Australasian Journal of Educational Technology

Publisher:: —
ISSN:: 1449-3098
Category:: EDUCATION & EDUCATIONAL RESEARCH
Impact factor:: 3.3

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Analysing AI utilisation in education through learner question types: A constructivist approach

2026-02-26

Hyunmin Lee, Amara Atif, Kyeong Kang

This study investigated the evolving role of artificial intelligence (AI) in higher education by analysing learner-generated questions through a constructivist framework. Drawing on Piaget and Vygotsky’s theories, student inquiries were categorised into three roles: knowledge transmitter, facilitator and co-learner. Data from 11 students across 12 information technology courses yielded 434 authentic questions, expert labelled and augmented to balance class distributions. Several natural language processing models including bidirectional encoder representations from transformers (BERT; baseline and fine-tuned), disentangled attention BERT approach (DeBERTa) and robustly optimised BERT approach (RoBERTa) were evaluated for their ability to classify these questions. Results indicate that while models excel at processing factual (knowledge transmitter) queries, they face challenges distinguishing higher-order facilitator and co-learner questions. Notably, DeBERTa achieved the highest overall accuracy (86.36%) yet struggled with capturing contextual nuances inherent in complex queries. These findings underscore the potential of AI to support personalised learning and adaptive feedback in educational settings while highlighting the indispensable role of human oversight. Implications for integrating such models into learning management systems and avenues for future research including model refinement, cross-disciplinary validation and ethical AI implementation are discussed. Implications for practice or policy: Instructors could enhance learner engagement by integrating AI-based question analysis tools to provide tailored feedback based on inquiry depth. Course designers may need to incorporate AI-driven scaffolding strategies to support students' higher-order thinking skills. Learning management systems could benefit from embedding automated question categorisation functions to identify students' learning needs more efficiently. Educational institutions should consider developing ethical guidelines for the use of AI in formative assessment processes.

AI-enhanced informal digital learning of English: Effects on EFL students’ cognitive, non-cognitive and oral proficiency skills

2026-02-18

Amira Ali

This study examined the effects of integrating artificial intelligence (AI) tools into informal digital learning of English (IDLE) to enhance cognitive and non-cognitive skills, as well as listening and speaking proficiency among English as a Foreign Language students. A sample of 120 Egyptian university students participated in a mixed-methods design that consisted of a questionnaire, pretests and post-tests for listening and speaking skills and semi-structured interviews. Quantitative data were analysed using descriptive statistics, t tests and mixed analysis of variance, while qualitative responses were thematically explored. The findings revealed significant advancements in cognitive skills, including the regulation of attitudinal needs, goal commitment, resource allocation and metacognitive skills, as well as enhanced non-cognitive skills. However, social connections via AI were found to be less impactful, with many students reporting limited authentic interactions. While AI-driven IDLE significantly enhanced speaking proficiency, listening skills showed more modest gains, suggesting differential effects of AI on productive versus receptive skills. Despite technical challenges, AI-based IDLE demonstrated potential for personalising learning. Future research should address these challenges while focusing on bridging the gap between informal digital learning and real-world language use. Implications for practice or policy: Educators should integrate AI tools into blended learning models, combining AI-driven practice with real-world communicative opportunities to bridge the gap between simulations and authentic language use. Developers must prioritise customisation in AI tools, such as adaptive learning paths and realistic conversation practice, to address diverse learner needs effectively. Policymakers and administrators should invest in resolving technical barriers (e.g., speech recognition accuracy, Internet reliability) to optimise AI tool effectiveness and user experience.