Enhancing Collaborative Problem Solving Through AI Personal Assistant Interactions: Insights From Diverse Prior Knowledge Groups

Abstract

Collaborative problem-solving (CPS) activities encourage learners to share knowledge and complete complex tasks together. However, learners with insufficient prior knowledge may struggle to contribute effectively to these tasks. Appropriate support is, therefore, needed to help learners address challenges that arise during CPS. This study developed an AI personal assistant (AIPA) embedded with a ChatGPT-based large language model to serve as a personal consultant in CPS activities by providing personalized knowledge, resources, and guidance. This design raises an important question about how AI assistance is associated with learners’ interaction patterns and the quality of collaboration. By analyzing student dialogues and interactions with the AIPA, this study found no significant differences in CPS performance among high-, mixed-, and low-knowledge groups after using the AIPA. However, both mixed- and low-knowledge groups interacted with the AIPA more frequently than the high-knowledge groups. Analysis of question types further showed significant differences, with explanation-oriented questions being more common in low-knowledge groups. In addition, high-knowledge groups interacted minimally with the AIPA, low-knowledge groups engaged consistently, and mix-knowledge groups showed moderate interaction, with shifting question types across the CPS process. The results also showed that groups with lower prior knowledge relied more on information provided by the AIPA in their discussions. These findings suggest that the AIPA can serve as a supportive resource in CPS contexts and contribute to understanding students’ interaction patterns with AI support in educational settings.