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 |
|---|---|
| Language: | English |
| Authors: | Yiming Taclis Luo (ORCID |
| Source: | Australasian Journal of Educational Technology. 2026 42(1):38-57. |
| Availability: | Australasian Society for Computers in Learning in Tertiary Education. Ascilite Secretariat, P.O. Box 44, Figtree, NSW, Australia. Tel: +61-8-9367-1133; e-mail: info@ascilite.org.au; Web site: https://ajet.org.au/index.php/AJET |
| Peer Reviewed: | Y |
| Page Count: | 20 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Inquiry, Active Learning, Artificial Intelligence, Educational Environment, Models, Student Behavior, Self Efficacy, Metacognition, Higher Education, Interaction, Undergraduate Students, Foreign Countries, Technology Uses in Education |
| Geographic Terms: | Macau, China |
| DOI: | 10.14742/ajet.10773 |
| ISSN: | 1449-3098 1449-5554 |
| 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. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1500106 |
| Database: | ERIC |
| 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. |
|---|---|
| ISSN: | 1449-3098 1449-5554 |
| DOI: | 10.14742/ajet.10773 |
