International Journal of Technology and Applied Science

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Academic Integrity and Misconduct Risks Associated with GAI in Higher Education

Author(s) Bernardo Corona Domínguez, Salvador González Flores
Country Mexico
Abstract Generative artificial intelligence (GAI) has emerged as one of the most disruptive technologies in higher education, transforming how students learn, write, research, and complete assessments. While GAI offers significant academic benefits, including improved access to information, writing support, personalized assistance, and productivity enhancement, its rapid use in higher education has generated serious concerns regarding academic integrity and misconduct. The ability of GAI tools to produce essays, summaries, code, answers, and other forms of academic content has challenged long-standing assumptions about authorship, originality, independent learning, and fair assessment. This article examines the academic integrity and misconduct risks associated with GAI in higher education. Using a narrative literature review approach, the study synthesizes recent scholarship on institutional policies, student behaviors, misconduct patterns, personality predictors, ethical concerns, and preventive strategies related to GAI use in academic contexts. The review finds that GAI-related misconduct is not limited to plagiarism but includes unauthorized assistance, concealed authorship, fabrication, contract-like substitution of academic labor, manipulation of assessments, and misuse of AI-generated content in research and publication. The findings further show that misconduct risks are shaped by institutional ambiguity, weak policy enforcement, assessment design flaws, student perceptions, personality traits, and uneven AI literacy. The article argues that academic integrity in the age of GAI must be addressed through a comprehensive framework that combines authentic assessment, clear governance policies, ethical literacy, early-warning systems, due process, and context-sensitive enforcement mechanisms. It concludes that higher education institutions must move beyond narrow anti-cheating responses and adopt a broader academic integrity strategy that recognizes the complexity of GAI use while preserving fairness, originality, trust, and educational purpose.
Keywords Generative artificial intelligence, academic integrity, academic misconduct, higher education, assessment, educational policy.
Published In Volume 17, Issue 3, Array 2026
Published On 2026-03-27
Cite This Academic Integrity and Misconduct Risks Associated with GAI in Higher Education - Bernardo Corona Domínguez, Salvador González Flores - IJTAS Volume 17, Issue 3, Array 2026. DOI 10.71097/IJTAS.v17.i3.1228
DOI https://doi.org/10.71097/IJTAS.v17.i3.1228
Short DOI https://doi.org/hbtzzx
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