Jun 2026· Journal of Ethics in Higher Education· 0 citations· 8 references
TL;DR
This study explored AI-related ethical dilemmas affecting academic integrity at a South African research-intensive university and reframes academic integrity in the AI era as a systemic governance issue requiring clear AI-integrated academic integrity policies, responsible implementation mechanisms, assessment redesign, and shared institutional accountability.
Abstract
The development of generative artificial intelligence (AI) has intensified debates regarding academic integrity in digitally mediated higher education. This study explored AI-related ethical dilemmas affecting academic integrity at a South African research-intensive university. It examined how graduate students interpret and navigate ethical tensions in AI use, and how institutional and structural conditions shape these experiences. A qualitative single-case design was used. Sixteen graduate students from a School of Public Health participated through open-ended questionnaires and semi-structured interviews. Data were analysed using Braun and Clarke’s six-phase reflexive thematic analysis. The study was theoretically informed by an integrative framework combining the Technology Acceptance Model (TAM) and connectivism, extended through an academic integrity lens. Two interrelated themes emerged. First, students articulated an ethical adoption tension: while recognising AI’s usefulness and efficiency, they expressed concerns about overreliance, blurred authorship, data privacy, and the erosion of critical thinking and scholarly identity. Second, participants highlighted structural and institutional conditions shaping AI engagement, including unequal access to digital resources, uncertainty about institutional expectations, and the need for ethical AI literacy. The study offers a contextually grounded perspective from a Global South setting and reframes academic integrity in the AI era as a systemic governance issue requiring clear AI-integrated academic integrity policies, responsible implementation mechanisms, assessment redesign, and shared institutional accountability.
The rapid integration of generative artificial intelligence (GenAI) into higher education has created significant tensions around authorship, assessment authenticity, and academic integrity. This paper introduces axiological expectations mismatch as a conceptual framework for understanding a core governance problem: the divergence between institutional value frameworks and the ethical reasoning students apply when using AI in their academic work. Drawing on an interpretive qualitative case study at a single private higher education institution in South Africa, the study analyses twelve institutional documents produced between 2021 and 2025, supplemented by descriptive trend data from 3,854 plagiarism incidents over the same period. Schwartz’s (2012) theory of basic values provides the theoretical lens; reflexive thematic analysis is the analytic method. Three findings emerge: first, a shift from prohibition to conditional permission for AI use, contingent on disclosure and authorship accountability; second, a reframing of academic integrity as a developmental process rather than a purely disciplinary matter; and third, evidence that policy adaptation improved institutional capacity to recognise and classify AI-related misconduct before it reduced its incidence. The paper argues that AI-related integrity disputes are better understood as conflicts between competing values (fairness, accountability, efficiency, and innovation) than as individual moral failings. Implications for policy design, assessment reform, and faculty development are discussed.
M. Grobler· Proceedings of the Internati...· 0 citations
AI ethics has become an essential part of higher education today, reshaping traditional ideas about academic honesty for the digital age. It remains unclear how students interpret the ethics of AI use, experience ethical principles, and apply guidelines in their academic settings. To fill the gap, this study explores perceptions and practices of AI ethics among higher education students at Banaras Hindu University (BHU), using the AI and Ethics Perception Scale (AEPS) dimensions, including transparency, accountability, privacy, fairness, and human oversight, as a conceptual framework. An interpretative qualitative case study was conducted with 15 purposively selected students from seven departments at BHU. We analysed responses from their semi-structured interviews using thematic analysis. The study findings show that most students (N = 10) perceive AI as lacking transparency, particularly regarding its sources and accuracy, and acknowledge their accountability for AI-assisted work. Students (N = 15) expressed concern about privacy, particularly about avoiding the entry of personal, financial, or academic information into AI systems. The study also found that teacher review plays an important role in maintaining academic integrity. Students reported frequent use of AI in their own educational tasks, often without considering ethical implications. The use of AI has affected their study habits, time management, and learning strategies, while also reducing their stress levels. This study suggests that universities should develop their own ethical guidelines and frameworks for the use of AI. They should also conduct seminars and workshops to raise awareness among students and teachers about ethical AI use in academia.
