This video presents a session on empowering students with generative AI, specifically large language models (LLMs) or chatbots, for enhanced learning. The speaker discusses student perspectives on the risks and benefits of using these tools, and shares two techniques—reverse engineering and collective excitation—for incorporating LLMs into online classes.
Student Concerns about LLMs: Students are aware of potential negative impacts on cognitive development, independent thinking, and communication skills. They worry about fairness in assessment and the unequal access to these tools among classmates. Students also express concerns about potential laziness and procrastination resulting from over-reliance on AI.
Reverse Engineering LLMs for Learning: This technique involves guiding students to craft effective prompts, analyze LLM outputs for keywords, and use these keywords to deepen their understanding of concepts. The process aims to personalize learning and enhance critical thinking by breaking down and analyzing the LLM’s responses.
Collective Excitation for Collaborative Learning: This approach simulates Wikipedia's collaborative model in the classroom. Students collectively answer a question using Google Docs, fostering collaboration and building collective intelligence. Constructive feedback from the instructor is key to the success of this method.
Instructor's Role: The instructor's role is crucial in guiding students on responsible and ethical LLM use, addressing their concerns, and adapting teaching methods to leverage the technology's benefits while mitigating its risks. The instructor needs to listen to student feedback and adapt teaching strategies accordingly.
Embrace, Don't Fear: The presentation advocates for embracing the opportunities presented by generative AI while acknowledging its risks. It highlights that the technology is rapidly evolving and requires a flexible and adaptable approach to education.
