AI Transparency Statements in Graduate Coursework: Evidence from a Doctoral-Level Program Evaluation Course

This study examines the extent to which explicit instruction in artificial intelligence (AI) use shapes graduate students’ engagement with AI transparency statements in a doctoral-level program evaluation course. Drawing on a mixed methods analysis of 98 statements across two semesters, one without formal instruction (Fall 2024) and one with structured, purposeful guidance (Fall 2025), the study compares differences in prevalence, thoroughness, transparency, and rigor. Results indicate that explicit instruction was associated with substantially higher reported AI use and more detailed, transparent disclosures, particularly in relation to tool identification, prompt disclosure, and workflow description. Differences in rigor were modest and not statistically significant. Findings underscore the importance of explicit instruction in supporting transparent and academically appropriate AI use.

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Publication Details

Journal
College Teaching
Published
2026-07-25
DOI
https://doi.org/10.1080/87567555.2026.2707131
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
0.00
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article

AI Transparency Statements in Graduate Coursework: Evidence from a Doctoral-Level Program Evaluation Course

William M. Murrah, David T. Marshall, Katelyn Nelson
College Teaching
Ethics and Social Impacts of AI
article

AI Transparency Statements in Graduate Coursework: Evidence from a Doctoral-Level Program Evaluation Course

William M. Murrah, David T. Marshall, Katelyn Nelson
article en

Abstract

This study examines the extent to which explicit instruction in artificial intelligence (AI) use shapes graduate students’ engagement with AI transparency statements in a doctoral-level program evaluation course. Drawing on a mixed methods analysis of 98 statements across two semesters, one without formal instruction (Fall 2024) and one with structured, purposeful guidance (Fall 2025), the study compares differences in prevalence, thoroughness, transparency, and rigor. Results indicate that explicit instruction was associated with substantially higher reported AI use and more detailed, transparent disclosures, particularly in relation to tool identification, prompt disclosure, and workflow description. Differences in rigor were modest and not statistically significant. Findings underscore the importance of explicit instruction in supporting transparent and academically appropriate AI use.

College Teaching
Auburn University (US)
Quality Education
Openalex Percentile: Top 5%
Ethics and Social Impacts of AI
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