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

• URL: http://arxiv.org/abs/2502.18066v1
• Date: Tue, 25 Feb 2025 10:32:19 GMT
• Title: To Deepfake or Not to Deepfake: Higher Education Stakeholders' Perceptions and Intentions towards Synthetic Media
• Abstract summary: Deepfake technologies use generative artificial intelligence to mimic a person's likeness or voice.<n>This study investigated stakeholder perceptions and intentions regarding deepfakes in higher education.<n>We found that academic stakeholders demonstrated a relatively low intention to adopt these technologies.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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