Understanding Student Attitudes and Acceptability of GenAI Tools in Higher Ed: Scale Development and Evaluation

2025-08-03

AI Summary

This study addresses the lack of validated instruments for assessing students’ attitudes toward generative artificial intelligence (GenAI) in higher education. Drawing on a six-theme theoretical framework, we developed and empirically validated a multidimensional attitude scale through exploratory factor analysis, identifying four core dimensions: academic acceptability, learning impact, career impact, and societal concerns. The scale’s reliability and validity were established using data from 297 undergraduate students, with subgroup analyses revealing significant attitudinal differences by gender, academic year, linguistic background, and familial generational status. This instrument constitutes the first empirically validated, standardized measure for evaluating student attitudes toward GenAI in educational contexts. It enables precise identification of cognitive heterogeneity, supports the design of stratified AI literacy interventions, and informs evidence-based, personalized policy development—thereby filling a critical gap in the assessment infrastructure for GenAI in education.

Technology Category

• Philosophy and Ethics of AI: Artificial General Intelligence
• Humans and AI: Learning Human Values and Preferences
• Application Domains: Humanities & Computational Social Science

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Security and Privacy: Large-scale security measurements

Abstract

As generative AI (GenAI) tools like ChatGPT become more common in higher education, understanding student attitudes is essential for evaluating their educational impact and supporting responsible AI integration. This study introduces a validated survey instrument designed to assess students' perceptions of GenAI, including its acceptability for academic tasks, perceived influence on learning and careers, and broader societal concerns. We administered the survey to 297 undergraduates at a U.S. university. The instrument includes six thematic domains: institutional understanding, fairness and trust, academic and career influence, societal concerns, and GenAI use in writing and coursework. Exploratory factor analysis revealed four attitudinal dimensions: societal concern, policy clarity, fairness and trust, and career impact. Subgroup analyses identified statistically significant differences across student backgrounds. Male students and those speaking a language other than English at home rated GenAI use in writing tasks as more acceptable. First-year students expressed greater societal concern than upper-year peers. Students from multilingual households perceived greater clarity in institutional policy, while first-generation students reported a stronger belief in GenAI's impact on future careers. This work contributes a practical scale for evaluating the student impact of GenAI tools, informing the design of educational AI systems.

Problem

• Assess student attitudes toward GenAI tools in higher education
• Develop a survey to measure GenAI acceptability and impact
• Identify subgroup differences in GenAI perceptions among students

Innovation

• Validated survey instrument for GenAI perceptions
• Exploratory factor analysis of attitudinal dimensions
• Subgroup analyses across student backgrounds