Please use this identifier to cite or link to this item: https://repository.isls.org//handle/1/10070
Title: The Effects of AI Feedback on Students’ Epistemic Emotion and Performance in Engineering Design: An Exploratory Study
Authors: Zheng, Juan
Jiang, Rundong
Li, Shan
Zhu, Jiayan
Xie, Charles
Keywords: Learning Sciences
Issue Date: 2023
Publisher: International Society of the Learning Sciences
Citation: Zheng, J., Jiang, R., Li, S., Zhu, J., & Xie, C. (2023). The effects of AI feedback on students’ epistemic emotion and performance in engineering design: An exploratory study. In Blikstein, P., Van Aalst, J., Kizito, R., & Brennan, K. (Eds.), Proceedings of the 17th International Conference of the Learning Sciences - ICLS 2023 (pp. 1885-1886). International Society of the Learning Sciences.
Abstract: One of the affordances of AI (Artificial Intelligence) for professionals is that AI can explore a much wider solution space to arrive at creative solutions that surprise them. This study explored the epistemic emotion of surprise and its effect on students’ performance as they used AI feedback to assist their engineering design. Specifically, we examined 43 high school students’ emotional reactions and their changes in design solutions after receiving AI feedback. Multinominal regression was performed to find that AI feedback did not have a significant influence on the level of surprise students experienced. However, most students made more positive changes to their designs when they found that AI feedback was much better than their original solutions. This study suggests that integrating AI elements in engineering design could help students optimize their designs.
Description: Poster
URI: https://doi.org/10.22318/icls2023.151549
https://repository.isls.org//handle/1/10070
Appears in Collections:ISLS Annual Meeting 2023

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