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Manuscript Format

Teaching and Learning Resource

Time required for implementation of resource

> 50 minutes

Subdiscipline of Kinesiology

Sport Sociology

Abstract

Educating physical education teachers to deliver effective online and hybrid instruction presents unique challenges. Instructors cannot observe preservice teachers live in a gymnasium, and real-time feedback is impossible. When dozens of students submit micro-teaching videos, the traditional feedback cycle breaks down. This teaching and learning resource presents a practical, scalable model to address these constraints through integration of two accessible AI tools: Brisk AI for automated video analysis and custom-designed AI chatbots for guided reflection. Through this approach, preservice teachers record short lessons, receive immediate automated feedback on their teaching behaviors, engage in chatbot-guided metacognitive reflection, and revise and reteach the same lesson with measurable improvement. This iterative cycle (teach, analyze, reflect, revise) happens within weeks rather than months, accelerating instructional growth while supporting instructor workload management. The resource has been implemented with online and hybrid cohorts of 10-40 post-baccalaureate physical education teacher education (PETE) licensure candidates. Students report that immediate, specific feedback changed their teaching. Data from analysis tools show measurable improvement in clarity, pacing, demonstration, and student engagement between first and second attempts. The model is cost-effective (using free or low-cost tools), accessible (students record anywhere), and flexible (adaptable across kinesiology disciplines). This resource provides instructors with step-by-step implementation procedures, detailed student instructions, assessment rubrics, troubleshooting guidance, and honest discussion of both benefits and real-world challenges.

Corresponding Author

Nikki Hollett: hollettn@uww.edu

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