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.
What is the application?
Who is the user?
Who age?
Study design
