Adopt AI-PBL frameworks with institutional and policy-level support and teacher training investment¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The study calls for institutional and policy-level adoption of AI-PBL frameworks, investments in teacher training, and further research to ensure scalability. The authors argue that AI-supported pedagogy is essential for equitable skill acquisition and preparing students for the world of work, particularly in under-resourced contexts where access to expert facilitators and robotics kits may be limited. The strategy positions AI tools as scalable, context-aware scaffolding embedded in inquiry-driven instruction.

Design Implications¶

Context¶

Requirements¶

• Institutional and policy-level adoption of AI-PBL frameworks
• Investments in teacher training

Constraints¶

• Teacher preparedness and infrastructure gaps may hinder equitable implementation in resource-constrained areas

Target Learners¶

• University students in STEM courses in developing-country contexts

Target Learning Goals¶

• Computational thinking
• Problem-solving capabilities for future workforce needs

Affordances¶

• Ai Pbl More Capable Peers Zpd Framework

Related Strategies¶

• Accrediting bodies and professional organizations should provide AI policy frameworks integrating academic integrity, data ethics, and equitable access, and programs should invest in faculty development
• Implement AI-supported STEM instruction through sustained, system-level integration of one to two months or more rather than isolated short interventions

Examples¶

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Key Sources¶

• Ayanwale, M. A., & Omeh, C. B. (2026). AI-supported problem-based learning for enhancing computational thinking skills in STEM education. Computers in Human Behavior: Artificial Humans 7 (2026) 100263. https://doi.org/10.1016/j.chbah.2026.100263