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Problem-Based Learning

● high · created 2026-08-20 · updated 2026-08-20 · active-learning, collaborative-learning, scaffolding, generative-ai, critical-thinking, higher-ed, medical-education, engineering-education

Problem-based learning (PBL) — a learner-centered pedagogy in which students acquire knowledge and skills by working to understand and resolve a realistic, often ill-structured problem, with a facilitator guiding inquiry rather than delivering instruction. In the AI era, PBL's structural features — problem-driven inquiry, collaborative knowledge construction, facilitation over instruction, and metacognitive reflection — have emerged as the same conditions under which generative AI integration becomes educationally productive rather than substitutive.

PBL originated in medical education (McMaster University, 1960s) and has since spread across health professions, engineering, and K-12. The learner takes responsibility for diagnosing learning needs, identifying resources, and constructing solutions, while the facilitator scaffolds the process. The distinctive claim of PBL is that the problem is the curriculum driver — not an illustration of content taught elsewhere but the context in which content is learned.

PBL in the AI era

The wiki's research shows that PBL has become a focal point for thinking about productive AI integration.

  • PBL's structural conditions make AI integration productive. Rowe (2026) argues that PBL's core features — problem-driven inquiry, collaborative construction, facilitation, metacognitive reflection — are exactly the conditions under which AI functions as a partner in professional development rather than a shortcut around it. The alignment is structural, not retrospective: PBL was designed around these conditions before AI existed, rooted in the recognition that professional competence requires adaptive judgement rather than routine execution.
  • AI raises the ceiling on problem complexity. The same argument holds that AI expands what category of problem PBL can engage, making "wicked problems" and complex real-world cases accessible to students who previously could not reach them.
  • The artefact-as-proxy problem. AI severs the link between a submitted artefact and the engagement that produced it — a problem PBL is well positioned to address because it assesses the process and demonstrated understanding, not just the product. This connects PBL to authentic and process-based assessment and to the wiki's over-reliance literature.
  • ChatGPT as adaptive scaffolding. La Sunra et al. (2026) show ChatGPT integrated as adaptive scaffolding within an AI-enhanced PBL framework to improve critical thinking and personalized learning in K-12 (120 eighth graders). This treats AI as a within-PBL support rather than an answer machine.
  • Responsible-use frameworks. Uden & Hwang (2026) advance the neuroscience-informed LEARN framework (Lifelong Learning, Engagement, Active Processing, Reflection, Neuro-based Design) for ethically grounding generative AI use within PBL, countering cognitive offloading and integrity erosion.
  • Domain implementations. PBL frameworks for biomedical engineering use GenAI for summarization and coding support with replication packages (Nnamdi et al. 2026); AI-supported PBL enhances computational thinking in robotics (AI-supported PBL for computational thinking); and genAI-enabled virtual patients support medical history-taking tutorials (Mool et al. 2026).
  • Evidence synthesis. Amdan et al. (2026) systematically review 50 studies (2015–2024) on educators' engagement with AI in PBL within a human-computer interaction framework.

PBL vs. related pedagogies

PBL is closely related to project-based learning — both are learner-centered and problem/context-driven — but PBL centers on an ill-structured problem whose solution requires inquiry and knowledge construction, whereas project-based learning centers on producing a tangible artefact or project. PBL is also kin to case-based and inquiry-based learning, which share the problem-driven, facilitation-led structure. In the wiki's mapping, the structural argument for productive AI integration applies to any pedagogy sharing these features, not only PBL.

Why PBL matters for AI integration

Because PBL foregrounds process, collaboration, and demonstrated understanding over artefact production, it is a natural home for responsible AI use: students learn with AI as a partner rather than from AI as a substitute. The facilitator's role and the design of the problem become the levers that determine whether AI supports learning or displaces it — the same human oversight and assessment-design concerns that recur across the wiki.

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