then proceeds to out- line the components and kinds of ICALI systems. Next, it examines such practical research considerations as the kinds of personnel needed to develop ICALI software. Finally, it indicates what aspect of ICALI research is discussed by each of the other articles in the special issue.

Key Words: intelligent computer-assisted language instruc- tion, ICALI, artificial intelligence, AI, computer-assisted language instruction, CALI, computer-assisted instruction, CAI, writing, second language teaching.

One might well ask why we have devoted this issue of Computers and the Humanities to intelligent computer-assisted language instruction (ICALI). First of all, ICALI is in no way a large field: there are, in fact, relatively few projects devoted to the development of ICALI software. Second, it has not created a substantial body of theory: the work is still in its infancy. Finally, it has not yet produced much significant software: what software there is often hints at far more than it can deliver. Why, then, devote a special issue to the field?

One of the most important reasons is the significance of ICALI as a research domain for both Artificial Intelligence (AI) and the humanities. Presumably the ultimate goal of research in Artificial Intelligence is to model human cognitive behavior. ICALI research focuses on this central goal. Unlike some work in Artificial Intelligence, ICALI is primarily concerned not with applying

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sophisticated programming techniques to technical problems (see, for example, Buchanan, 1985), but rather with the modeling of the cognitive behavior which occurs in a normal human activity, language teaching.

ICALI is also an area in which the tools of computational science are being applied in a thorough manner to the humanities. ICALI research must be concerned not only with pedagogy but also with the internal properties of the subject matter, language. The results of the research are thus important to all who are interested in looking at traditional humanities areas through a modern analytic lens.

In addition to its importance as a domain of research, the software which ICALI projects produce may eventually change the way in which we teach language and language skills. As ICALI software becomes more and more able to assume some of the onerous aspects of language teaching, language teachers can view their roles in different ways and begin to look for alternatives to the traditional classroom.

The appeal of ICALI lies both in its intrinsic interest as a research domain and in the promise of the software. However, one cannot help feeling awed by the task of constructing a truly intelligent computer program for language instruction. Build- ing a system can involve a large number of tasks: collecting and studying detailed information about the learner, the subject matter, and the learning process; designing teaching materials including help and feedback files; incorporating Artificial Intelligence tools such as parsers and knowledge representations; combining the expertise of specialists in first and second language teaching, linguistics, cognitive psychology, and computer science.

As can be seen, the domain is vast. In this introduction, we will sketch the general outlines of this developing field in order to highlight the contribution each paper makes to ICALI research. We will first look at the relation of ICALI to the general concerns of Artificial Intelligence. We will then examine briefly the knowledge components which ICALI needs to create systems and present a taxonomy of the kinds of software development. Next, we will consider the research requirements

of ICALI projects. Finally, we will indicate the ways in which the articles in this issue relate to this general overview of the field.

Artificial Intelligence and ICALI Artificial Intelligence has been defined as the use of computers to simulate intelligent behavior. However, it has also been pointed out that AI is a sub-field of cognitive science as well as computer science and, as such, is concerned with the study of human intelligence (Charniak and McDermott, 1985; Haugeland, 1981; Stillings et al., 1987; Pylyshyn, 1981). In other words, researchers hope that models of intelligence that can be program- med on a computer can also serve as models of human intelligence.

A premise of most AI research is that intelligent behavior consists of the manipulation of knowl- edge (Winograd, 1983 and Stillings et al., 1987). It follows that in order to build an AI program to simulate intelligent behavior, it is necessary to identify the pertinent knowledge and construct a way of representing for a computer both the knowledge and the ways in which it must be manipulated. All AI systems consist of representa- tions (coding) of knowledge and the procedures for manipulating it.

It is therefore essential to define a representa- tion. There are, however, important differences between the representations used in the humanities and those in AI. In the humanities, graphs, tables, categorial descriptions and other devices for representing knowledge are normally used as a way of either illustrating or organizing ideas and information. They are generally not intended as precise descriptions. AI representations, on the other hand, are intended to be descriptions so exact that they can be used in instructions which tell a machine (i.e., a computer) what mechanical operations to perform.

