Jul 2026· International Journal of Studies in Education and Science· Vol 7, pp. 397-420· 0 citations
TL;DR
Examination of student and faculty perceptions of generative AI, institutional responses to AI-related integrity challenges, and the effectiveness of AI detection tools suggests that AI detection tools remain unreliable as standalone mechanisms, with persistent risks of false positives and false negatives.
Abstract
Generative artificial intelligence (AI) tools such as ChatGPT are increasingly shaping teaching, learning, and assessment in higher education, raising critical concerns about academic integrity, authorship, and ethical use. This study synthesizes existing research to examine student and faculty perceptions of generative AI, institutional responses to AI-related integrity challenges, and the effectiveness of AI detection tools. A narrative literature review was conducted, analyzing 24 peer-reviewed studies published between 2022 and 2024 using thematic synthesis. The findings indicate that students often view AI tools as helpful learning supports and frequently use them without a clear understanding of ethical boundaries or disclosure expectations. Faculty members report growing difficulty verifying student-authored work, citing overreliance on AI-generated content and inconsistent performance of detection technologies. Institutional responses vary widely, ranging from restrictive bans to conditional integration supported by ethical guidelines and AI literacy initiatives. Evidence suggests that AI detection tools remain unreliable as standalone mechanisms, with persistent risks of false positives and false negatives. Overall, the review highlights that scholarship in this area remains exploratory and context-dependent. Clearer academic integrity frameworks, improved assessment design, and sustained AI literacy efforts are needed to support responsible AI integration while preserving core principles of academic integrity in higher education.
The findings suggest that academic integrity in the age of artificial intelligence (AI) cannot be focused solely on preventing fraud, and this needs to expand to support ethical digital literacy, redesign learning tasks that require human reasoning, and ensure fairness in automated decision-making systems.
W. Phornprasert, W. Nuankaew, Pratya Nuankaew· International Journal of Adv...· 0 citations
Artificial Intelligence should be viewed as an assistive technology that complements rather than replaces human expertise in teacher education research, and the implications for research quality, reliability, equity, and public trust in educational research are highlighted.
Dr Yudhvir Singh and Dr Geetu Gupta· International Journal of Adv...· 0 citations
Current practices related to AI use are examined, focusing on LLM-based ghostwriting and the reliability of disclosed interactions as evidence of authentic use, and the possibility of mimicking authentic interactions, which raises concerns about the effectiveness of current approaches.
It is suggested that AI can enhance drafting, revision, and feedback processes, improving coherence, metacognition, and writing confidence, however, these benefits are accompanied by persistent concerns regarding ethical ambiguity, inconsistent policy guidance, and insufficient faculty training.
Samira Dichari, Fadi Jaber· Journal of Education and Tra...· 0 citations
This document outlines the conceptual, theoretical, and methodological underpinning of the AI-Augmented Pedagogy Integration Model (AAPIM), which has now been further supported by a growing evidence base of 2025–2026 meta-analyses and systematic reviews.
Ahnaf Afsin, Rumaysha Tahan Towaa, Kasif Suhail Ayate et al.· Frontiers in Computer Scienc...· 0 citations
The rapid adoption of generative artificial intelligence (GenAI) has created a practical problem for virtual higher education: universities must distinguish legitimate AI-supported learning from undisclosed delegation of academic work, while maintaining valid, fair, and privacy-sensitive assessment. This study compared student and faculty perceptions of AI use and academic integrity at the Technical University of Machala, Ecuador. A descriptive-comparative cross-sectional survey was administered to 1660 students and 34 faculty members during the second academic semester of 2024. The student questionnaire examined AI-use frequency, perceived academic benefit, readiness for non-assisted assessment, observation of dishonest online practices, perceived efficacy of virtual assessment, and attitudes toward proctoring. The faculty questionnaire examined suspected AI-generated submissions, responses to suspected use, perceived assessment efficacy, training, control tools, ethical judgments and proctoring. Findings indicate a transitional integrity landscape: students view AI mainly as a useful academic support, whereas faculty interpret it primarily through authorship, evidence and assessment-security concerns. Both groups report limitations in current virtual assessment, suggesting the need for AI-resilient assessment design, explicit disclosure rules, faculty development, student AI literacy, and proportional use of proctoring. The article argues against both blanket prohibition and permissive ambiguity, proposing a governance model grounded in transparent policy, authentic assessment, due process and human-centered AI literacy.
Héctor Carvajal, Fernanda Tusa, Rosemary Samaniego et al.· Trends in Higher Education· 0 citations