AI Ed Wiki logoAI Ed Wiki
Use with AI

🏷️ Concept

Inquiry-Based Learning and AI

● high · created 2026-08-22 · updated 2026-08-22 · inquiry-based-learning, problem-based-learning, active-learning, critical-thinking, metacognition, self-regulated-learning, generative-ai, stem-education, scaffolding

Inquiry-based learning (IBL) — a learner-centered pedagogy in which students develop understanding by posing questions, exploring independently, and constructing knowledge through a cycle of inquiry, reflection, and revision, with the instructor scaffolding rather than lecturing. In the AI era, IBL's question-driven, exploration-centred structure has become a focal point: generative AI and LLM tools can serve as interactive "co-inquirers" that support questioning and investigation — but only when designed to preserve rather than bypass the cognitive work of inquiry.

Inquiry-based learning centers on student-driven questions and the inquiry process itself, typically moving through phases (orientation → conceptualization → investigation → discussion → conclusion). It is the broader family under which problem-based learning and project-based learning are often nested: IBL emphasizes the questioning and discovery process, PBL the ill-structured problem, and project-based learning the tangible artefact.

How inquiry-based learning appears in the wiki

AI as co-inquirer. A systematic review of ChatGPT for inquiry-based learning in STEAM (Jiang Chatgpt Inquiry Steam Review 2026, 24 studies) found ChatGPT used mainly in the conceptualization, investigation, and discussion phases — as learning tool, tutor, learning peer, domain expert, and teaching assistant — improving performance, critical thinking, engagement, and motivation, while posing risks of over-reliance, hallucination, and superficial conclusions when output is treated as authoritative.

Problem posing as the starting point. A quasi-experiment with 97 third-graders (Dai Chatbots Problem Posing Primary 2026) showed GenAI chatbots significantly outperformed search engines for science problem posing, improving question quality and producing more integrated epistemic networks while lowering cognitive load.

Cognitive-level patterns in LLM-driven IBL. An exploratory study of 117 interview transcripts and interaction records (Luo et al.) identified 14 interaction patterns across Bloom's cognitive levels, showing how students' prior knowledge shapes LLM use and highlighting the need for scaffolding that targets higher-order thinking stages and mitigates over-reliance.

Outcomes evidence is mixed. An AI-supported IBL experiment in mathematics (Mujib et al.) improved creative mathematical performance and attitudes but not critical problem-solving skills — suggesting AI-IBL mainly supports creativity and affective development. A meta-analysis of 29 experiments (Zhao et al.) found GenAI has a moderate positive effect on higher-order thinking, strongest for problem-solving and with 8–16 week interventions and higher self-regulated-learning learners benefiting most.

Scaling IBL with AI as a metacognitive coach. A conceptual framework (Doyle & Swisher) uses AI as a Scaffolding layer that transitions "from an output generator to a metacognitive coach," mapping AI interactions onto IBL's stages to scale scaffolding to large cohorts while shifting assessment from content mastery to complex-thinking skills.

Equity and context. A conceptual framework (Botes et al.) bridges generative-AI co-design with open educational practices to support inquiry-led STEM teaching in under-resourced contexts, using AI-generated, multilingual, contextually relevant simulations.

Why inquiry-based learning matters for AI integration

IBL's question-driven, process-focused structure is the natural home for productive AI use: students learn with AI as a partner rather than from it as a substitute. The wiki's evidence converges on a core tension — AI can lower the friction of information retrieval and question formulation (reducing cognitive load and enabling deeper reflection), but without design scaffolding it risks over-reliance and bypassing higher-order cognition. The facilitator's role, the design of scaffolds, and the explicit teaching of evaluation skills are the levers determining whether AI deepens or displaces inquiry.

Connected Concepts

Connected Articles