Consider, for example, the parsing of a sentence (that is, the assignment of a grammatical structure to a sentence). A pedagogical grammar of the kind normally used in language courses gives informal descriptions of the categories which are to be used in the analysis and the ways in which these categories are to be applied. Thus a noun is said to be a person, place or thing (sometimes also an

INTRODUCTION: INTELLIGENT COMPUTER-ASSISTED LANGUAGE INSTRUCTION 5

activity), and the student is instructed to apply the term to single words of this sort. Such categorial descriptions can clearly only be a guide. Intuition and guesswork must be used to fill in the gaps. A computer, however, has no intuitions and, as a consequence, the representation of grammatical categories cannot be so vague. If a word is to be called a noun, it must be a member of a set of words which are specifically stipulated as func- tioning as nouns in particular linguistic contexts (for example, in a context in which the word is immediately preceded by an article, or an article and an adjective). The representations must be precise enough that a machine can use them, and them alone, to identify nouns in a sentence.

AI representations are not, of course, restricted to language; they can pertain to any type of human cognitive behavior and knowledge. ICALI systems encompass only a particular subset of these types - - the types of knowledge and ability which can be used in teaching a language. Nevertheless, this subset is quite large. An ICALI program can include representations of, among other things, an expert's knowledge of one or more aspects of grammar, pedagogical strategies to handle a student's incomplete or imperfect knowledge of a language, and the knowledge of cultural contexts in which the target language is used.

In these and other domains, the AI representa- tions used in ICALI provide a new way of exploring the concerns and interests of the humanities, a way which is far more exact and analytical than traditional approaches. They can allow us not only to build more capable computer- assisted language instruction (CAM) software but also to gain a more precise understanding of the cognitive knowledge and abilities that constitute the psychology of language teaching and learning. In the following section we examine more closely the knowledge components which are used in ICALI systems.

C o m p o n e n t s o f an ICALI S y s t e m The versatility and intelligence of ICALI systems come from the interaction of a variety of compo- nents which fall under two basic AI rubrics: natural language processing (NLP) and problem solving. The components correspond roughly with

the knowledge and skills which a language teacher (ideally) possesses. The design of each of these components is an active research question and most actual systems do not contain all of them.

Natural Language Processing Components An ideal ICALI system would have the ability to process natural language just as a language teacher does. For example, it might parse sentences, generate sentences, search for common error patterns, "understand" a text by storing it in some sort of knowledge representation, be able to identify speech acts, or resolve pronoun reference. However, at the moment, no ICALI system comes close to this ideal. In fact, the natural language processing requirements of existing systems depend on the teaching point and instructional strategy employed. Current ICALI systems use one or more of the following natural language pro- cessing components: (1) syntax, (2) morphology, (3) semantics/pragmatics and, possibly in the near future, (4) phonology. Let us look briefly at each in turn.

Syntax. Although there is not complete agreement about the boundaries between linguistic compo- nents, syntactic theories have included word order, the structural relations between words, phrases and clauses, grammatical relations (subject of, object of), some kinds of anaphora, and semantic roles (such as agent of an action and patient). Work on syntax in computational linguis- tics is particularly concerned with syntactic representations which can allow computers both to parse sentences (that is, assign grammatical structures to them) and to generate them. Syntactic parsing is used in ICALI programs which check for grammatical errors and also in many programs which attempt to "understand" language (see the article by Sanders and Sanders in this issue). Language generation is used in ICALI programs which generate sentences for dialog with students or for strictly grammatical exercises (see Mulford's article in this issue, and Bailin and Thomson, 1988).

Morphology. Morphology is the study of the structure of words. Since words can be divided

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into units such as prefixes, suffixes, and roots, the issue of compactness -- when to store complete words in a lexicon and when to store only roots -- is of paramount importance. In languages like English it is relatively simple to store a reasonable set of complete words, but storing all of the complete words in a heavily inflected language could be quite costly.