Generative artificial intelligence (AI) tools are rapidly reshaping academic practices in higher education. While global debates focus on academic integrity, authorship, and assessment reform, empirical evidence from developing contexts remains limited. This study investigates first-year university students’ perceptions of generative AI in academic work, focusing on ethical awareness, learning adaptation, and expectations for institutional guidance. Using a cross-sectional survey design, data were collected from 213 undergraduate students across multiple disciplines. The instrument included demographic variables and Likert-scale items measuring attitudes toward AI collaboration, plagiarism awareness, and confidence in distinguishing AI-generated content, motivation to improve AI skills, and demand for university policy frameworks. Descriptive and comparative analyses reveal generally positive attitudes toward AI as a learning support tool, accompanied by high ethical concern and strong demand for institutional guidelines. Disciplinary variation suggests differing levels of comfort and adaptive engagement with AI tools. The findings indicate that students do not view AI solely as a shortcut mechanism but as an emerging academic partner requiring structured governance and literacy development. The study contributes to ongoing discussions on AI integration in higher education by foregrounding student agency in a Global South context and offering pedagogical and policy implications for responsible AI adoption.
Sharifuzzaman, M. Rahman· Asian Journal of Contemporar...· 0 citations
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.
Ignatius Ogbaga, U. Onwudebelu, Nathaniel Akwuma et al.· Systems and Computing· 0 citations
The rapid integration of Generative AI in higher education has transformed teaching and learning, yet limited research explores the factors driving its adoption and impact on academic performance. This study addresses the gap in understanding how ethical principles (fairness, accountability, transparency, accuracy, autonomy) and AI characteristics (perceived anthropomorphism, perceived intelligence) influence students’ use of Generative AI tools and their subsequent academic outcomes. The research aims to develop and test a theoretical model that integrates these factors to explain Generative AI adoption and its effect on perceived academic performance among university students. Data were collected through surveys from 318 students and analyzed via Partial Least Squares-Structural Equation Modeling (PLS-SEM). Results revealed that accountability, transparency, accuracy, autonomy, perceived anthropomorphism, and perceived intelligence significantly drive Generative AI use, while fairness does not. Generative AI use, in turn, is positively associated with academic performance, explaining 49.9% of its variance. These findings advance technology adoption and educational technology research by highlighting the interplay of ethical and technical factors in AI adoption, offering practical insights for educators and developers to optimize AI tools for equitable and effective learning.
Mostafa Al-Emran, Mohammad A. Al-Sharafi, Behzad Foroughi et al.· Journal of educational compu...· 0 citations
Introduction The integration of generative artificial intelligence into higher education has reshaped students’ academic practices and blurred normative boundaries around academic integrity. While prior research has focused mainly on attitudes and usage intentions, limited attention has been paid to students’ AI ethical decision-making in academic scenarios and its psychological mechanisms. This study examined the associations among AI ethics guidance, academic ethics course experience, moral cognition, cognitive complexity, and AI ethical decision-making. It tested whether moral cognition mediated the relationships between normative inputs and AI ethical decision-making and whether cognitive complexity strengthened these relationships. The study integrated the Stimulus–Organism–Response framework with Social Cognitive Theory and combined net-effect, configurational, and importance–performance analyses. Methods Survey data were collected from 1,106 undergraduates at Chinese universities. Partial least squares structural equation modeling was used to test direct, mediating, and moderating relationships, complemented by fuzzy-set qualitative comparative analysis to identify configurational pathways to high AI ethical decision-making. Importance–performance matrix analysis identified intervention priorities. Results AI ethics guidance (β = 0.334, p < 0.001) and academic ethics course experience (β = 0.336, p < 0.001) were positively associated with AI ethical decision-making. Both normative inputs were positively associated with moral cognition (β = 0.479 and 0.280, respectively; p < 0.001), which was positively associated with AI ethical decision-making (β = 0.215, p < 0.001). Significant indirect relationships were identified through moral cognition (indirect β = 0.103 and 0.060; p < 0.001). Cognitive complexity strengthened the relationships between the two normative inputs and AI ethical decision-making (β = 0.077 and 0.073; p < 0.05). The model explained 66.3% of the variance in AI ethical decision-making (R2 = 0.663). fsQCA revealed no single necessary condition; however, AI ethics guidance and academic ethics course experience consistently emerged as core conditions across high-performing configurations (overall consistency = 0.934; coverage = 0.793). Discussion The findings advance academic integrity research by shifting attention from attitudes to scenario-based decision quality and clarifying the internalization mechanism through moral cognition. Practically, they highlight the complementary roles of technological prompts and academic ethics course experience in fostering students’ AI ethical decision-making.
Hao Deng, Minli Yang, Liling Huang et al.· Frontiers in Psychology· 0 citations