Jul 2026· Systems and Computing· 0 citations· 38 references
TL;DR
It is concluded that AI-related academic misconduct is often a rational behavioral choice driven by perceived institutional unpreparedness rather than ignorance, and calls for a transition toward adaptive academic integrity frameworks that prioritize ethical awareness and transparent academic policy communications to students.
Abstract
Context: This study examined the intersection of academic integrity and Generative Artificial Intelligence (GenAI) adoption among students in Nigerian universities, addressing a critical gap in empirical, student-centered research from the Global South. Objective: To investigate students' knowledge, usage patterns, and perceptions of GenAI, as well as their awareness of academic integrity and the behavioral factors that shape ethical decision-making in AI use. Methods: A quantitative cross-sectional survey was conducted with 262 undergraduate and postgraduate students from nine Nigerian higher education institutions. The study was informed by relevant literature from major academic databases. Data were collected via a structured questionnaire and analyzed using descriptive and inferential statistics, with Prospect Theory applied as the theoretical framework. Results: Findings revealed high AI literacy, with 84.7% of participants already integrating AI tools into academic work. However, a significant knowledge–behavior gap emerged: while over 90% acknowledged the importance of academic honesty, only 36% believed AI use required disclosure. This ethical ambiguity was compounded by weak institutional guidance: 74.8% of students reported being unaware of their university AI policies. Inferential analysis indicated that students engage in risk–reward evaluations, where low perceived detection risks and academic pressures frequently outweigh potential sanctions. Conclusion: This study concludes that AI-related academic misconduct is often a rational behavioral choice driven by perceived institutional unpreparedness rather than ignorance. It calls for a transition toward adaptive academic integrity frameworks that prioritize ethical awareness and transparent academic policy communications to students.
This study explores how digital literacy shapes research integrity among final-year Library and Information Science students at the University of Ilorin, Nigeria, within AI-mediated academic environments. Using a qualitative phenomenological approach, data were collected from thirty participants through interviews and focus groups and analysed thematically to capture students’ experiences of digital research and ethical decision making. The findings show that although students display strong ethical awareness linked to their professional identity, this does not always translate into practice. Digital competence supports source evaluation and reference management, but also enables uncritical copying, use of rewriting tools, and uncertain engagement with artificial intelligence, especially under academic pressure. Behaviour is further influenced by inconsistent supervision, unclear institutional guidance, and peer norms. The study reframes digital literacy as an ethical competence rather than a purely technical skill, showing that its role in research integrity depends on intention and context. It contributes evidence from an underexplored African setting and highlights the need for clearer, discipline-sensitive policies on AI use, alongside stronger supervision and integrity education.
A. Dunmade· IQRO Journal of Islamic Educ...· 0 citations
The findings advance academic integrity research by shifting attention from attitudes to scenario-based decision quality and clarifying the internalization mechanism through moral cognition.
Hao Deng, Minli Yang, Liling Huang et al.· Frontiers in Psychology· 0 citations
Objectives: The perspective on the use of Generative AI (GAI)in higher education at King Abdulaziz University is balanced yet cautious. Professors recognize GAI's potential for education but are concerned about originality, academic integrity, and ethics. Faculty believe GAI can enhance analysis and clarity in research writing, but maintaining ethical compliance and oversight is crucial to uphold originality and scholarly standards. The study aims to explore ethical concerns and their impact on the quality of graduate students' academic submissions. It seeks to promote responsible use of GAI, thereby strengthening academic integrity and fostering innovation in graduate research. The research emphasizes the importance of ethical training, clear institutional policies, and transparent guidelines to responsibly incorporate GAI into research and teaching. Increasing awareness and developing robust ethical frameworks are essential to ensure GAI serves as a tool for innovation, not academic compromise.Methods: A quantitative approach was used, with a questionnaire to collect data on professors' assessments of graduate students' research quality and their adherence to GAI ethics. 29 participants, including full professors, associate professors, assistant professors, and lecturers. Purposive sampling was used, with significant experience in evaluating research integrating GAI tools, to ensure participants are well-versed in assessing GAI-augmented academic research.Results: The findings show a cautious yet balanced view of Generative AI in higher education. Professors at the Department of Information Science at King Abdulaziz University see GAI's benefits but worry about its impact on originality, integrity, and ethics. Faculty view GAI as a helpful research tool but emphasize ethical compliance and supervision to maintain standards.Conclusions: The study highlights the need for ethical training, policies, and clear guidelines to ensure responsible use of GAI, promoting innovation while safeguarding academic values.