Morphology also plays a role in the extraction of grammatical information from words during parsing. With respect to this, linguists make a distinction between derivational and inflectional morphology. Derivational morphology is con- cerned with the ways in which some words are derived from others (un + happy + ness -- unhappiness); inflectional morphology with the way words change (i.e., inflect) in relation to gram- matical contexts. Inflectional morphology is a key part of the grammatical processing of languages like Latin and German which encode grammatical relations through inflections on nouns (case mark- ing) and verbs (agreement). In ICALI programs for such languages, inflectional morphology plays a role in parsing and language generation similar to that of syntactic structure in determining gram- matical relations.

Semantics/Pragmatics. Semantics looks at meaning abstracted from context; pragmatics examines it within context. Unlike work in theoretical linguis- tics, computational linguistic work on meaning has not payed much attention to the distinction, since machine "understanding" of discourse must incor- porate both (see Mulford's article in this issue). Semantics/pragmatics is also a necessary com- ponent of any ICALI program which involves parsing because without such information, it is sometimes impossible to decide on the gramma- tical structure of a sentence.

Phonology. Although phonology, the study of sound systems in language, is one of the more developed parts of theoretical linguistics, it is harder to handle in computational practice than it is in theory. One basic problem is that it is difficult to identify the phonetically relevant aspects of acoustic signals: depending on properties of the speaker's voice and on the surrounding sounds, different sounds will look the same on a sound spectograph and the same sound may look differ-

ent (Lea, 1980). It is also difficult, without con- siderable extra-phonetic information, to identify the boundaries of words in continuous speech (Rich, 1983, pp. 345--46). Nevertheless, recent limited applications in other software give hope that phonological processing will soon become a useful part of ICALI programs.

It should be noted that the components of natural language systems are only partly available in off-the-shelf form and usually require extensive augmentation and customization, if not complete reconstruction. For example, it is often possible to find a good public domain parser, but the type of grammatical information required by the parser can vary greatly depending on what the parser is being used for. Furthermore, most parsers and other NLP programs are not typically designed to identify and diagnose errors; they simply fail when they encounter ill-formed input and do not identify the cause of the failure. For ICALI applications which require exact diagnosis of errors, parsing programs must be augmented with error-detection procedures (see the article by Sanders and Sanders in this issue). This in fact means it is often necessary to write a specialized grammar for each ICALI system.

It should also be kept in mind that an essential, but often under-emphasized, part of any natural language component is a lexicon: an annotated list of words from the target language. The annota- tions contain information needed for parsing, pattern matching, or knowledge representation -- e.g. part O f speech, number, person, gender, and relationship to other semantically similar words (e.g. sparrows are birds). They are the core of natural language systems because parsers, pattern matchers, semantic networks, and text generators cannot operate without detailed linguistic information about individual words and phrases.

Unfortunately, lexicons for ICALI systems often contain at least several thousand words and it is frequently quite time consuming to identify and enter all of the features which must be stored for each word. On-line word lists and dictionaries are available but before they can be used in natural language systems, the information they contain must be reformatted and augmented to suit the needs of the system. Computational lexicography

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is becoming an increasingly important area of research (see, for example, Walker et al., 1987).

Problem Solving Components: Models for Teaching and Learning In much AI work, problem solving is a matter of implementing rules or procedures that can be articulated. There is a technical problem (for example, identifying the medical illness which has resulted in a particular set of symptoms) and there are strategies for solving it (rules for diagnosing the illness from the symptoms). However, in ICALI problem solving techniques must be applied not so much to strictly technical questions but rather to the psychological and pedagogical issues involved in teaching and learning languages and language skills.

In effect, the problem in ICALI is how to teach students with the computer playing the social role of teacher. The problem is solved by building into the software (1) expertise in relation to the skills and knowledge to be acquired, (2) a "conception" of the student's abifity at a particular point, and (3) a pedagogical strategy for teaching the skills to a student at the student's level of ability. In ICALI these three properties have been dealt with increasingly in terms of models of expertise, learning, and teaching (see Neuwirth's article in this issue).

Model of Expertise. A central component of any ICALI system is a model of an expert's knowl- edge or skill concerning the teaching point. In an ideal system, the expert model would contain native skill in all areas of the target language. In practice, however, an ambitious ICALI system would model a minute piece of expertise on the teaching point. For example, a grammar instruc- tion program might use a parser with a partial grammar of the target language as a model of expertise whereas a writing instruction system such as PARNASSUS might include rules for generating sentences which cohere with surround- ing sentences (see Neuwirth's article in this issue).

