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Related Experiment Video

Updated: Apr 14, 2026

Interactive and Visualized Online Experimentation System for Engineering Education and Research
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Innovating instructional design through generative AI prompt engineering for health professions educators.

Taralyn Tan1,2, Krisztina Fischer3,4

  • 1Office for Graduate Education, Harvard Medical School, Boston, MA, United States.

Academic Medicine : Journal of the Association of American Medical Colleges
|March 2, 2026
PubMed
Summary

Medical educators improved generative AI (GAI) prompt quality for instructional design after a training intervention. This pilot study shows GAI can enhance learning and support evidence-based teaching practices.

Keywords:
faculty developmentgenerative AIinstructional designmetacognitionprompt engineering

Related Experiment Videos

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Area of Science:

  • Medical Education
  • Artificial Intelligence in Education
  • Instructional Design

Background:

  • Theory-informed and evidence-based educational offerings are crucial but time-consuming for health professions educators.
  • Generative AI (GAI) presents an opportunity to streamline instructional design, but educators need training to use it effectively.
  • An experiential learning activity was developed to train educators in leveraging GAI for evidence-based instructional design.

Purpose of the Study:

  • To pilot and evaluate a 2-part experiential learning activity designed to train medical educators in using generative AI for instructional design.
  • To assess the effectiveness of the intervention in improving educators' ability to engage with GAI tools for creating inclusive and evidence-based educational content.
  • To explore educators' experiences and planned behavioral changes regarding GAI integration in their teaching.

Main Methods:

  • The intervention was implemented in a graduate-level course at Harvard Medical School with 27 educators.
  • Educators used GAI to annotate lesson plans, focusing on evidence-based teaching principles and prompt engineering.
  • Evaluation used the Kirkpatrick Model, assessing subjective experience (Level 1), learning via prompt quality (Level 2), and planned behavioral changes (Level 3).

Main Results:

  • Prompt quality significantly improved post-instruction (mean 1.4 to 4.0, P < .0001).
  • Educators reported enhanced learning and identified specific actions to integrate GAI feedback into their instructional design.
  • The intervention demonstrated a 62% completion rate among participants.

Conclusions:

  • The pilot intervention effectively improved medical educators' generative AI prompt engineering skills for instructional design.
  • Educators found the AI-assisted assignments beneficial for their learning and planned to implement GAI in their practice.
  • Future work includes developing a scalable, interactive GAI tool for faculty development in AI literacy and instructional design.