Tag: change

What do we know about artificial intelligence (AI) in language teaching and learning already? What can we see if we look back more than two or so years? In the last two years, discourses on generative AI (GenAI) in the academic literature on (language) education, writing, publishing, (machine) translation, computer science, and many other areas as well as in mainstream and specialized media have resulted in a multitude of articles, books, chapters, columns, essays, guidelines, opinion pieces, and tip sheets. Here, the time window will be much wider, to provide a more leveled, quasi-historical lens on the rapidly evolving AI approaches and tools in the context of language education (see also Stockwell (2024) for another brief retrospective).

My inspiration for this title came from the book  
Snyder, T. (2017). On tyranny: Twenty lessons from the twentieth century. Tim Duggan Books. 

I am sharing these early drafts of a book chapter I published in 
Yijen Wang, Antonie Alm, & Gilbert Dizon (Eds.) (2025), 
Insights into AI and language teaching and learning. Castledown Publishers. 

https://doi.org/10.29140/9781763711600-02.

Three very early milestone years are important in this context: 1948, 1950, and 1955. In 1948, the first publication that connects AI and language learning came out. In it, Alan Turing, often called the father of AI, mentions a number of different ways in which computers would be able to demonstrate their intelligence in the future: “(i) Various games, for example, chess, noughts and crosses, bridge, poker; (ii) The learning of languages; (iii) Translation of languages; (iv) Cryptography; (v) Mathematics” (Turing (1948) quoted in Hutchins, 1986, pp. 26-27, my emphasis). Also in 1948, the then brand-new field of Applied Linguistics reached a noticeable breakthrough with the publication of the first issue of Language Learning. A Quarterly Journal of Applied Linguistics (Reed, 1948). In 1950, what we call today the Turing Test was published as the “Imitation Game” (Turing, 1950). Seventy-four years passed before newspapers and magazines announced that ChatGPT-4 had passed the Turing Test. Researchers at UC San Diego had published a preprint (under review) about their replication of the Turing test (Jones & Bergen, 2024). “Human participants had a 5 minute conversation with either a human or an AI, and judged whether or not they thought their interlocutor was human. GPT-4 was judged to be a human 54% of the time” (p. 1). It was five years after the proposal of the Turing Test, which is meant to test the intelligence of a machine, that research and development in the field of Artificial Intelligence started. McCarthy et al. (1955) proposed “that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire,” coining the name of the field — Artificial Intelligence.

The intersection of artificial intelligence and (computer-assisted) language learning has thus had a trajectory of about 70 years and had been termed Intelligent CALL (ICALL) up until the advent of GenAI, when AI became the buzzword and label. The documented development of ICALL software and systems occurred later; Bowerman notes that “Weischedel et al. (1978) produced the first ICALL system …” (1993, p. 31). The system was a prototype German tutor implemented as an Augmented Transition Network with a semantic and syntactic component. Weischedel et al. (1978) reference earlier work in CALL, for example an article by Nelson et al. (1976). However, this and other earlier publications elsewhere seem to rely on string comparison, often character by character replacement, or regular expressions rather than natural language processing. It would only be the latter that is part of AI research and thus ICALL. ICALL played a significant role in Tutorial CALL (Heift & Schulze, 2015; Hubbard & Bradin-Siskin, 2004; Schulze, 2024) over many years, but it never became mainstream in CALL in terms of research and development. The label tutorial CALL captures the learning interaction of the student with the computer rather than interaction of the learner with other persons via the computer, as in computer-mediated communication. It is not only the utilization of AI that GenAI and ICALL have in common, GenAI has also brought a revival of the learner interacting with the machine and can thus be described as a form of tutorial CALL.

Heift and Schulze (2007) identified and discussed 119 ICALL projects over about thirty years, but with very rare exceptions these were research prototypes. Only a few ICALL projects had limited use in language classrooms (e.g., Heift, 2010; Nagata, 2002). In a review article, Schulze (2008) used a list of nine key desiderata for ICALL by the applied linguist Rebecca Oxford (1993) to discuss developmental trajectories in ICALL:

1. Communicative competence must be the cornerstone of ICALL.
2. ICALL must provide appropriate language assistance tailored to meet student needs.
3. ICALL must offer rich, authentic language input.
4. The ICALL student model must be based in part on a variety of learning styles.
5. ICALL material is most easily learned through associations, which are facilitated by interesting and relevant themes and meaningful language tasks.
6. ICALL tasks must involve interactions of many kinds and these interactions need not be just student-tutor interactions.
7. ICALL must provide useful, appropriate error correction suited to the student’s changing needs.
8. ICALL must involve all relevant language skills and must use each skill to support all other skills.
9. ICALL must teach students to become increasingly self-directed and self-confident language learners through explicit training in the use of learning strategies. (p. 174)

