Your Name and Title:
Dr. Marvin Starominski-Uehara, Adjunct Assistant Professor
School, Library, or Organization Name:
International Business & Environmental Studies at Temple University Japan
Area of the World from Which You Will Present:
Japan
Language in Which You Will Present:
English
Target Audience(s):
Faculty teaching in high school and college
Short Session Description (one line):
Techniques to build collective knowledge with chatbots
Full Session Description (as long as you would like):
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’?
Websites / URLs Associated with Your 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).
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