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Exploring Student Experiences with ChatGPT in Data Analytics Education: Gender, Academic Level, and Structural Model Evidence

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Title: Exploring Student Experiences with ChatGPT in Data Analytics Education: Gender, Academic Level, and Structural Model Evidence
Language: English
Authors: Mandy Yan Dang, Yulei Gavin Zhang, Yiyan Stella Li, Susan Williams, Howard Qi, Xihui Zhang
Source: Information Systems Education Journal. 2026 24(2):44-58.
Availability: Information Systems and Computing Academic Professionals. Box 488, Wrightsville Beach, NC 28480. e-mail: publisher@isedj.org; Web site: http://isedj.org
Peer Reviewed: Y
Page Count: 15
Publication Date: 2026
Document Type: Journal Articles
Reports - Research
Tests/Questionnaires
Education Level: Higher Education
Postsecondary Education
Descriptors: Student Experience, Artificial Intelligence, Synchronous Communication, Data Analysis, Technology Uses in Education, Programming Languages, Student Attitudes, Undergraduate Students, Graduate Students, Student Satisfaction, Business Education
ISSN: 1545-679X
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.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1506293
Database: ERIC
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  Data: Exploring Student Experiences with ChatGPT in Data Analytics Education: Gender, Academic Level, and Structural Model Evidence
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  Data: <searchLink fieldCode="SO" term="%22Information+Systems+Education+Journal%22"><i>Information Systems Education Journal</i></searchLink>. 2026 24(2):44-58.
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  Data: Information Systems and Computing Academic Professionals. Box 488, Wrightsville Beach, NC 28480. e-mail: publisher@isedj.org; Web site: http://isedj.org
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  Data: 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.
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        StartPage: 44
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      – SubjectFull: Synchronous Communication
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      – SubjectFull: Student Satisfaction
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