Recorded SessionWelcome and Overview
Introduce the session’s purpose and presenters, framing AI as a thought partner that enhances creativity, collaboration, and provides an opportunity for deeper reflection.
Understanding AI in Education
Explore what presenter is seeing around the globe in AI integration. This will include stories from New Zealand and Dubai.
Student Examples and Deconstruction
Review authentic student work created with AI. In small groups, attendees analyze examples to identify how AI supports metacognition, collaboration, and instant feedback.
Fostering Collaboration and Tailoring Learning
Share strategies and tools that help students co-create with AI, personalize learning, and strengthen equity and inclusion.
Research-Connected Dialogue and Q&A
Discuss challenges and opportunities in implementation. Participants share insights, grounded in evidence and experience, about human-AI collaboration in classrooms.
Engagement Frequency and Tactics
Every 5–10 minutes: Interaction through polls, group discussions, or live deconstructions.
Hands-on learning: Real AI tool exploration and student work analysis.
Collaborative reflection: Shared digital spaces for crowd-sourced insights and commitments.
Attendees will leave with a solid ideas for using AI in the classroom, outlining how to integrate AI as a thought partner in their teaching practice. Through guided exploration, they’ll look at prompts, feedback routines, and reflection strategies tailored to an AI-infused classroom. They will leave with practical, ready-to-implement ideas that elevate student thinking and achievement.
John Hattie & Helen Timperley “The Power of Feedback Revisited: A Meta-Analysis of Educational Feedback”
A foundational meta-analysis that synthesizes evidence on how effective feedback affects learning.
Ajogbeje & Alonge et al. —“Enhancing Classroom Learning Outcomes: The Power of Immediate Feedback Strategy”
This quasi-experimental study shows that immediate feedback significantly improves students’ learning outcomes in STEM subjects.
Zhang, Gao, Cukurova, Nazaretsky, Suraworachet “Evaluating Trust in AI, Human, and Co-produced Feedback Among Undergraduate Students”
Investigates how students perceive and trust AI-generated, human, and hybrid feedback.
“Using LLMs to bring evidence-based feedback into the classroom”
A recent empirical study showing that large language model (LLM)-generated feedback improves revision performance and motivation.
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