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Inquiry-Based Learning

created 2026-08-22 · updated 2026-09-30

Inquiry-based learning (IBL) — a learner-centered Pedagogies and Teaching Strategies 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-centered structure has become a focal point: generative AI and Large Language Models (LLMs) 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.

Questions to Consider

  • Inquiry-based learning centers on student-driven questions and the inquiry process itself. How is learning driven by your own question different from learning driven by someone else's?
  • The page asks whether AI serves as a 'co-inquirer' supporting your investigation or as a substitute that bypasses the cognitive work. When you use AI to explore a question, who is doing the inquiring?
  • Evidence on AI-supported inquiry is mixed: it improved creative performance and attitudes but not critical Problem Solving in one study, and deepened conceptual understanding without boosting AI literacy in another. What does that suggest about what AI alone can and cannot develop?
  • If treating AI output as authoritative risks over-reliance and superficial conclusions, how would you teach students to evaluate AI-generated answers as part of the inquiry cycle?
  • The page connects inquiry to productive failure — learners exploring before instruction, so struggle itself prepares learning. How might AI that withholds answers and elicits multiple attempts support that struggle rather than remove it?
  • What is the facilitator's role when AI lowers the friction of asking questions? If students can get answers instantly, what does the instructor now need to scaffold that they didn't before?

Introduction

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 artifact.

How inquiry-based learning appears in the knowledge base

AI as co-inquirer. A systematic review of ChatGPT for inquiry-based learning in STEAM (The AI-Powered Co-inquirer: A Systematic Review of ChatGPT for Inquiry-Based Learning in STEAM Education, 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 (Inquiry-Based Learning in STEM Education: The Impact of Generative AI-Based Chatbots on Primary School Students' Problem Posing Ability in Science) 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. A quasi-experiment with 48 pre-service science teachers in Türkiye (Aydın) using an 8-week AI-supported guided inquiry program (integrating problem- and design-based learning) found significant group-by-time gains in conceptual understanding of photosynthesis and cellular respiration, but no significant effect on AI literacy or self-perceived computational thinking — evidence that AI-IBL can deepen domain understanding while the development of AI/CT competencies requires more explicit, targeted design.

Gousopoulos (2026) isolated the AI contribution from the inquiry itself in a three-group climate study: AI-assisted inquiry outgained inquiry-only peers on decision-making (d = 0.69), with the largest gains in monitoring and adaptive management (d = 1.37), while basic data collection improved equally across all conditions.

Equity and context. A conceptual framework 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.

AI-scaffolded evidence comparison. An and colleagues (2026) show how an AI-powered information-extraction system shifts the undergraduate literature review from summarization toward evidence organization and comparison — students moving from isolated reading to cross-study comparison and evidence-based justification in support of research-based learning and thesis completion. This concretizes how AI can scaffold inquiry processes in STEM undergraduate research.

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 knowledge base'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.

Productive failure and inquiry

Productive failure (PF) is the most structured cousin of inquiry-based learning: learners explore problems and generate solutions before direct instruction, then consolidate. Both share the premise that learner-generated attempts (even failed ones) activate prior knowledge and prepare learners to learn from instruction. The AI-era PF research sharpens how AI should scaffold inquiry without short-circuiting it: Kim et al. (2026) derive AI design principles for preserving struggle through problem exploration and solution generation; Puech et al. (2025) show LLM tutors can be steered to withhold answers and elicit multiple attempts; clue-before-correction tasks exemplify clue-based (vs. direct) scaffolding that keeps learners doing the reasoning. These connect inquiry and PF to the broader imperative that AI must not remove the productive struggle through which durable learning forms.

Connected Concepts

Connected Articles

Metadata

DisciplineStem Education
Learning and instructionActive Learning, Inquiry-Based Learning, Metacognition, Problem-Based Learning, Scaffolding, Self-Regulated Learning
Technologies and techniquesGenerative AI
Foundations of AI in educationCritical Thinking
SourceHistoryEditChat

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