ChatGPT in the Classroom: A Preliminary Exploration on the Feasibility of Adapting ChatGPT to Support Children’s Information Discovery
Mohammad Aliannejadi, Theo Huibers, Monica Landoni, Emiliana Murgia, and Maria Soledad Pera. 2022. “ChatGPT in the Classroom: A Preliminary Exploration on the Feasibility of Adapting ChatGPT to Support Children’s Information Discovery”. In Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization (UMAP 2023). ACM, pp. 22 - 27. DOI:10.1145/3563359.3597399
Abstract
The influence of ChatGPT and similar models on education is being increasingly discussed. With the current level of enthusiasm among users, ChatGPT is envisioned as having great potential. As generative models are unpredictable in terms of producing biased, harmful, and unsafe content, we argue that they should be comprehensively tested for more vulnerable groups, such as children, to understand what role they can play and what training and supervision are necessary. Here, we present the results of a preliminary exploration aiming to understand whether ChatGPT can adapt to support children in completing information discovery tasks in the education context. We analyze ChatGPT responses to search prompts related to the 4th grade classroom curriculum using a variety of lenses (e.g., readability and language) to identify open challenges and limitations that must be addressed by interdisciplinary communities.