For example, a grammar checking program might contain grammar rules to handle typical student errors, and a reading comprehension program might include rules for processing complex sentences in the same incorrect manner as a student.

Another aspect of student modeling is tracking the level of skill the student acquires over time. In a model of this sort, the program makes a note every time it has evidence of the student acquiring a required skill. For example, it would note that the student correctly passivized some number of yes-no questions with two auxiliary verbs. This type of model is useful in formulating feedback, particularly in distinguishing careless mistakes (in skills the student has mastered) from real miscon- ceptions (in skills that the student is still working on) (cf. Burton and Brown, 1982).

Pedagogical Model. The pedagogical model imple- ments the pedagogical strategy of an ICALI system. It incorporates the strategy for presenting material to students and the kind of feedback and help (including remediation) which the system offers. Help and feedback, an important part of a pedagogical model, could be produced by access- ing the system's expert, student, and pedagogical models and then feeding the information through both an explanation and a natural language generator (cf. Wenger, 1987). However, all ICALI programs that we know of store feedback and help in text files. In fact, hypertext configurations of exercises with grammatical and cultural infor- mation about the language are gaining in popularity. These provide the student with maximum flexi- bility in accessing information about the language (see Underwood's article in this issue).

ICALI Types Just as the knowledge components of ICALI systems roughly correspond to the kinds of skills a language teacher ideally has, the kinds of ICALI systems roughly correspond to the kinds of roles a language teacher (ideally) performs.

Model of the Student. This component represents a student's partial or partially wrong knowledge of the target language. Again, since it is not possible at this time to model all aspects of the student's knowledge, each system models a small piece of it.

Intelligent Tutoring Systems (ITS) One of the most important functions of a language teacher, both in the early stages of language learning and in the teaching of writing, is to operate as a tutor -- that is, someone who, in an

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individualized manner, presents information con- cerning the subject matter and helps the student to learn it by presenting situations where the infor- mation must be used. In terms of language instruction, the material presented is generally aspects of the grammar and rhetoric of the target language. The practice situations generally involve drill-and-practice and/or revision exercises (see Chapelle's article in this issue for other kinds of ITS exercises and functions).

An ITS attempts to replicate the role of a tutor on a computer. ITS systems present the student with controlled exercises which are geared to the student's level of ability and give the student the kind of help and feedback which a tutor would. In building an ITS one attempts to understand as precisely as possible what the teacher does in tutoring situations and to model the computer's behavior on this understanding (see Neuwirth's article in this issue). This can involve not only employing expert, student, and teaching models, but also incorporating enough linguistic skills to interact with the student in the target language.

Microworlds Simulation is a tried and true technique for language instruction. Children learning a second language are often asked to act out scenes from short plays or to pretend they are in situations where they must use the target language. What microworlds attempt to do is to have the computer present such situations and play the role of an overseeing teacher who helps correct grammar, rhetoric, and vocabulary.

Although the techniques employed in micro- worlds borrow heavily from adventure games, they go substantially beyond the rather rigid response mechanisms which such games generally employ. If a microworld is to be successful as an "intelligent" teaching device it must, at least to some degree, be able to "understand" what a student is saying. Such understanding must involve not only the ability to react differentially to different meanings/propositions, but also the ability to identify linguistic errors and nevertheless to construe an appropriate meaning (see Mulford's article in this issue). One can, in fact, look at the syntactic and semantic processing which goes on in a microworld as a limited test sphere for the processing which is needed in open-ended text processing.

Open-ended Text Processing Perhaps the most time consuming part of any language teacher's work is the correction of student writing. Open-ended text processing attempts to simulate this role. At the moment, such processing is limited to the realm of grammar (intelligent grammar checkers) and even within that realm it is nowhere near as complete as a teacher would be. The obstacles to overcome are substantial: the role of semantics in determining some syntactic structures, the development of appropriate error detection mechanisms, strate- gies for partial parses of "sentences" with errors, etc. Nevertheless, open-ended text processing, even in its present state, can be of substantial use in language instruction. Computer identification of even a small number of grammatical errors can help students to improve their work (Hull, 1986; Hull et al,, 1987).

