JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1233 10.6339/26-JDS1233 Education in Data Science Perceptions and Utilization of GenAI Tools Among Data Science Students and Faculty https://orcid.org/0009-0000-6444-9980 HasanAbeer M.amhasan1@ncat.edu1∗ MostafaSayed A.1 Department of Mathematics & Statistics, North Carolina A&T State University, Greensboro, NC, USA Corresponding author. Email: amhasan1@ncat.edu. 202636202600117 Supplementary Material

The supplementary material contains (S1) additional findings from both the student and faculty surveys; (S2) the complete student survey instruments, including response options and item-level summaries/visualizations; and (S3) the complete faculty survey questions with item-level response summaries and visualizations.

18220262852026 2026 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2026 Open access article under the CC BY license.

This study investigates perceptions and use of generative artificial intelligence (GenAI) tools among students and faculty in statistics and data science at a historically Black college or university. Survey data from 119 valid student responses and 14 faculty responses were used to examine familiarity, usage patterns, perceived benefits, awareness of limitations, and instructional support needs. Students reported substantial use of GenAI, with ChatGPT as the dominant tool, primarily for coding assistance and writing support. Although student perceptions of AI in data science workflows and careers were generally positive, confidence in interpreting AI-generated outputs was limited, and concerns about accuracy, reliability, and over-reliance were common. Faculty also viewed GenAI favorably, but self-rated proficiency and the frequency of classroom integration remained limited. Comparisons across student subgroups suggested that familiarity with GenAI and awareness of its limitations varied more by academic level than by gender. These findings highlight a gap between AI adoption and AI literacy and underscore the need for structured training, validation practices, and clearer institutional guidance for responsible AI integration in data science education.

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