Powerful chatbots can help students learn faster, better, and have more fun (Cooper 2023; Alier et al. 2024; Pesovski et al. 2024). Some even argue that these tools are ‘skill levelers’ in a sense that now ‘everyone is above average’ (Mollick 2023). This session shares online educational practices that test the premises of this claim. Reverse engineering (Zhong & Li 2024) and collective excitation (Zheng et al. 2023) are two opposing, but complementary, methods that empower students to engage in an interactive process of building collective knowledge while further developing their analytical and critical skills by paying close attention to causality, assumptions, structures, and patterns on AI-generated outputs.
The guiding question of this session is:
‘How do reverse engineering and collective excitation help high school and college students leverage chatbots for the design of tailor-made educational experiences?’
Learning outcomes:
. What are the assumptions of chatbots as a ‘skill leveler’ in education?
. How does ‘reverse engineering’ help students enhance their critical thinking skills?
. What is ‘collective excitation’ and what role it plays in creating valuable ‘collective knowledge’?
URLs associated with this session:
https://www.youtube.com/watch?v=rXfDH3gMy-g
https://www.youtube.com/watch?v=Rf96bDPGNYQ
References
Alier, M., García-Peñalvo, F., & Camba, J. D. (2024). Generative Artificial Intelligence in Education: From Deceptive to Disruptive.
Cooper, G. (2023). Examining science education in ChatGPT: An exploratory study of generative artificial intelligence. Journal of Science Education and Technology, 32(3), 444-452.
Mollick, E. (2023, September 24). Everyone is above average. Retrieved from https://www.oneusefulthing.org/p/everyone-is-above-average
Pesovski, I., Santos, R., Henriques, R., & Trajkovik, V. (2024). Generative AI for Customizable Learning Experiences. Sustainability, 16(7), 3034.
Zheng, L., Mai, F., Yan, B., & Nickerson, J. V. (2023). Stigmergy in Open Collaboration: An Empirical Investigation Based on Wikipedia. Journal of Management Information Systems, 40(3), 983-1008.
Zhong, B., Liu, X., & Li, X. (2024). Effects of reverse engineering pedagogy on students’ learning performance in STEM education: The bridge-design project as an example. Heliyon, 10(2).
AI Summary: 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.
Key Takeaways
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.