Date
Publisher
arXiv
Mathematical modelling (MM) is a key competency for solving complex
real-world problems, yet many students struggle with abstraction,
representation, and iterative reasoning. Artificial intelligence (AI) has been
proposed as a support for higher-order thinking, but its role in MM education
is still underexplored. This study examines the relationships among students'
design thinking (DT), computational thinking (CT), and mathematical modelling
self-efficacy (MMSE), and investigates their preferences for different AI roles
during the modelling process. Using a randomized controlled trial, we identify
significant connections among DT, CT, and MMSE, and reveal distinct patterns in
students' preferred AI roles, including AI as a tutor (providing explanations
and feedback), AI as a tool (assisting with calculations and representations),
AI as a collaborator (suggesting strategies and co-creating models), and AI as
a peer (offering encouragement and fostering reflection). Differences across
learner profiles highlight how students' dispositions shape their expectations
for AI. These findings advance understanding of AI-supported MM and provide
design implications for adaptive, learner-centered systems.
Study at a glance
- Question: Which AI roles do students prefer when solving mathematical modelling problems, and how do baseline competencies relate to preferences?
- Setting and data: Online experiment with 26 undergraduate students from non-STEM majors without university mathematics coursework.
- Method: Within-subjects, counterbalanced randomized controlled trial with survey and qualitative analyses.
Why it matters
Mathematical modelling is difficult for many students, yet the role of artificial intelligence in supporting this complex process is underexplored. Understanding how learners perceive different AI pedagogical roles can guide the design of adaptive learning systems.
Key findings
- Students preferred competent AI personas (Tutor, Excellent Student, Peer, and Teaching Assistant) similarly, while ranking the Struggling Student persona significantly lowest across all measures.
- Both design thinking and computational thinking were significant positive predictors of mathematical modelling self-efficacy, with design thinking demonstrating a particularly strong predictive effect.
- Participants with higher design thinking scores rated the Teaching Assistant role significantly more favorably than those with lower scores.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
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Study design
