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2 references
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- Vol 7 RE: Chapter 11
generative artificial intelligenceadaptive explanationsmisconception diagnosislow-stakes testingspaced practiceteacher oversightWorking paper Evidence summaryAt a glance
Mollick and Mollick show how generative AI can reduce the preparation burden associated with five evidence-based teaching practices: supplying varied examples and explanations, identifying and addressing misconceptions, creating frequent low-stakes tests, assessing learning, and spacing practice over time. For each strategy, they provide adaptable prompts and explain the relevant learning principle.
Key ideas
AI can quickly generate alternative explanations, analogies, diagnostic questions, practice items, rubrics, and review schedules, allowing instructors to tailor material to a topic or learner group. Yet generated content may be inaccurate, biased, superficial, or poorly calibrated, so teachers must verify outputs and retain pedagogical control. Students should also understand when and how AI is being used. The paper treats the technology as a force multiplier rather than an autonomous teacher: its value comes from making sound practices easier to implement consistently.
Why it matters
Effective use therefore begins with a learning goal and an established instructional strategy, followed by careful prompting, expert review, classroom adaptation, and observation of whether the resulting activity actually improves learning.
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- Vol 4 RE: Chapter 24
generative artificial intelligencecognitive engagementhuman-centred augmentationai literacycurriculum alignmentdigital equityReport Evidence summaryAt a glance
The 2026 Outlook synthesizes emerging evidence about generative AI in teaching, learning, assessment, guidance, and educational administration. It stresses that better task performance with a general-purpose model does not automatically produce learning: students may obtain polished answers while bypassing the cognitive effort that develops knowledge and transferable skill.
Key ideas
Educational value depends on explicit pedagogy, well-designed interaction, feedback, reflection, and alignment with curriculum. GenAI can serve as tutor, practice partner, creative collaborator, teacher assistant, and administrative aid when humans retain goals and judgment. Systems should protect foundational and independent thinking, use AI selectively, and distinguish learning without AI, learning through purpose-built educational AI, and responsible use of general tools. Recommendations include investment in research and curriculum-aligned resources, sustained professional learning, equitable access, privacy and safety protections, transparency, evaluation, and alternatives where digital divides persist.
Why it matters
The report favors human-centred augmentation: technology should enrich practice and relationships, not replace productive struggle, teacher expertise, or the social purposes of education.
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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
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