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1 reference
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.
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.
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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References are checked against publisher records, scholarly indexes, DOI or ISBN metadata, repositories and author records where available. An entry marked [Citation not verified] preserves the wording found in the relevant volume without attributing findings to an unconfirmed work.
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