Blended ContentIntroduction and Session Overview (5 minutes)
Content:
Welcome participants and introduce the session's objectives.
Provide an overview of TinyLlama’s fine-tuning for student behaviors and the relevance of AI in educational simulations.
Engagement:
Begin with a thought-provoking question: "How can AI simulate real-life student behavior in the classroom?"
Process:
Use an interactive poll to gather participant input and frame the session’s focus.
Encourage participants to share expectations or past experiences with AI in education through the chat.
2. Understanding the TinyLlama Model and Behavior Simulation (10 minutes)
Content:
Present the technical foundation of the TinyLlama language model, emphasizing its fine-tuning to simulate specific student behaviors: Focused, Confused, and Distracted.
Discuss how these simulations can be applied in educational settings.
Engagement:
Show a live demonstration of the model simulating different behaviors.
Process:
After the demonstration, conduct a brief Q&A where participants ask about the model’s functioning and implementation.
3. Application in Educational Simulations (10 minutes)
Content:
Explain how the fine-tuned model can enhance teacher-student interactions by simulating diverse behaviors for practice.
Explore case studies and examples of AI in educational simulations.
Engagement:
Display a scenario-based video showing teacher-student interaction using the AI model.
Process:
Facilitate a guided discussion on how AI-based simulations can be used in participants' classrooms, with peer-to-peer idea sharing.
4. Challenges and Solutions in AI-Based Educational Tools (10 minutes)
Content:
Address the challenges encountered during the fine-tuning process, including limitations of synthetic datasets and the difficulty of modeling nuanced behaviors.
Offer potential solutions for overcoming these challenges.
Engagement:
Encourage participants to brainstorm challenges they foresee when implementing AI-driven simulations in their settings.
Process:
Use a digital collaboration tool (e.g., Miro or Google Jamboard) where participants can contribute challenges and proposed solutions, fostering a collaborative atmosphere.
5. Hands-On Activity: Designing an Educational Scenario with AI (15 minutes)
Content:
Guide participants through the process of designing an educational scenario where they can apply the TinyLlama model to simulate student behaviors.
Engagement:
Participants work in small groups (via breakout rooms if virtual) to design a lesson plan or scenario that integrates AI to manage student behaviors.
Process:
Each group presents their scenario, explaining how they would use AI to enhance engagement and address student needs.
Provide feedback and suggestions to refine their scenarios.
6. Evaluating AI’s Impact on Student Learning and Engagement (7 minutes)
Content:
Present key findings from the model's performance, including the 80.95% accuracy rate in generating appropriate behaviors and the potential for future applications.
Discuss how AI-driven simulations can be assessed for their effectiveness in enhancing student learning and engagement.
Engagement:
Share data visualizations showing the model’s impact on simulated teacher-student interactions.
Process:
Conduct a brief reflective activity where participants rate their confidence in using AI-driven tools to improve classroom engagement, using a polling tool to gather responses.
7. Conclusion and Next Steps (3 minutes)
Content:
Summarize the session’s key takeaways and reiterate how AI-based simulations can be implemented in educational contexts.
Provide additional resources and recommendations for continued learning.
Engagement:
Ask participants to share their primary takeaway from the session in the chat.
Process:
Distribute a post-session feedback form and offer contact information for further inquiries.
Luckin, R. (2017). "Towards Artificial Intelligence-Based Assessment Systems." Nature Human Behaviour, 1, 0028.
Cameron, D. (2019). "AI in Education: Solving Problems with Artificial Intelligence." Educause Review.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign.
Johnson, L., Adams Becker, S., Estrada, V., & Freeman, A. (2015). "NMC Horizon Report: 2015 K-12 Edition." New Media Consortium.
Woolf, B. P. (2010). Building Intelligent Interactive Tutors: Student-Centered Strategies for Revolutionizing E-Learning. Morgan Kaufmann.
West, D. M. (2018). "How Artificial Intelligence Is Changing Teaching." Brookings Institute.
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