Preferred scaffolding does not lead to better learning performance: Empirical evidence from AI-supported mathematical modelling

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Mathematical modelling (MM) is widely recognized as a key competency for addressing complex real-world problems, which requires learners to engage in higher-order processes such as abstraction, representation, and iterative reasoning. However, students often experience difficulties in coordinating these processes within mathematics modelling tasks. While recent advances in artificial intelligence (AI) offer new possibilities for supporting mathematical modelling, limited research has examined how different forms of AI-mediated interaction shape students' learning processes in MM. To address this gap, the present study investigates five AI interaction roles, Tutor, Teaching Assistant, Peer, Excellent Student, and Struggling Student, as distinct pedagogical scaffolding strategies in AI-assisted learning environments. To evaluate the effectiveness of these roles, we conducted a randomized within-subjects experiment with 26 university students to compare modelling competency, learners' role preferences, and learning experiences across the five conditions. A randomized within-subjects experiment with 26 university students was conducted to examine differences in modelling performance and learners’ role preferences across conditions. The findings reveal a notable divergence between mathematical modelling performance and pedagogical roles preference. Students demonstrated higher modelling competency when interacting with Peer and TA roles, which fostered collaborative reasoning and co-construction of ideas. In contrast, students expressed stronger preferences for Tutor and Excellent Student roles, which provided more explicit guidance and structured explanations. The Struggling Student role was consistently perceived as least supportive across both performance and preference measures. These findings examine how interactional structures influence students’ engagement in mathematical modelling. The study highlights the importance of balancing cognitive scaffolding with opportunities for collaborative sense-making, and provides design implications for developing adaptive, learner-centered AI-supported modelling environments in mathematics education.