Generative artificial intelligence in higher education
Emotional tensions and ethical declaration
Bibliographic Data
| ID | 21297632 |
|---|---|
| Authors | Yao Qu (0009-0009-7738-4093, School of Social Sciences Nanyang Technological University Singapore Singapore), Hui En Loo (School of Social Sciences Nanyang Technological University Singapore Singapore), H Lôo (0000-0002-7252-8520, Nanyang Technological University), Jue Wang (0000-0002-6305-4298, School of Social Sciences Nanyang Technological University Singapore Singapore, corresponding author) |
| Year | 2025 |
| Publication date | 2025-11-24 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | British Journal of Educational Technology (JOURNAL) |
| Journal identifiers | ISSN: 0007-1013 • E-ISSN: 1467-8535 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/bjet.70029 |
| OpenAlex | W4416598502 |
| Language | EN |
| Citations received | 3 |
| References cited | 36 |
The increasing use of Generative Artificial Intelligence (GenAI) tools such as ChatGPT in higher education has raised questions about authorship, ethical responsibility, and academic transparency. While institutional guidelines exist, many remain vague and ineffective, leaving students to interpret disclosure obligations on their own. This mixed‐methods study investigates how undergraduates at a large Singaporean university decide whether to disclose GenAI use, introducing the concepts of strategic and conceptual non‐disclosure. We examine two psychological mechanisms: the placebo effect , where perceived cognitive enhancement encourages risk‐taking behaviours, and the AI ghostwriter effect , where responsibility for authorship is externalized. Quantitative results show that the AI ghostwriter effect significantly predicts lower disclosure, particularly in theoretical disciplines (e.g., Humanities, Science, and Arts). The placebo effect is not statistically significant but shows a consistent negative trend in applied fields. Qualitative data supports and explains these patterns. Students in theoretical disciplines express more ethical discomfort, whereas those in applied fields view GenAI as a practical tool. Ambiguity in institutional policies and concerns about detection fairness also emerge as key factors driving non‐disclosure. This study advances understanding of ethical behaviour in the GenAI context and offers practical recommendations for discipline‐sensitive disclosure policies. Practitioner notes What is already known about this topic Students often use GenAI in academic work but do not always disclose it. Institutional guidelines are often vague, leading to confusion about disclosure. Disciplinary norms affect how students interpret authorship and integrity. What this paper adds This study provides empirical evidence for the AI ghostwriter effect , showing that students who frame GenAI as a personal thinking tool are significantly less likely to disclose the use. It reveals a key disciplinary divergence: students in the theoretical discipline (e.g., Humanities and Sciences) more frequently report ethical discomfort, while their peers in applied field (e.g., Engineering and Business) more readily normalize GenAI as a standard tool. It introduces and validates a distinction between two motivations for non‐disclosure: Conceptual non‐disclosure (not perceiving the need to declare) is a stronger driver of non‐disclosure than strategic non‐disclosure (intentional concealment), particularly in theoretical disciplines. Implications for practice and/or policy Policies can be designed to target the ghostwriter effect in theoretical disciplines by having guidelines that explicitly distinguish between tool use and undisclosed co‐authorship. To address students' fears, institutions are encouraged to build trust with transparent and fair disclosure procedures, including clear, tiered protocols and a robust appeals process
Ambiguity · Cognition · Context (archaeology) · Context effect · Declaration · Generative grammar · Higher education · Metacognition · Artificial Intelligence in Healthcare and Education · Ethics and Social Impacts of AI · Neuroethics, Human Enhancement, Biomedical Innovations
ChatGPT is fun, but not an author
Generative AI tools and assessment
Google Effects on Memory
Relationships between subject matter characteristics and the structure and output of university departments.
Chatting and cheating
To disclose or not to disclose
The placebo effect of human augmentation
Beware of metacognitive laziness
Contributorship and division of labor in knowledge production
Computer Networks and Scientific Work
Use of ChatGPT in academia
Generative Artificial Intelligence (GenAI) in the research process – A survey of researchers’ practices and perceptions
Moral Disengagement in the Perpetration of Inhumanities
Algorithms, Governance, and Governmentality
Authorship Norms and Project Structures in Science
The significance of disciplinary differences
| Unique citing works | 3 |
|---|---|
| Citations per year | 3 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 3 |