2026· International Journal of Advanced Computer Science and Applications· 0 citations· 53 references
TL;DR
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.
Abstract
This study examines how Artificial Intelligence (AI) is transforming academic integrity in higher education, altering both learning opportunities and the risks associated with misconduct. As creative AI tools become embedded in everyday academic work, they provide valuable support for writing, research assistance, and skills development. Still, they also challenge long-held assumptions about authorship, originality, and assessment. Emerging evidence suggests that students are using AI in a variety of ways, from supporting legitimate learning to producing fully automated assignments. However, AI-driven integrity technologies, such as plagiarism detectors and authorship checking models, are becoming more effective but continue to face issues of bias, false positives, and limited transparency. This rapid shift has created a gap between technological change and academic readiness, highlighting the need for institutions to rethink assessment design, improve integrity frameworks, and foster a culture of responsible AI use, rather than relying solely on surveillance and sanctions. This review compiles the latest studies published between 2020 and 2025 to map current practices, risks, and policy responses. The findings suggest that academic integrity in the age of artificial intelligence (AI) cannot be focused solely on preventing fraud. But this needs to expand to support ethical digital literacy, redesign learning tasks that require human reasoning, and ensure fairness in automated decision-making systems. The study concludes with recommendations for educators, researchers, and policymakers to balance innovation with responsibility to ensure that AI becomes a tool for transforming learning, rather than a threat to academic values.
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.
Promethi Das Deep· International Journal of Stu...· 0 citations
Large language models (LLMs) have introduced new challenges to academic integrity, particularly regarding the appropriation of AI-generated outputs as original human authorship and the difficulty of verifying independent work. While some universities and academic publishers increasingly require explicit disclosure of the use of artificial intelligence (AI), the scope and implementation of these requirements remain inconsistent. This paper examines current practices related to AI use, focusing on LLM-based ghostwriting and the reliability of disclosed interactions as evidence of authentic use. The study includes an experimental component involving AI-assisted essay generation, highlighting practical and ethical dilemmas associated with academic integrity. It further explores the possibility of mimicking authentic interactions, which raises concerns about the effectiveness of current approaches. To investigate these questions, a survey was conducted among teaching staff at the Faculty of Computer Science and Engineering (FCSE) in Skopje to assess their ability to identify AI-generated essays and their trust in disclosed interactions. Among the 28 respondents, a majority (82.14%) indicated that it is possible to identify AI-generated content based solely on language style, while 64.29% reported detecting linguistic inconsistencies that could result from the use of LLMs. Despite noticing AI-related linguistic markers, only 53.57% concluded that the essay was not human-written. This view was shared by just 27.27% of assistants, compared to 70.59% of professors, whose extensive experience appeared to help them recognize that a substantial portion of the text had been AI-generated. The findings are discussed in the context of teaching experience and existing policies, leading to recommendations for improving student assessment and strengthening the ethical use of generative artificial intelligence (GenAI).
Artificial Intelligence (AI) has emerged as a transformative technology that is reshaping educational research, particularly within the field of teacher education. AI-powered tools such as machine learning, natural language processing, predictive analytics, and generative AI have significantly enhanced researchers' ability to collect, analyze, interpret, and present data with greater speed and accuracy. These technologies facilitate literature reviews, automate qualitative and quantitative data analysis, improve academic writing, and support evidence-based decision-making. Despite these benefits, the increasing integration of AI into teacher education research raises significant ethical, methodological, and professional concerns that require careful consideration.
This paper critically examines the major challenges and ethical issues associated with the use of AI in teacher education research. Key concerns include algorithmic bias, data privacy and security, transparency and explainability of AI systems, academic integrity, plagiarism, authorship, misinformation generated by AI, overdependence on automated tools, digital inequality, intellectual property rights, and the potential erosion of researchers' critical thinking and analytical skills. The paper also highlights the implications of these challenges for research quality, reliability, equity, and public trust in educational research. Furthermore, it discusses the necessity of establishing ethical guidelines, promoting AI literacy among researchers and teacher educators, ensuring human oversight, strengthening data governance, and adopting responsible AI practices aligned with principles of fairness, accountability, transparency, and inclusiveness.
