What does this research mean for the field?

College mathematics students primarily use generative AI for answer-checking rather than conceptual understanding, though structured guidance can promote more learning-oriented applications. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This research aims to understand how generative AI tools affect student engagement and learning in mathematics.

June 10, 2026Open Access

The Impact of Generative AI on Student Learning in College-Level Mathematics: How AI Can Be Effectively Incorporated into the Learning Process

Key Points

This research aims to understand how generative AI tools affect student engagement and learning in mathematics.
Mixed-methods approach using surveys for data collection.
Undergraduate students completed two phases: initial survey on study habits and AI use, followed by reflections after using an AI Study Guide.
Data were qualitatively coded and quantitatively analyzed to identify trends.
Students primarily use AI for answer-checking (approx. 70% of respondents), limiting deeper conceptual understanding.
The AI Study Guide encouraged more learning-oriented use, increasing engagement by 30% in follow-up responses.
AI inaccuracies noted as a limitation impact students' confidence in using AI tools for learning.

Abstract

This research examines the impact of generative AI tools on student engagement and perceived learning in college-level mathematics courses. Using a mixed-methods, survey-based approach, undergraduate students completed two phases of data collection. The first survey explored study habits, math experiences, and patterns of AI use, informing the development of a structured AI Study Guide focused on prompt engineering and responsible AI use. The second survey gathered students’ reflections after using the guide while preparing for an exam. Responses were qualitatively coded and statistically analyzed to identify trends in AI use, benefits, and limitations. Findings suggest students primarily use AI for answer-checking rather than conceptual understanding, though the guide encouraged more learning-oriented applications. Limitations, including AI inaccuracies and the exploratory scope of the second phase, restrict the strength of conclusions. Overall, the study offers preliminary recommendations for responsibly integrating AI into mathematics education while balancing innovation with pedagogical integrity.

The Impact of Generative AI on Student Learning in College-Level Mathematics: How AI Can Be Effectively Incorporated into the Learning Process

Key Points

Abstract

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