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Harnessing AI with the Danielson Framework: Redefining Teacher Growth

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W110A

Interactive Session
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Session description

Discover how AI can enhance teacher professional growth when grounded in the Danielson Framework for Teaching. This interactive session explores agile, efficient, and responsive approaches that address today’s leadership complexities. Attendees will engage in hands-on activities modeling how AI supports reflection, feedback, and instructional excellence.

Outline

0–10 minutes: Welcome and framing of challenges facing K-12 leaders (VUCA context, professional growth needs). Audience polling to surface shared experiences; 10–25 minutes: Interactive presentation on the Danielson Framework as a foundation for teacher growth. Participants engage in a peer activity aligning FFT domains with leadership challenges; 25–40 minutes: Live exploration of AI-enhanced tools (case studies, simulations, or sample scenarios). Small-group discussions on applications for observation, feedback, and coaching; 40–55 minutes: Collaborative design: participants co-create an AI-supported professional growth strategy (artifact or action plan); 55–60 minutes: Reflection and synthesis. Participants share key takeaways and commit to next steps.

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Outcomes

Apply the Danielson Framework for Teaching to design AI-enhanced approaches to professional growth; Evaluate the ethical and practical opportunities of AI to ensure equity, transparency, and responsiveness in teacher development; Create an initial action plan for leveraging AI in coaching, feedback, and leadership practices that promote instructional excellence.

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Supporting research

Danielson, C. (2024). Enhancing Professional Practice: A Framework for Teaching (3rd ed.). ASCD; Darling-Hammond, L., Hyler, M. E., & Gardner, M. (2017). Effective Teacher Professional Development. Learning Policy Institute; Trust, T., & Whalen, J. (2021). K-12 teachers’ experiences and perspectives on AI in education. Journal of Research on Technology in Education; Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning; Fullan, M., Quinn, J., & McEachen, J. (2018). Deep Learning: Engage the World, Change the World. Corwin; U.S. Department of Education, Office of Educational Technology. (2023). Artificial Intelligence and the Future of Teaching and Learning; Hattie, J. (2024). Visible Learning: The Sequel. Routledge; TNTP. (2018). The Opportunity Myth; Southwick, S., & Charney, D. (2018). Resilience: The Science of Mastering Life’s Greatest Challenges. Cambridge University Press; ISTE Standards (2017). ISTE Standards for Educators and Education Leaders.

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Presenters

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Director of General Education
Huron Intermediate School District
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Assistant Director
The Danielson Group
ISTE & ASCD Book Author
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Implementation Consultant
Sibme

Session specifications

Topic:

Artificial Intelligence

Grade level:

PK-12

Audience:

District-Level Leadership, School Level Leadership, Teacher Development

Attendee devices:

Devices useful

Attendee device specification:

Smartphone: Android, iOS, Windows
Laptop: Chromebook, Mac, PC
Tablet: Android, iOS, Windows

Participant accounts, software and other materials:

Google drive to access resources; web platform to view demonstrations.

ISTE Standards:

For Coaches: Collaborator
For Education Leaders: Empowering Leader