Learning Outside the Classroom During a Pandemic: Evidence from an Artificial Intelligence-Based Education App - Ko, Ga Young; Shin, Donghyuk; Auh, Seigyoung; Lee, Yeonjung; Han, Sang Pil (Management Science, 2023)
Drawing on the notion of compensatory behavior, this paper studies how students compensate for learning loss during a pandemic and what role artificial intelligence (AI) plays in this regard. We further probe into a difference in compensatory behavior for learning loss in terms of quantity, pattern, and pace (i.e., tripartite aspect of learning behavior) of AI-powered learning app usage depending on the level of pandemic threat and the proximity of a goal to students. Results show that the pandemic threat affects student learning behavior differently. Immediately following the COVID-19 outbreak, students who live in the epicenter of the outbreak (versus those who do not) use the app less at first, but with time, they use it more (quantity), on a more regular basis (pattern), and rebound to a curriculum path (pace) comparable to students who do not live in the outbreak’s epicenter. These findings collectively explain behavior that is consistent with compensation for learning loss. The results also partially corroborate the goal-proximity effect, revealing that proximity to a goal (e.g., the degree to which the national university admission exam is approaching) has a moderating role in explaining the tripartite perspective of student learning behavior. Overall, these findings have important theoretical and practical implications for understanding how innovative education technologies can not only facilitate student learning during adversity, but also support learning recovery after adversity. This paper was accepted by D. J. Wu, information systems. Supplemental Material: Data files available at https://doi.org/10.1287/mnsc.2022.4531 .