Will They Try Again? A Large-Scale RCT on Scaffolds that Support Persistence in an Intelligent Tutoring System

要旨

Persistence after failure is critical for learning—but when students make mistakes in intelligent tutoring systems, they often choose not to try again. How can digital platforms encourage students to persist at these moments? We conducted a randomized controlled trial in an intelligent tutoring system for math and science, involving 164,532 students (Grades 8-12) who completed 17 million practice problems. We tested two scalable interventions: a brief persuasive prompt encouraging students to try again, and a visual default nudge that highlighted the retry option. Both interventions increased persistence after failure, and when combined, their effects were additive—suggesting they operate through distinct psychological mechanisms. The nudge had a much larger immediate effect, but the prompt showed proportionally greater spillover to untreated problems. These findings advance theories of persuasive design, demonstrating that implicit, interface-level nudges and explicit motivational prompts can be combined to avoid redundancy while amplifying impact.

受賞
Honorable Mention
著者
Michael W. Asher
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Yumou Wei
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Adam Daniel. Reynolds
Siyavula Foundation, Johannesburg, South Africa
Amy Ogan
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Paulo F.. Carvalho
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States

会議: CHI 2026

ACM CHI Conference on Human Factors in Computing Systems

セッション: AI Tutors and Learning Support Systems

P1 - Room 123
7 件の発表
2026-04-17 20:15:00
2026-04-17 21:45:00