Scaling Complex Thinking: A Conceptual Framework for AI-Supported Inquiry-Based Learning

Synthesis: This conceptual framework paper integrates inquiry-based learning with AI to solve IBL's scalability problem. It positions AI as a Scaffolding layer that transitions "from an output generator to a metacognitive coach," mapping AI interactions onto IBL's stages (orientation, conceptualization, investigation, conclusion, discussion). The framework's sustainability depends on shifting assessment from content mastery to measurable complex-thinking skills — with implications for teacher training and technological governance.

Key Findings

1. IBL's scalability problem. IBL scaffolding requires instructors to continually assess and tailor support per student — time-intensive and hard to scale to large cohorts. AI is proposed as the mechanism to scale that scaffolding.
2. AI as a scaffolding layer, not an output generator. The framework maps AI interactions onto the stages of IBL, positioning AI as a metacognitive coach: in the question phase AI critique compels clarification; in Investigate/Create it prompts self-evaluation; in Discuss/Reflect it introduces conflict to challenge assumptions.
3. AI vs. teacher scaffolding. AI provides instant, in-task, individualized intervention, whereas teacher scaffolding is post-task and cohort-balanced — a timing-and-scale distinction.
4. Complex thinking as the goal. Complex thinking synthesizes critical, creative, and systematic thinking, with metacognition as its backbone — and IBL, scaffolded by AI, is positioned as the pedagogy to develop it.
5. Institutional conditions. Sustainability depends on shifting assessment from content mastery to measurable complex-thinking skills, plus teacher training and technological governance.

Implications

This paper contributes a design-oriented framework to the wiki's Inquiry Based Learning cluster: it articulates how AI should scaffold inquiry at scale while preserving metacognition — directly addressing the over-reliance risk the cluster documents. The AI-as-metacognitive-coach framing (vs. output generator) operationalizes the "co-inquirer vs. answer machine" distinction, and the call to assess complex-thinking skills (rather than content mastery) connects to Assessment and AI Education concerns. It complements the empirical and review evidence in the cluster with a scalable design vision.

Connected Concepts

• Inquiry Based Learning
• Metacognition
• Scaffolding
• Generative AI
• Critical Thinking
• AI Education
• Higher Ed
• Assessment
• Cognitive Offloading

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• Liu Tool Tutor Crutch Programming 2026 — Tool, tutor, or crutch: cognitive scaffolding vs offloading
• Botes AI Oep Ibl Underresourced 2026 — AI-supported OEP/RS and IBL in under-resourced contexts
• Mujib AI Ibl Creative Math 2026 — AI-supported IBL and creative mathematical performance

Citation

Doyle, L. B., & Swisher, J. L. (2026). Scaling complex thinking: a conceptual framework for AI-supported inquiry-based learning. Educational Point, 3(3), e183.