H. Albadi· International journal of com...· 0 citations
The proliferation of Generative Artificial Intelligence (GenAI) in higher education, particularly among undergraduate student population, has raised major contradiction about the traditional notions of academic integrity. This study reviewed related literatures to evaluate the balance of choice between digital governance and academic integrity, with intention of shifting the focus of educators from enforcement of academic integrity to student empowerment strategy through AI-literacy. Altogether, over 50 peer-reviewed articles and institutional policy frameworks published in peer-reviewed journals between 2021 and 2026 were solicited, and synthesized as part of the systematic review process. The key findings revealed a significant policy gap in which over 64% of undergraduate students are utilizing AI tools without formal institutional guidance or constraints. The other significant findings show that the traditional detection-based methods of academic dishonesty are losing their effectiveness, and could lead to a stability contradiction where ambiguous rules cause educators to struggle with competing balance of choice between AI adoption and academic integrity. Overall, the research findings draw the attention of educators and stakeholders to a new empowerment strategy that view AI-literacy as a competency ability to reduce intentional misbehavior in academic process. The study concludes that in order for undergraduate education to continue to be relevant in a society where AI is pervasive, governance must change toward process-oriented evaluation and relational originality. The other key suggestions include making AI-literacy classes mandatory for first-year students, and establishing uniform disclosure policies to encourage transparency and intellectual responsibility.
Joe Mutebi, Brian Mugisha, Ibrahim Adabara et al.· F1000Research· 0 citations
It is argued that AI-related integrity disputes are better understood as conflicts between competing values than as individual moral failings, and implications for policy design, assessment reform, and faculty development are discussed.
M. Grobler· Proceedings of the Internati...· 0 citations
The increasing use of Generative Artificial Intelligence (GenAI) in higher education has changed the academic writing practices of students, presenting both opportunities to improve learning and challenges to academic integrity. The study explored the relationship between the GenAI use profile, academic integrity awareness, ethical judgment and decision-making, and responsible GenAI use practices in academic writing of fourth-year students of North Eastern Mindanao State University (NEMSU). It examined students’ awareness of risks to academic integrity associated with GenAI, ethical judgment and decision-making in GenAI-assisted writing, and responsible AI-use practices. The study also explored the lived experiences and perceptions of students on ethical use of GenAI in academic writing. A descriptive correlational design was employed. The quantitative data were collected from 303 fourth-year students selected using simple random sampling. Descriptive statistics and Pearson Product-Moment Correlation were used to analyze the data. The findings revealed that respondents were generally moderately aware of GenAI-related academic integrity risks, exhibited moderately manifested ethical judgment and decision-making, and demonstrated moderately practiced responsible GenAI use behaviors. Significant relationships were found between GenAI use profile and academic integrity awareness, academic integrity awareness and ethical judgment and decision-making, and ethical judgment and decision-making and responsible GenAI use practices. These findings suggest that while students possess foundational knowledge and ethical awareness regarding GenAI use, gaps remain in translating awareness into consistently responsible practices. The study underscores the need for comprehensive AI literacy programs, explicit institutional guidelines, and ethics-focused educational interventions that promote transparency, accountability, critical evaluation, and responsible human oversight in AI-assisted academic writing.
Christianne Mae R. Rivas, Mardie E. Bucjan· International journal of res...· 0 citations