Expert Systems Another function of a teacher is to give advice and help solve problems a student is having with the subject matter. In relation to language instruction, this means giving relevant information concerning the grammar and/or rhetoric. A student poses a problem ("How do you say x?') and the teacher helps solve the problem by explaining not only how one says x, but also why x must be said in that way. What this means is explaining the rules and/or conventions which relate to the particular problem and how these rules/conventions apply to it.

In such cases, what a teacher is in effect doing is operating as an expert who has at his/her disposal the relevant rules and knows how to apply them to particular situations. In terms of AI, this function corresponds to the kind of program which is often called an expert system. It is interesting that very little work has been done to develop systems of this sort for CALI, although in most other areas this particular application is receiving the most attention.

Practical Research Considerations: Personnel, Pedagogical Materials and Time

Personnel The personnel needed to build an ideal ICALI program are as diverse as the components of the system. ICALI projects can benefit from the input

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of computer scientists, linguists, cognitive psychol- ogists, and language teachers. In this section we will sketch the potential contribution of each specialist.

Computer Scientists and Programmers. Program- mers are obviously necessary in developing ICALI systems. They program the overall control structure which determines the interaction of the different components, write the natural language processing software and error detection routines, create the authoring system and interfaces for linguists and other system developers, and implement the expert, student, and tutoring modules.

It should be noted that although computer scientists can be and often are the chief designers of ICALI systems, they are often not trained to deal with the communications, educational, and linguistic factors which must be considered in developing ICALI systems. For this reason, in at least some ICALI projects, the responsibility for much of the overall design has been given to courseware designers, specialists in developing CALI software. Generally these specialists are not programmers, but rather professionals from other areas (language teaching, linguistics, etc.) who have become well-versed in the issues related to the design of CALI systems. Courseware design is clearly an area where computer science interfaces with the humanities and social sciences. If it is to become more sophisticated, interdisciplinary programs will probably need to be developed.

Linguists. Linguists offer expertise in formulating and formalizing linguistic rules and can contribute to all areas of natural language processing: devel- oping lexicons, writing grammar rules for parsers and text generators, writing semantic networks for knowledge representation systems, and writing morphological rules for spelling checkers.

Ideally, the ICALI work which linguists perform should be informed by both computational and theoretical linguistics. However, linguists involved in ICAL1 have generally concentrated on compu- tational linguistics while the potential role of theoretical linguistics has been almost universally ignored. Thus ICALI reports will discuss the kinds of parsing mechanisms which are employed (an essentially computational linguistic concern), but not the kind of syntactic representation which is

produced (an essentially theoretical linguistic concern since it relates directly to the grammar of the language).

Nevertheless, linguistic representations should be a major concern of !CALI since these represen- tations contain the linguistic information which ICALI can use. Different linguistic grammars produce representations which encode different kinds of information in different ways. The point is not that one type of linguistic grammar is better than another, but that different kinds of represen- tations and hence grammars may be more or less useful in particular kinds of ICALI applications. In addition, a linguistic representation which is not theoretically informed may fail to encode information which an ICALI system could use to better perform its pedagogical functions. Such issues as this can be properly explored only if linguists with theoretical (rather than strictly computational) interests become more involved in ICALI projects.

Cognitive Psychologists and Psycholinguists. Many tutoring systems for subjects other than language have based their expert, student, and tutoring modules on cognitive research concerning skill aquisition, learning, and the differences between experts and novices (see, for example, Anderson, 1983; Newell and Simon, 1972; Simon and Chase, 1973; Chi, Glaser and Rees, 1982). An underlying theme of much of this research is that many mistakes are actually systematic" errors (Brown and Burton, 1978) possibly resulting from miscon- ceptions about the subject matter or flaws in the student's problem solving procedures. In the area of language learning, work on error analysis provides evidence for the systematic nature of student errors (see, for example, Shaughnessey, 1977, and the papers in Richards, 1974). What is obviously called for is research on teaching and learning which reveals the nature of the systematic errors made by students.

