Exploring ChatGPT‐Facilitated Scaffolding in Undergraduates' Mathematical Problem Solving. - Zhou, Ruijie; He, Xiuling; Fan, Qiong; Li, Yangyang; Li, Yue; Xiao, Xiong; Fang, Jing (Journal of Computer Assisted Learning, 2025)
Background: ChatGPT, an AI‐based chatbot, supports learning by accurately interpreting and responding to user inputs. Despite its potential, few empirical studies have examined its influence on college students' mathematical problem‐solving processes. Objectives: This study aimed to introduce a ChatGPT‐facilitated scaffolding to investigate its impact on students' mathematical problem‐solving behaviours, performance and perceptions. Methods: Twenty‐nine undergraduates participated in this study, engaging in mathematical problem‐solving tasks using the scaffolding. A mixed‐method approach was employed, incorporating performance data, interaction analysis and self‐reported surveys to assess both quantitative and qualitative aspects of students' experiences. In particular, lag sequential analysis was applied to explore the undergraduates' problem‐solving behavioural patterns. Results and Conclusions: Results demonstrated that the ChatGPT‐facilitated scaffolding significantly improved students' mathematical problem‐solving performance. The high‐performance group (HPG) exhibited a greater frequency of interpretive and evaluative activities, transitioning from factual to metacognitive representations, while the low‐performance group (LPG) primarily transitioned from prompt selection to procedural representations. Additionally, most participants expressed positive perceptions of the scaffolding experience and reported an improvement in their problem‐solving skills. Major Takeaways: These findings offer valuable insights for the design and implementation of AI‐facilitated learning activities in mathematical problem‐solving contexts.