Inquiry-based learning patterns in large language model-driven learning environments: An exploratory study from Bloom's perspective.
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| Title: | Inquiry-based learning patterns in large language model-driven learning environments: An exploratory study from Bloom's perspective. |
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| Authors: | Luo, Yiming Taclis1, Liu, Ting1, Pang, Patrick1, McKay, Dana2, Chang, Shanton3, Buchanan, George2 |
| Source: | Australasian Journal of Educational Technology. 2026, Vol. 42 Issue 1, p38-57. 20p. |
| Subject Terms: | *Inquiry-based learning, *Student engagement, *Academic support programs, *Cognition, *Metacognition, *Bloom's taxonomy, Language models, Self-efficacy |
| Abstract: | Inquiry-based learning (IBL) is a problem-driven and exploration-centred learning method. The emergence of large language models (LLMs) such as ChatGPT provides a new interactive environment for IBL. However, research has not sufficiently explored how students interact with LLMs for IBL. This study aimed to understand students' behaviours interacting with LLM at different cognitive levels during the IBL process. We conducted an experiment on a data science academic writing task and used Bloom's educational taxonomy to examine the behavioural patterns of students' IBL at different cognitive stages. Through the exploratory thematic analysis of 117 interview transcripts, 370 interaction records and 1,694 minutes of screen recordings, we identified 14 interaction patterns among students at different levels of prior knowledge. This article discusses the potential impact of self-efficacy and metacognitive monitoring on students' learning behaviour in an LLM-driven learning environment and called for the design of a guiding planning framework and scaffolding to address challenges such as reliance on artificial intelligence. Our study provides new insights for the development of IBL in the era of emerging artificial intelligence technologies. Implications for practice or policy: • Educators can improve student inquiry-based learning outcomes by designing cognitive scaffolding that targets specific higher-order thinking stages. • Instructional designers should develop planning frameworks that mitigate overreliance on artificial intelligence while fostering student metacognitive monitoring. • Policymakers could implement training programmes to enhance students' critical evaluation skills within an LLM-driven environment. [ABSTRACT FROM AUTHOR] |
| Copyright of Australasian Journal of Educational Technology is the property of Australasian Journal of Educational Technology (AJET) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 192571640 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Inquiry-based learning patterns in large language model-driven learning environments: An exploratory study from Bloom's perspective. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Luo%2C+Yiming+Taclis%22">Luo, Yiming Taclis</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Liu%2C+Ting%22">Liu, Ting</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Pang%2C+Patrick%22">Pang, Patrick</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22McKay%2C+Dana%22">McKay, Dana</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Chang%2C+Shanton%22">Chang, Shanton</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Buchanan%2C+George%22">Buchanan, George</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Australasian+Journal+of+Educational+Technology%22">Australasian Journal of Educational Technology</searchLink>. 2026, Vol. 42 Issue 1, p38-57. 20p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Inquiry-based+learning%22">Inquiry-based learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Student+engagement%22">Student engagement</searchLink><br />*<searchLink fieldCode="DE" term="%22Academic+support+programs%22">Academic support programs</searchLink><br />*<searchLink fieldCode="DE" term="%22Cognition%22">Cognition</searchLink><br />*<searchLink fieldCode="DE" term="%22Metacognition%22">Metacognition</searchLink><br />*<searchLink fieldCode="DE" term="%22Bloom's+taxonomy%22">Bloom's taxonomy</searchLink><br /><searchLink fieldCode="DE" term="%22Language+models%22">Language models</searchLink><br /><searchLink fieldCode="DE" term="%22Self-efficacy%22">Self-efficacy</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Inquiry-based learning (IBL) is a problem-driven and exploration-centred learning method. The emergence of large language models (LLMs) such as ChatGPT provides a new interactive environment for IBL. However, research has not sufficiently explored how students interact with LLMs for IBL. This study aimed to understand students' behaviours interacting with LLM at different cognitive levels during the IBL process. We conducted an experiment on a data science academic writing task and used Bloom's educational taxonomy to examine the behavioural patterns of students' IBL at different cognitive stages. Through the exploratory thematic analysis of 117 interview transcripts, 370 interaction records and 1,694 minutes of screen recordings, we identified 14 interaction patterns among students at different levels of prior knowledge. This article discusses the potential impact of self-efficacy and metacognitive monitoring on students' learning behaviour in an LLM-driven learning environment and called for the design of a guiding planning framework and scaffolding to address challenges such as reliance on artificial intelligence. Our study provides new insights for the development of IBL in the era of emerging artificial intelligence technologies. Implications for practice or policy: • Educators can improve student inquiry-based learning outcomes by designing cognitive scaffolding that targets specific higher-order thinking stages. • Instructional designers should develop planning frameworks that mitigate overreliance on artificial intelligence while fostering student metacognitive monitoring. • Policymakers could implement training programmes to enhance students' critical evaluation skills within an LLM-driven environment. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Australasian Journal of Educational Technology is the property of Australasian Journal of Educational Technology (AJET) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 38 Subjects: – SubjectFull: Inquiry-based learning Type: general – SubjectFull: Student engagement Type: general – SubjectFull: Academic support programs Type: general – SubjectFull: Cognition Type: general – SubjectFull: Metacognition Type: general – SubjectFull: Bloom's taxonomy Type: general – SubjectFull: Language models Type: general – SubjectFull: Self-efficacy Type: general Titles: – TitleFull: Inquiry-based learning patterns in large language model-driven learning environments: An exploratory study from Bloom's perspective. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Luo, Yiming Taclis – PersonEntity: Name: NameFull: Liu, Ting – PersonEntity: Name: NameFull: Pang, Patrick – PersonEntity: Name: NameFull: McKay, Dana – PersonEntity: Name: NameFull: Chang, Shanton – PersonEntity: Name: NameFull: Buchanan, George IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 14493098 Numbering: – Type: volume Value: 42 – Type: issue Value: 1 Titles: – TitleFull: Australasian Journal of Educational Technology Type: main |
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