ISEDJ

Abstract: The rapid rise of generative AI technologies, particularly large language models such as ChatGPT, has introduced new opportunities for supporting student learning in data analytics education. This study investigates the use of ChatGPT as a complementary aid in a business data analytics course that teaches data mining and machine learning techniques. To accommodate students with differing levels of programming experience, three ChatGPT-assisted Python labs were incorporated alongside traditional learning tools. A structural model was developed and tested using survey responses from 260 students. The results indicate that effort expectancy, task–technology fit, and difficulty management significantly influenced learning satisfaction, and that difficulty management also significantly affected perceived learning performance. In addition, subgroup comparisons across academic levels revealed limited differences, with graduate students reporting higher task–technology fit, whereas undergraduates reporting higher perceived learning performance. Gender-based differences were more evident among undergraduates than graduates. Overall, the findings suggest that ChatGPT was positively received, although its perceived benefits varied across student subgroups. Students’ perceptions of ease of use, alignment between AI assistance and analytical tasks, and their ability to manage task difficulty played central roles in shaping their learning experiences. This study provides empirical insights into how generative AI tools can be effectively integrated into data analytics education to complement existing instructional approaches.

Download this article: ISEDJ - V24 N2 Page 44.pdf

Recommended Citation: Dang, M.Y., Zhang, Y.G., Li, Y.S., Williams, S., Qi, H., Zhang, X., (2026). Exploring Student Experiences With ChatGPT in Data Analytics Education: Gender, Academic Level, and Structural Model Evidence. Information Systems Education Journal 24(2) pp 44-58. https://doi.org/10.62273/HVBV9700