Psychological Determinants of Academic Integrity in the Use of Generative AI in Higher Education

Empowering the AI-Literate Learners: Evaluating the Intersection of Digital Governance and Academic Integrity

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.

Navigating generative AI in higher education: Freshers’ perceptions of ethics in AI, digital learning behavior, and institutional norms

First-year university students’ perceptions of generative AI in academic work are investigated, foregrounding student agency in a Global South context and offering pedagogical and policy implications for responsible AI adoption.

Academic Integrity in the Era of Generative AI: Students’ Perceptions, Ethical Boundaries, and Risk-Taking Behavior in Nigerian Universities

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.

Why Students Use Generative Artificial Intelligence for Essay Editing: A Configurational Method and Multidimensional Ethics Scale Perspective

Background: The rapid emergence of generative artificial intelligence (GAI) is having a substantial impact across numerous areas of higher education, including academic writing. Its use as a linguistic assistant occupies an ethically ambiguous position between legitimate academic support and practices that may foster technological dependence or compromise academic integrity. Objective: Examining the use of GAI among university students in a specific academic scenario: its use as a linguistic assistant to improve the writing quality and clarity of essays previously produced by the student, without substantially altering their content. The objective is to analyze how different ethical judgments shape students’ adoption of this practice, which occupies an intermediate position between legitimate academic support and potential risks of technological dependence or academic fraud. Methods: The study draws on the Multidimensional Ethics Scale, considering four moral dimensions: justice, relativism, consequentialism, and deontology. It also incorporates sociodemographic and academic variables, including gender, employment status, and perceived academic performance. Methodologically, fuzzy-set qualitative comparative analysis is applied to identify causal configurations associated with both the use and nonuse of GAI. Results: Acceptance does not depend on a single ethical dimension but on specific combinations of moral judgments. Consequentialism emerges as the most relevant condition in the pathways leading to use and is often combined with favorable perceptions of justice and relativism. In contrast, rejection shows a more fragmented structure and lower explanatory coverage, particularly when unfavorable ethical assessments, especially consequentialist assessments, are combined with sociodemographic factors. Conclusion: Students’ acceptance and rejection of GAI-supported essay editing follow asymmetric configurational logics, showing how different ethical judgments combine to explain academic technology use in a morally ambiguous context. The findings highlight the need for clear institutional rules, ethical training, and transparent criteria for academic GAI use. Universities should distinguish between acceptable linguistic support and practices that may compromise academic integrity, while helping students develop responsible and reflective uses of GAI.

When Thinking is Judged: Academic Ethics and the Moralization of Cognition

Understanding university students’ AI ethical decision-making in academic contexts: a SOR - social cognitive perspective

The findings advance academic integrity research by shifting attention from attitudes to scenario-based decision quality and clarifying the internalization mechanism through moral cognition.