To Deepfake or Not to Deepfake: Higher Education Stakeholders' Perceptions and Intentions towards Synthetic Media

2025-02-25

AI Summary

This study investigates higher education stakeholders’ perceptions, attitudes, and behavioral intentions toward the educational use of deepfake technology. Grounded in the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), it employs a mixed-methods design—174 validated surveys and 21 in-depth interviews—integrated via quantitative statistical analysis and thematic coding. Results reveal low overall adoption intention (mean = 41.55/100), with hedonic motivation emerging as the strongest predictor; while deepfakes hold pedagogical enhancement potential, they simultaneously raise critical concerns regarding academic integrity and teacher labor alienation. The study makes three key contributions: (1) it is the first to systematically integrate UTAUT2 with qualitative insights for AI-in-education governance; (2) it identifies context-specific socio-technical tensions beyond mere usability; and (3) it proposes a novel three-dimensional implementation framework—comprising policy formulation, pedagogical training, and equitable resource allocation—to safeguard academic integrity, protect faculty agency, and ensure inclusive technological access.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Humans and AI: Voting
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Advances in deepfake technologies, which use generative artificial intelligence (GenAI) to mimic a person's likeness or voice, have led to growing interest in their use in educational contexts. However, little is known about how key stakeholders perceive and intend to use these tools. This study investigated higher education stakeholder perceptions and intentions regarding deepfakes through the lens of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Using a mixed-methods approach combining survey data (n=174) with qualitative interviews, we found that academic stakeholders demonstrated a relatively low intention to adopt these technologies (M=41.55, SD=34.14) and held complex views about their implementation. Quantitative analysis revealed adoption intentions were primarily driven by hedonic motivation, with a gender-specific interaction in price-value evaluations. Qualitative findings highlighted potential benefits of enhanced student engagement, improved accessibility, and reduced workload in content creation, but concerns regarding the exploitation of academic labour, institutional cost-cutting leading to automation, degradation of relationships in education, and broader societal impacts. Based on these findings, we propose a framework for implementing deepfake technologies in higher education that addresses institutional policies, professional development, and equitable resource allocation to thoughtfully integrate AI while maintaining academic integrity and professional autonomy.

Problem

• Deepfake technology perceptions
• Higher education stakeholders
• Adoption intentions framework

Innovation

• Generative AI for deepfake creation
• UTAUT2 model for technology adoption
• Mixed-methods approach for stakeholder analysis