Eunmi Joung
This study examined gains in undergraduate students' mathematical computation and GenAI error-detection skills following a GenAI-integrated error analysis activity in a quantitative reasoning course. An explanatory sequential mixed-methods design was employed, with pretest and posttest data analyzed quantitatively and open-ended survey responses analyzed qualitatively using thematic analysis. Quantitative results indicated statistically significant improvement in both mathematical computation ( M = 2.78 to M = 6.39, d = 1.62) and GenAI error-detection skills ( M = 3.61 to M = 7.72, d = 1.81), with large effect sizes for both subscales. Qualitative findings revealed that students developed independent verification habits, recognized recurring error types in the GenAI-generated solutions they analyzed across fractions, decimals, percents, and introductory algebra, and perceived their error-detection skills as transferable beyond the mathematics classroom. These preliminary findings are consistent with a dual-development framework in which mathematical reasoning and GenAI error-detection literacy may be developed simultaneously through error analysis pedagogy and establish a foundation for future larger-scale investigations of GenAI-integrated error analysis in undergraduate mathematics education.