Designing AI Peers for Collaborative Mathematical Problem Solving with Middle School Students: A Participatory Design Study

TL;DR

This study explores how middle school students can collaboratively design AI peers that act as competent, friendly, and supportive partners in mathematics problem solving, enhancing collaborative learning experiences.

Contribution

It introduces a participatory design approach to create AI peers tailored for middle school math collaborative problem solving, emphasizing student preferences and design insights.

Findings

Students prefer AI peers that are competent and deferential.

AI peers should provide hints and checks under student control.

A friendly expertise tone is favored over exaggerated personas.

Abstract

Collaborative problem solving (CPS) is a fundamental practice in middle-school mathematics education; however, student groups frequently stall or struggle without ongoing teacher support. Recent work has explored how Generative AI tools can be designed to support one-on-one tutoring, but little is known about how AI can be designed as peer learning partners in collaborative learning contexts. We conducted a participatory design study with 24 middle school students, who first engaged in mathematics CPS tasks with AI peers in a technology probe, and then collaboratively designed their ideal AI peer. Our findings reveal that students envision an AI peer as competent in mathematics yet explicitly deferential, providing progressive scaffolds such as hints and checks under clear student control. Students preferred a tone of friendly expertise over exaggerated personas. We also discuss design recommendations and implications for AI peers in middle school mathematics CPS.