TY - GEN
T1 - ChatGPT in the Classroom
T2 - 31st ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2023
AU - Murgia, Emiliana
AU - Abbasiantaeb, Zahra
AU - Aliannejadi, Mohammad
AU - Huibers, Theo
AU - Landoni, Monica
AU - Pera, Maria Soledad
N1 - Publisher Copyright:
© 2023 Owner/Author.
PY - 2023/6/26
Y1 - 2023/6/26
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85163725878
U2 - 10.1145/3563359.3597399
DO - 10.1145/3563359.3597399
M3 - Conference contribution
AN - SCOPUS:85163725878
T3 - UMAP 2023 - Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization
SP - 22
EP - 27
BT - UMAP 2023 - Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization
Y2 - 26 June 2023 through 30 June 2023
ER -