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

Related papers

• Bridging the Gap: Integrating Ethics and Environmental Sustainability in AI Research and Practice [57.94036023167952]
  We argue that the efforts aiming to study AI's ethical ramifications should be made in tandem with those evaluating its impacts on the environment. We propose best practices to better integrate AI ethics and sustainability in AI research and practice.
  arXiv  Detail & Related papers  (2025-04-01T13:53:11Z)
• The Role of AI, Blockchain, Cloud, and Data (ABCD) in Enhancing Learning Assessments of College Students [0.0]
  This study investigates how ABCD technologies can improve learning assessments in higher education. The objective is to research how students perceive things, plan their behavior, and how ABCD technologies affect individual learning, academic integrity, co-learning, and trust in the assessment.
  arXiv  Detail & Related papers  (2025-02-17T15:11:44Z)
• Auto-assessment of assessment: A conceptual framework towards fulfilling the policy gaps in academic assessment practices [4.770873744131964]
  We surveyed 117 academics from three countries (UK, UAE, and Iraq) We identified that most academics retain positive opinions regarding AI in education. For the first time, we propose a novel AI framework for autonomously evaluating students' work.
  arXiv  Detail & Related papers  (2024-10-28T15:22:37Z)
• The Rise of Artificial Intelligence in Educational Measurement: Opportunities and Ethical Challenges [2.569083526579529]
  AI in education raises ethical concerns regarding validity, reliability, transparency, fairness, and equity. Various stakeholders, including educators, policymakers, and organizations, have developed guidelines to ensure ethical AI use in education. In this paper, a diverse group of AIME members examines the ethical implications of AI-powered tools in educational measurement.
  arXiv  Detail & Related papers  (2024-06-27T05:28:40Z)
• Qualitative and quantitative analysis of student's perceptions in the use of generative AI in educational environments [0.0]
  The effective integration of generative artificial intelligence in education is a fundamental aspect to prepare future generations. The objective of this study is to analyze from a quantitative and qualitative point of view the perception of controlled student-IA interaction within the classroom.
  arXiv  Detail & Related papers  (2024-05-22T09:56:05Z)
• Generative AI Adoption in Classroom in Context of Technology Acceptance Model (TAM) and the Innovation Diffusion Theory (IDT) [1.9659095632676098]
  This study aims to dissect the underlying factors influencing educators' perceptions and acceptance of GenAI and LLMs. Our investigation reveals a strong positive correlation between the perceived usefulness of GenAI tools and their acceptance. The perceived ease of use emerged as a significant factor, though to a lesser extent, influencing acceptance.
  arXiv  Detail & Related papers  (2024-03-29T22:41:51Z)
• Bringing Generative AI to Adaptive Learning in Education [58.690250000579496]
  We shed light on the intersectional studies of generative AI and adaptive learning. We argue that this union will contribute significantly to the development of the next-stage learning format in education.
  arXiv  Detail & Related papers  (2024-02-02T23:54:51Z)
• Toward enriched Cognitive Learning with XAI [44.99833362998488]
  We introduce an intelligent system (CL-XAI) for Cognitive Learning which is supported by artificial intelligence (AI) tools. The use of CL-XAI is illustrated with a game-inspired virtual use case where learners tackle problems to enhance problem-solving skills.
  arXiv  Detail & Related papers  (2023-12-19T16:13:47Z)
• Proactive and Reactive Engagement of Artificial Intelligence Methods for Education: A Review [2.2843885788439793]
  We investigate how artificial intelligence, machine learning and deep learning methods are being utilized to support students, educators and administrative staff. We consider the involvement of AI-driven methods in the education process in its entirety.
  arXiv  Detail & Related papers  (2023-01-23T02:47:36Z)
• Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
  In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society. A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests. This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
  arXiv  Detail & Related papers  (2021-06-25T22:31:55Z)
• An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
  This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
  arXiv  Detail & Related papers  (2021-05-07T12:01:31Z)
• Personalized Education in the AI Era: What to Expect Next? [76.37000521334585]
  The objective of personalized learning is to design an effective knowledge acquisition track that matches the learner's strengths and bypasses her weaknesses to meet her desired goal. In recent years, the boost of artificial intelligence (AI) and machine learning (ML) has unfolded novel perspectives to enhance personalized education.
  arXiv  Detail & Related papers  (2021-01-19T12:23:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.