Corpora and AI for inductive learning: Theory and practice

Guest Editors: Eniko Csomay, Reka R. Jablonkai, and Hui Sun

Inductive learning has been used as an umbrella term to refer to a range of instructional methods including discovery learning, problem-based learning, case-based learning, etc. While deductive learning starts with explanation of general principles followed by learners applying them to specific cases, inductive learning begins with specific observations, problems, and case studies, and learners discover principles and rules in the process of analyzing or interpreting these specific cases. Advantages for explicit inductive instruction have been identified in the acquisition of L2 morphosyntactic structures and pragmatic competence, and more recently, the application of corpora and generative AI using large language models (e.g., ChatGPT) in language learning has opened a new area of exploration.

The special issue will focus on applications of corpora and AI in connection with inductive learning. The special issue will explore how corpus-based pedagogy and AI tools can facilitate inductive learning and promote learner autonomy.

Suggested topics include but are not limited to
1. Theoretical underpinnings of applying corpora and AI for inductive learning
- How could corpora and AI enhance the process and outcomes of inductive learning?
- What type of learners can benefit most from the learning process?
- What is the role of teachers and peers in the learning process?
2. Instructional frameworks, principles, and strategies for integrating corpus use and AI for inductive learning
- What kind of corpus-based and AI-assisted activities are theoretically sound for effective inductive learning?
- What are the guiding principles of integrating corpus use and AI for inductive learning in language instruction?
- How can corpora and AI be used to develop language knowledge (grammar, vocabulary), language skills (speaking, listening, reading and writing), and various competences (e.g., academic competence, pragmatic competence, intercultural competence)?
- What kind of data literacy is necessary for such pedagogic applications?
3. Teacher perspectives on inductive learning using corpora and AI
- What are teachers’ attitudes to and perceptions of corpus and AI use?
- What are effective ways to train teachers to successfully integrate corpus use and AI in their teaching?
4. Student perspectives on inductive learning using corpora and AI
- How do students engage with corpora and AI?
- What learner training is required for successful learning outcomes?
5. Ethical considerations
- What are relevant ethical issues when applying corpora and AI for inductive learning?
- What safeguarding measures are necessary for applying corpora and AI in educational contexts?

Abstracts should describe position papers and empirical studies and include application of corpora and AI to inductive learning and its implications. Full-length articles should not exceed 8,000 words excluding tables and figures and Short Communication pieces should not exceed 4,000 words. See further details here (also on book reviews): https://www.sciencedirect.com/journal/applied-corpus-linguistics/publish/guide-for-authors.

Please send a max. 500-word abstract for a full-length article, or a max. 300-word abstract for a Short Communication piece. For all submissions, send the abstract without author(s) names. On a separate sheet, include each author’s name, affiliation, mailing address, e-mail address, telephone number, and a 50-word biographical statement.

Send abstracts to Eniko Csomay ([email protected]) by no later than October 15, 2024. Please indicate in the subject line whether it is a research article, a book review, or a short communication piece.