Prompting the Professoriate: A Qualitative Study of Instructor Perspectives on LLMs in Data Science Education
Year: 2026
Type: article
Abstract: Large Language Models (LLMs) have shifted in just a few years from novelty to ubiquity, raising fundamental questions for data science education. Tasks once used to teach coding, writing, and problem-solving can now be completed by LLMs, forcing educators to reconsider both pedagogy and assessment. To understand how instructors are responding to this new development, we conducted semi-structured interviews with 42 instructors from 33 institutions in nine countries in June and July 2025. Our qualitative analysis reveals a pragmatic mix of optimism and concern. Many respondents view LLMs as inevitable classroom tools—comparable to calculators or Wikipedia—while others worry about de-skilling, misplaced confidence, and uneven integration across institutions. Around 58% have already introduced demonstrations, guided activities, or make extensive use of LLMs in their courses, though most expect change to remain slow and uneven. That said, 31% have not used LLMs to teach students and do not plan to. We highlight some instructional innovations, including AI-aware assessments, reflective use of LLMs as tutors, and course-specific chatbots. By sharing these perspectives, we aim to help data science educators adapt collectively to ensure curricula keep pace with technological change.
Source: Harvard Data Science Review
Authors: Ana Elisa Lopez-Miranda, Tiffany Timbers, Rohan Alexander
Institutions: University of Toronto, University of British Columbia
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W7123390442