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

View on arXiv ← Back to list

Authors: Jasper Roe, Mike Perkins, Klaire Somoray, Dan Miller, Leon Furze

Published: 2025-02-25 10:32:19+00:00

AI Summary

This study investigated higher education stakeholders' perceptions and intentions regarding deepfake technology using the UTAUT2 framework and qualitative interviews. Findings revealed low adoption intentions primarily driven by hedonic motivation and gender-specific price-value evaluations, alongside ethical concerns and institutional power dynamics.

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.

Key findings

Hedonic motivation was the strongest predictor of deepfake adoption intention. A gender-specific interaction effect showed that price-value influenced male educators' intentions more than female educators'. Qualitative data revealed significant ethical concerns regarding misuse and institutional power dynamics.

Approach

The research employed a mixed-methods approach, combining a survey (n=174) using the UTAUT2 framework to measure technology adoption factors and qualitative interviews to explore stakeholder perspectives on deepfakes in education. Regression analysis and thematic analysis were used to analyze the quantitative and qualitative data respectively.

Datasets

Survey data (n=174 after cleaning) from higher education stakeholders (educators, researchers, administrators, and leaders) and qualitative interview data.

Model(s)

UTAUT2 (Unified Theory of Acceptance and Use of Technology 2) framework used for quantitative analysis; linear regression model used to predict behavioural intention.

Author countries

United Kingdom, Vietnam, Australia, Australia

← Previous