Some ICALI researchers have taken steps in this direction (see Neuwirth's article in this issue; Hull et al., 1987; Hull, 1986). However, the field as a whole has done very little to relate ICALI to research on second language acquisition or error analysis and this is perhaps the largest missed opportunity of ICALI to date. In the future we hope to see ICALI benefit from the input of

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second language acquisition research and second language acquisition research enhanced by the testing and evaluation made available by ICALI.

Language Teachers. The skill and insight of language teachers are crucial in most areas of an ICALI project. Teachers who can put into words and formalize their many years of experience are probably the most valuable people for designing expert and student models and pedagogical strate- gies. In addition, teachers are needed to fill in feedback and help files, produce lessons and exercises, consult with computer scientists on the design of authoring systems, and conduct error analyses.

All of the above mentioned tasks are crucial to the success of an ICALI project. However, the language teachers who have responsibility for them will not be able to perform them properly unless they have learned how to articulate in precise terms (i.e., formalize) their knowledge of language teaching. At the moment, this skill must be self-taught. Here again, interdisciplinary pro- grams involving computer science, the humanities (language teaching) and the social sciences (educa- tional psychology) would be extremely useful.

The Development of Pedagogical Materials While one is designing the complex structure of an ICALI system, it is easy to forget that it must be filled in with exercises and lesson plans. The teaching materials implemented in ICALI systems can be quite diverse, ranging from traditional drills with intelligent error-detection to student-directed exploration of knowledge bases. Because of the complexity of ICALI programs, new lessons typically have to be coded by researchers and programmers, but if ICALI systems are to be put to practical use in the classroom, it will eventually be necessary for teachers to be able to enter new ICALI lessons for programs which do not generate them themselves. There are currently many excel- lent authoring systems for CALI, but an ICALI authoring system would need many additional capabilities. For example, adding a new lesson might involve extending the natural language capabilities in parsing, generation, or knowledge representation.

Time Considerations To the uninitiated researcher, time lines for ICALI projects can be surprising, including everthing involved in producing teaching materials along with everything involved in building a natural language processing system plus some work on expert and student modeling.

The immense effort required to build a working ICALI system stems in part from the unavailability of ready-made natural language software suitable for instructional purposes and from the fact that many thorny problems in natural language proces- sing have not been solved. Thus, implementing an ICALI program with natural language processing involves adapting whatever partial solutions are available and inventing some new ones, which may require a substantial amount of research.

Perhaps the most time-consuming task in any natural language processing system is the creation of knowledge bases such as grammars, semantic networks, and lexicons. In fact, filling these knowl- edge bases often takes more time than building the parsers and knowledge representation programs that interpret them, especially since an ICALI program might need new knowledge bases for each new lesson or teaching point.

Conclusion The articles in this special issue of CHum are intended to bring the reader closer to the issues involved in ICALI research than is possible in a general article such as this. Sanders and Sanders and Mulford discuss the two natural language processing components which are most important to present-day ICALI systems: syntax (parsing) and semantics. Sanders and Sanders present a general discussion of parsing in ICALI systems, while Mulford focuses on issues related to semantic processing with particular reference to the Univer- sity of Delaware Foreign Language Adventure Project with which he is currently involved. In the same vein as Mulford, Neuwirth discusses the issues related to modeling and design as they pertain to PARNASSUS, an ITS under develop- ment at Carnegie Mellon University. Chapelle examines the pedagogical uses of both present and future ICALI software, while Underwood specu- lates on the developmental trends in ICALI over the next decade.

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In a very real sense, these articles are the products not of a fully developed field, but of a research domain which is just beginning to come into its own. Thus the articles should be viewed as attempts to begin to define the issues which are of importance in ICALI. We hope that in the process of articulating these issues, this special issue of CHum sheds light on what we believe is an exciting new area of research.

ACKNOWLEDGEMENTS

As all writers are aware, editors have limitations. For this reason, the role of a journal "reader" is extremely important in helping to develop solid, scholarly articles. We would like to thank the following for their work in this role:

Ann Grafstein (University of Western Ontario) Bob Mercer (University of Western Ontario) Peter Robinson (University of Birmingham) Linda Schmandt (University of Pittsburgh)