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

Lee, H; Atif, A; Kang, K

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

Lee, H Atif, A

Kang, K

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Publisher:: Australasian Society for Computers in Learning in Tertiary Education
Publication Type:: Journal Article
Citation:: Australasian Journal of Educational Technology

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Field	Value	Language
dc.contributor.author	Lee, H
dc.contributor.author	Atif, A https://orcid.org/0000-0003-0381-0343
dc.contributor.author	Kang, K https://orcid.org/0000-0003-4252-9802
dc.date.accessioned	2026-02-28T19:10:57Z
dc.date.available	2026-02-28T19:10:57Z
dc.identifier.citation	Australasian Journal of Educational Technology
dc.identifier.issn	1449-3098
dc.identifier.issn	1449-5554
dc.identifier.uri	http://hdl.handle.net/10453/193750
dc.description.abstract	<jats:p>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.</jats:p>
dc.language	en
dc.publisher	Australasian Society for Computers in Learning in Tertiary Education
dc.relation.ispartof	Australasian Journal of Educational Technology
dc.relation.isbasedon	10.14742/ajet.10657
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0906 Electrical and Electronic Engineering, 1302 Curriculum and Pedagogy, 1303 Specialist Studies in Education
dc.subject.classification	Education
dc.subject.classification	3903 Education systems
dc.subject.classification	3904 Specialist studies in education
dc.title	Analysing AI utilisation in education through learner question types: A constructivist approach
dc.type	Journal Article
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1302 Curriculum and Pedagogy
utslib.for	1303 Specialist Studies in Education
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Professional Practice and Leadership
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.date.updated	2026-02-28T19:10:56Z
pubs.publication-status	Published online

Abstract:

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.

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