Inquiry-Based Learning Patterns in Large Language Model-Driven Learning Environments: An Exploratory Study From Bloom's Perspective

Synthesis: This exploratory study of students interacting with LLMs during an inquiry-based learning data-science writing task used Bloom's taxonomy to analyze behavioral patterns across cognitive levels. Thematic analysis of 117 interview transcripts, 370 interaction records, and 1,694 minutes of screen recordings identified 14 interaction patterns at different levels of prior knowledge. The study highlights how Self Efficacy and metacognitive monitoring shape LLM use in IBL and calls for guiding planning frameworks and Scaffolding to mitigate AI reliance.

Key Findings

1. 14 interaction patterns across Bloom's cognitive levels. Students interacted with LLMs in 14 distinct patterns, varying by level of prior knowledge.
2. Prior knowledge shapes interaction. Students at different prior-knowledge levels used LLMs differently during the IBL process, affecting how they progressed through cognitive stages.
3. Self-efficacy and metacognitive monitoring matter. These factors significantly influenced learning behavior in the LLM-driven environment — and their absence correlates with reliance on AI.
4. Design implication: scaffolding for higher-order thinking. Educators should design cognitive scaffolding targeting specific higher-order thinking stages; instructional designers should build planning frameworks that mitigate over-reliance while fostering metacognitive monitoring.
5. Policy: critical evaluation training. Policymakers should implement training to enhance students' critical evaluation skills within LLM-driven environments.

Implications

This study is a core piece of the wiki's Inquiry Based Learning cluster, directly examining how students use LLMs across the cognitive levels of inquiry. The finding that students' prior knowledge and metacognitive skills determine whether LLM use deepens or bypasses inquiry reinforces the wiki's central design principle: AI in IBL requires explicit Scaffolding, planning frameworks, and critical evaluation training to convert interaction into higher-order learning rather than over-reliance. It complements the IBL cluster's outcome evidence (Mujib AI Ibl Creative Math 2026, Zhao GenAI Higher Order Thinking Meta 2026).

Connected Concepts

• Inquiry Based Learning
• LLM
• Critical Thinking
• Metacognition
• Self Efficacy
• Self Regulated Learning
• Scaffolding
• Higher Ed
• Cognitive Offloading

Connected Articles

• Mujib AI Ibl Creative Math 2026 — AI-supported IBL and creative mathematical performance
• Zhao GenAI Higher Order Thinking Meta 2026 — GenAI and higher-order thinking meta-analysis
• Jiang Chatgpt Inquiry Steam Review 2026 — ChatGPT for inquiry-based learning in STEAM
• Liu Tool Tutor Crutch Programming 2026 — Tool, tutor, or crutch: cognitive scaffolding vs offloading
• Dai Chatbots Problem Posing Primary 2026 — GenAI chatbots and problem posing in primary science

Citation

Luo, Y. T., Liu, T., Pang, P., McKay, D., Chang, S., & Buchanan, G. (2026). Inquiry-based learning patterns in large language model-driven learning environments: an exploratory study from Bloom's perspective. Australasian Journal of Educational Technology, 42(2), 38–57.