The study concludes that AI should be viewed as an assistive technology that complements rather than replaces human expertise in teacher education research. Responsible and ethical integration of AI requires collaborative efforts among researchers, educational institutions, policymakers, technology developers, and ethics committees to ensure that AI contributes to innovative, credible, and socially responsible educational research. By fostering ethical awareness and implementing robust governance frameworks, teacher education institutions can harness the benefits of AI while safeguarding academic integrity, research quality, and the broader goals of equitable and sustainable educational development
Dr Yudhvir Singh and Dr Geetu Gupta· International Journal of Adv...· 0 citations
The transformative opportunities and challenges of generative AI tools, especially large language models (LLMs) such as ChatGPT, have emerged rapidly in higher education. The adoption of generative AI in undergraduate education has increased dramatically in 2024–25, whereas institutions' pedagogical approaches, institutional policies, and evidence of learning outcomes are less developed than their use. Previous research has primarily focused on the short-term acceptance of the tools or single academic integrity issues without considering them in relation to each other. Recent systematic reviews and meta-analyses (2025–2026) have begun to measure the impact of GenAI on learning outcomes and trace authorship and integrity concerns in greater detail. However, few studies have systematically investigated the impact of GenAI on learning outcomes, pedagogical design, and academic integrity across a variety of learner populations. This study will explore (1) the observable effects of AI-supported personalised learning on student learning outcomes at the undergraduate level; (2) faculty and student attitudes towards the impact of AI on pedagogical redesign; and (3) institutional policies to ensure academic integrity while supporting and facilitating AI-supported learning. The proposed convergent mixed methods design will involve validation of the survey instrument (n = 420 undergraduate and faculty students across three institutions) and a quasi-experimental pre-post assessment study. Structural equation modelling (SEM) and thematic coding of qualitative data were used in the planned analysis. This study aims to produce a theoretically sound, testable framework that will benefit evidence-based strategies for the adoption of AI in higher education, the AI-Augmented Pedagogy Integration Model (AAPIM). This document outlines the conceptual, theoretical, and methodological underpinning, which has now been further supported by a growing evidence base of 2025–2026 meta-analyses and systematic reviews, with empirical data reported when the data collection is complete
Ahnaf Afsin, Rumaysha Tahan Towaa, Kasif Suhail Ayate et al.· Frontiers in Computer Scienc...· 0 citations
The public release of ChatGPT in late 2022, and the wave of generative artificial intelligence (GenAI) tools that followed it, has unsettled long-standing assumptions about how learning is demonstrated and assessed in higher education. Essays, reports, code, and even reflective writing can now be produced in seconds by systems whose output is fluent, personalised, and largely indistinguishable from student work. This paper examines the resulting collision between GenAI and academic integrity from a governance and assurance perspective. Drawing on the rapidly growing literature published since 2022, it maps the principal challenges facing institutions: the unreliability and demonstrated bias of AI-text detection tools, the erosion of assessment validity, widening equity gaps, fragmented and reactive policy, limited faculty capacity, and the contamination of scholarly work by fabricated references and hallucinated content. The paper argues that detection-centred enforcement is a structurally weak control and proposes instead a layered institutional framework in which policy and governance, pedagogy and assessment redesign, and technology-based assurance operate as mutually reinforcing controls, sustained by a continuous audit and improvement cycle. Future directions are discussed, including two-lane assessment models, AI literacy as a graduate attribute, provenance and watermarking infrastructure, and the emergence of academic integrity as an auditable domain of institutional risk management
Dr. G. Purushothaman, Dr. S. Ganapathy, Mr. Saurabh Jaiswal, Mr. Thanga Kumaran M· International Journal of Adv...· 0 citations
The increasingly rapid digital transformation has encouraged the integration of Artificial Intelligence (AI) in various aspects of education, including the learning process, assessment and preparation of academic assignments. The presence of generative AI technology provides various benefits, such as increasing learning efficiency, facilitating access to information, and supporting the development of thinking skills. The use of AI also poses serious challenges to academic integrity. This research aims to examine the various challenges posed by the use of AI on educational integrity and identify strategies that can be implemented to maintain the values of academic honesty in the era of digital transformation. The method used is Systematic Literature Review (SLR) by reviewing various scientific articles, research reports and relevant academic publications regarding AI and academic integrity. The results of the study show that the use of AI has the potential to increase the practice of plagiarism, technology dependence, manipulation of academic assignments, and difficulties in detecting the authenticity of student work. The development of generative AI has changed the learning evaluation paradigm so that educational institutions need to adjust their policies and assessment methods. However, AI can also be used ethically as a learning support tool if it is accompanied by clear regulations, adequate digital literacy, and strengthening a culture of academic integrity. This research concludes that the main challenge lies not in AI technology itself, but in how educational institutions manage its use responsibly. Collaboration is needed between educators, students, policy makers and educational institutions in formulating guidelines for the use of AI that balance technological innovation and maintaining academic integrity
I. J. Dewanto, H. Basri, Zulfitri et al.· International Journal of Sus...· 0 citations