Aiplato: A Novel Ai Tutoring And Stepwise Feedback System For Physics Homework

Atharva Dange, The University of Texas at Arlington

Ramón López, The University of Texas at Arlington

Date

01/2026

AI Overview

Why it matters

Online homework often provides only correctness feedback, leading students to guess answers rather than learn problem-solving processes. Stepwise AI tutoring may offer real-time, context-sensitive guidance for complex physics problems at scale.

Study at a glance

Question: How does student engagement with an AI tutoring platform relate to introductory physics performance and student perceptions?
Setting and data: Introductory mechanics course at the University of Texas at Arlington, Spring 2025, 87 consenting undergraduate students.
Method: Quasi-experimental study using regression and covariance analyses (ANCOVA), Fisher's exact test, and survey analysis.
AI tools: aiPlato

Key findings

Higher engagement with aiPlato was associated with an average 13.86 percentage point gain on the final exam over low engagement after controlling for prior performance.
Students with higher engagement were significantly more likely to earn an A or B course grade, with no high-engagement students receiving a D.
Students primarily used iterative feedback features rather than full solutions, rating the platform positively overall while reporting issues with handwriting recognition and interface lag.

Summary drafted with AI from the full paper and may contain errors. See the full paper for details.

This exploratory study examines the classroom deployment of aiPlato, an AI-enabled homework platform, in a large introductory physics course at the University of Texas at Arlington. Designed to support open-ended problem solving, aiPlato provides step-wise feedback and iterative guidance through tools such as "Evaluate My Work" and "AI Tutor Chat", while preserving opportunities for productive struggle. Over four optional extra-credit assignments, the platform captured detailed student interaction data, which were analyzed alongside course performance and end-of-semester survey responses. We examine how students engaged with different feedback tools, whether engagement patterns were associated with performance on the cumulative final exam, and how students perceived the platform's usability and learning value. Students who engaged more frequently with aiPlato tended to achieve higher final exam scores, with a mean difference corresponding to a standardized effect size of approximately 0.81 between high and low engagement groups after controlling for prior academic performance. Usage patterns and survey responses indicate that students primarily relied on iterative, formative feedback rather than solution-revealing assistance. As a quasi-experimental pilot study, these findings do not establish causality and may reflect self-selection effects. Nonetheless, the results demonstrate the feasibility of integrating AI-mediated, step-wise feedback into authentic physics homework and motivate future controlled studies of AI-assisted tutoring systems.