Here, these desiderata will be adapted and used as a tertium comparationis when drawing lessons from the ‘history’ of ICALL for the emerging use of GenAI in language education, using them also as a structuring criterion for these blog posts as follows:

1. Exposure to rich, authentic language
2. Communication in context
3. Varied interaction in language learning tasks
4. Appropriate error correction and contingent feedback
5. Recording learner behavior and student modeling
6. Dynamic individualization  
7. Gradual release of responsibility

A discussion of the work in ICALL as such over the decades is beyond the scope of this chapter; in addition to the review article mentioned above (Schulze, 2008), overviews of ICALL research can be found in the monograph by Heift and Schulze (2007), which also provides an introduction to the main concepts and research questions in the field about 20 years ago, in a chapter (Nerbonne, 2003) in The Oxford Handbook of Computational Linguistics, and in articles (Gamper & Knapp, 2002; Matthews, 1993) in CALL journals. Many publications on ICALL appeared in edited volumes and in refereed conference proceedings and journals on computational linguistics, broadly conceived, and thus outside of the literature on CALL. This might be one of the reasons why GenAI was such a surprising novelty in language education in general and CALL in particular and why a focused retrospective can further our understanding of role and developmental trajectory of GenAI in language education today. We start with an excursion into a branch of AI that is relevant here – natural language processing (NLP).

… to be continued …

References

Bowerman, C. (1993). Intelligent Computer-Aided Language Learning. LICE: A System to Support Undergraduates Writing in German [PhD Thesis, UMIST]. Manchester.

Gamper, J., & Knapp, J. (2002). A Review of Intelligent CALL Systems. Computer Assisted Language Learning, 15(4), 329-342.

Heift, T. (2010). Developing an Intelligent Tutor. CALICO JOURNAL, 27(3), 443-459.

Heift, T., & Schulze, M. (2007). Errors and Intelligence in CALL. Parsers and Pedagogues. Routledge.

Heift, T., & Schulze, M. (2015). Tutorial CALL. Language Teaching, 48(4), 471–490.

Hubbard, P., & Bradin-Siskin, C. (2004). Another Look at Tutorial CALL. ReCALL, 16(2), 448–461.

Hutchins, J. (1986). Machine Translation – Past, Present and Future. Ellis Horwood.

Jones, C. R., & Bergen, B. K. (2024). People cannot distinguish GPT-4 from a human in a Turing test. http://dx.doi.org/10.48550/arXiv.2310.20216

Matthews, C. (1993). Grammar Frameworks in Intelligent CALL. CALICO JOURNAL, 11(1), 5-27.

McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (1955). A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence. Retrieved Sep 30 from http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html

Nagata, N. (2002). BANZAI: An Application of Natural Language Processing to Web-Based Language Learning. CALICO, 19(3), 583-599.

Nelson, G. E., Ward, J. R., Desch, S. H., & Kaplow, R. (1976). Two New Strategies for Computer-Assisted Language Instruction (CALI). Foreign Language Annals, 9(1), 28-37.

Nerbonne, J. A. (2003). Computer-Assisted Language Learning and Natural Language Processing. In R. Mitkov (Ed.), The Oxford Handbook of Computational Linguistics (pp. 670-698). Oxford University Press.

Oxford, R. L. (1993). Intelligent computers for learning languages: The view for Language Acquisition and Instructional Methodology. Computer Assisted Language Learning, 6(2), 173-188.

Reed, D. W. (1948). Editorial. Language Learning, 1(1), 1–2.

Schulze, M. (2008). AI in CALL – Artificially Inflated or Almost Imminent? CALICO JOURNAL, 25(3), 510-527.

Schulze, M. (2024). Tutorial CALL — Language practice with the computer. In R. Hampel & U. Stickler (Eds.), Bloomsbury Handbook of Language Learning and Technologies (pp. 35–47). Bloomsbury Publishing.

Snyder, T. (2017). On tyranny: Twenty lessons from the twentieth century. Tim Duggan Books.

Stockwell, G. (2024). ChatGPT in language teaching and learning: Exploring the road we’re travelling. Technology in Language Teaching & Learning, 6(1), 1–9.

Turing, A. (1950). Computing machinery and intelligence. Mind, LIX(236), 433-460.

Weischedel, R. M., Voge, W. M., & James, M. (1978). An Artificial Intelligence Approach to Language Instruction. Artificial Intelligence, 10, 225-240.