Jul 2026· Proceedings of the International Academic Conference on Education, Teaching and Learning· 0 citations· 18 references
TL;DR
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.
Abstract
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.
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.
Ignatius Ogbaga, U. Onwudebelu, Nathaniel Akwuma et al.· Systems and Computing· 0 citations
Background: GenAI systems are now an integral element of academic life, although guidelines for research ethics are mostly based on the experiences of the NorthAmerican and European countries. There is not much information available about the ethical issues related to AI that researchers face in Tunisia as well as whether their institutions have adapted to the developments.
Objective: to record the way faculty members, postgraduate students and editors in Tunisia comprehend and handle the ethical issues related to AI-created academic materials and to investigate whether the current institutional policy provides significant assistance.
Methods: eighteen participants across three institutions (ISET Kasserine, FLSH Sousse, University of Sfax) took part in semi-structured interviews, supplemented by analysis of institutional documents and observation of five research-methods and writing seminars, coded thematically using Braun and Clarke's (2006) six-phase approach.
Results: awareness of AI ethics was uneven, with only about a third of participants describing solid understanding and a fifth reporting minimal awareness; institutional documents were largely silent on AI, with the great majority of integrity codes making no mention of it; and the concern raised most often was the absence of structured training, followed by uncertainty over authorship attribution.
Conclusion: Tunisian higher education is operating in something close to a policy vacuum on AI-assisted writing. To remedy this situation, it is essential to formulate codes of ethics that incorporate the notion of so-called AI literacy into the educational process of teaching research methods and academic writing.
Mongi Aloui· Review of Artificial Intelli...· 0 citations
The expansion of generative artificial intelligence in higher education has reshaped writing, reading, research, and assessment practices, bringing classical issues of academic integrity back to the center of debate, such as plagiarism, authorship, authenticity, and intellectual responsibility. This article critically analyzes recent literature on the relationship between generative AI and academic integrity, based on a qualitative and interpretive scoping review. The study brings together contributions from critical pedagogy, technology ethics, and algorithmic culture studies, situating the discussion within the broader context of higher education platformization and the intensification of digital surveillance. The findings indicate three central axes: the transformation of notions of authorship and originality; the prevalence of institutional responses focused on control, detection, and sanction; and the emergence of pedagogical proposals that treat AI as an opportunity to rethink assignments, assessment, and formative processes. The article concludes that academic integrity cannot be reduced to rule compliance or technological surveillance, but must be understood as a constitutive dimension of educational experience, linked to intellectual honesty, responsibility, and critical formation.
Academic integrity has emerged as a critical concern in contemporary higher education due to increasing cases of plagiarism, data manipulation, contract cheating, and unethical research practices. While global frameworks emphasize codes of conduct, digital surveillance tools, and institutional policies, such approaches often remain compliance-driven rather than value-driven. In contrast, the Indian Knowledge System (IKS) offers a deeply rooted ethical framework that integrates moral conduct, self-discipline, and knowledge responsibility as essential dimensions of education.
This paper reinterprets indigenous ethical principles—such as Satya (truthfulness), Dharma (righteous conduct), Āchāra (ethical behavior), Svādhyāya (self-learning), and the Guru–Shishya tradition—as foundational pillars for strengthening academic integrity in higher education. It argues that academic honesty is not merely a regulatory requirement but a moral and spiritual commitment toward knowledge creation and dissemination.
By adopting a conceptual and critical approach, the study integrates philosophical insights from IKS with contemporary debates on research ethics and higher education governance. It further proposes a conceptual framework that aligns indigenous ethical values with modern academic practices. The paper concludes that embedding value-based education inspired by IKS can significantly enhance ethical awareness, reduce academic misconduct, and foster responsible scholarship in global higher education contexts.
Shrutika Sahu Shrutika Sahu· International Journal of Sci...· 0 citations
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
Ubiquitous conversations in the literature and media around AI ethics will benefit from rigorous examination of the terminology used. Terms such as ethics, integrity, authorship, and fairness are abstract constructions, which means we each bring our unique interpretations to these discussions. The practical conceptual framework offered in this paper addresses this diversity of interpretations directly by proposing a research-based approach to make the abstract concrete, metaphorically speaking, by putting the concepts into a wheelbarrow so we can push them around more successfully in the context of the higher education institutional and classroom settings. Five enduring concerns are identified and organized into a framework that proposes specific areas that can become at risk in the formation of a learner when using AI: truth, integrity, justice, independence, and responsibility. Four conceptual metaphors are provided that transform abstract concepts into accessible, practice-oriented principles drawn from research in AI-mediated communication (AI-MC), learner development, self-determination theory, epistemic trust, and institutional design. Implications are offered for student practice toward self-authorship and program completion in the age of AI, faculty pedagogy, institutional design, and a companion Part II theory-building integrative review is referenced.
Janet L Hanson· International Journal of Lea...· 0 citations