Addressing student use of generative AI in schools and universities through academic integrity reporting

The aim of this theoretical article is to explore frameworks contributing to reasonable and applicable definitions of artificial intelligence (AI), human intelligence, and generative artificial intelligence (GenAI), as well as propose a framework for an efficient, transparent, and scalable approach to assess and address inappropriate use of GenAI resources in an academic environment.The integration of artificial intelligence technologies in academic environments has transformed how students engage with learning materials, complete assignments, and demonstrate formative knowledge. The opportunities AI-enhanced resources offer for personalized learning and robust academic experiences are comingled with significant challenges related to appropriate student use. Although automated content generators can enhance productivity and understanding, they also raise complex questions about academic integrity and the role educators must play in shaping its ethical use. AI-powered tools provide students with grammar correction, citation management, language translation, research abstracts, and a wide-range of enhanced learning support when used appropriately (Dwivendi et al., 2021). These tools also assist non-native speaking students with accessibility to learning modalities throughout the world (Karakas, 2023). It is when these tools are used to circumvent academic effort that concerns quickly emerge. Large language models (LLMs) capable of generating human-quality text and creative content have blurred the lines of authorship and academic integrity. Students have unprecedented access to resources capable of completing assignments, writing essays, and solving complex problems with minimal personal effort. This raises strong opinion about the assessment of student learning (Luo, 2024), determination of plagiarism (Bittle et al., 2025), and efficacy of performance outcomes (Weng et al., 2024).Luo's critical policy analysis published in 2024 examined the institutional frameworks guiding the use of GenAI in assessment at twenty world-leading universities. Employing Bacchi's "What's the problem represented to be" (WPR) methodology, the research sought to critically analyze how these institutions articulate the challenges posed by GenAI within an evolving academic landscape.The core critique identified a dominant, nearly universal policy paradigm that frames GenAI as a potential threat to academic integrity and the intellectual originality of student submissions. By designating these resources as a form of external assistance separate from the student's contribution, this prevailing policy structure suggests a critical silence regarding the increasingly distributed and collaborative nature of modern, technology-mediated knowledge production (Luo, 2024). This research suggests a redefinition of "originality" to effectively accommodate and integrate human-AI collaborative endeavors.The potential effects of these prevailing integrity-focused policies are significant: they risk stigmatizing students utilizing GenAI resources for legitimate purposes and may transform faculty into punitive "gatekeepers" whose focus is policing misconduct rather than empowering robust student learning experiences (Luo, 2024). Notwithstanding its theoretical utility, Luo's study exhibits key research gaps. A primary limitation is the absence of empirical data concerning the lived effects of the policies, specifically lacking both student perspectives on their resultant impact and real-world faculty implementation data across varied disciplines. This reliance on a limited, purposed sample of elite universities undermines the generalizability of findings, neglecting distinct policy challenges faced by diverse institutional types, such as community colleges, proprietary and non-profit institutions. Finally, a key absence is the inclusion of alternative policy models, specifically in-depth case studies on institutions that have successfully moved beyond the punitive model to implement progressive, integrated GenAI policies.A systematic literature review was conducted to effectively assess how GenAI technologies influence the demonstration of formative knowledge, balancing both educational benefits and associated risks to academic honesty (Bittle et al., 2025). This comprehensive analysis encompassed 41 studies gathered from key databases, including the IEEE Xplore and JSTOR.Core findings suggest a profound impact of GenAI resources in higher education environments. The opportunities AI-enhanced resources offer for customized learning and robust educational engagement are comingled with significant challenges related to appropriate student use. Large language models capable of generating human-quality assignments and creative content have blurred the lines of authorship, enabling the evasion of conventional plagiarism tools and raising complex questions about academic integrity. This review suggests that immediate actions are required to effectively manage GenAI's influence. This primarily involves enhancing digital literacy among both students and faculty, and developing more robust detection tools that can assess and address AI-generated content. 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