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Please note this is a comparison between Version 1 by Manolis Adamakis and Version 2 by Arao Fu.
Artificial Intelligence (AI), particularly Generative AI (GenAI) and Large Language Models (LLMs), is rapidly reshaping higher education by transforming teaching, learning, assessment, research, and institutional management. This entry provides a state-of-the-art, comprehensive, evidence-based synthesis of established AI applications and their implications within the higher education landscape, emphasizing mature knowledge aimed at educators, researchers, and policymakers. AI technologies now support personalized learning pathways, enhance instructional efficiency, and improve academic productivity by facilitating tasks such as automated grading, adaptive feedback, and academic writing assistance. The widespread adoption of AI tools among students and faculty members has created a critical need for AI literacy—encompassing not only technical proficiency but also critical evaluation, ethical awareness, and metacognitive engagement with AI-generated content. Key opportunities include the deployment of adaptive tutoring and real-time feedback mechanisms that tailor instruction to individual learning trajectories; automated content generation, grading assistance, and administrative workflow optimization that reduce faculty workload; and AI-driven analytics that inform curriculum design and early intervention to improve student outcomes. At the same time, AI poses challenges related to academic integrity (e.g., plagiarism and misuse of generative content), algorithmic bias and data privacy, digital divides that exacerbate inequities, and risks of “cognitive debt” whereby over-reliance on AI tools may degrade working memory, creativity, and executive function. The lack of standardized AI policies and fragmented institutional governance highlight the urgent necessity for transparent frameworks that balance technological adoption with academic values. Anchored in several foundational pillars (such as a brief description of AI higher education, AI literacy, AI tools for educators and teaching staff, ethical use of AI, and institutional integration of AI in higher education), this entry emphasizes that AI is neither a panacea nor an intrinsic threat but a “technology of selection” whose impact depends on the deliberate choices of educators, institutions, and learners. When embraced with ethical discernment and educational accountability, AI holds the potential to foster a more inclusive, efficient, and democratic future for higher education; however, its success depends on purposeful integration, balancing innovation with academic values such as integrity, creativity, and inclusivity.
- artificial intelligence literacy
- cognitive debt
- pedagogical innovation
- academic integrity policies
- generative artificial intelligence
- large language models
Artificial Intelligence (AI), especially Generative AI (GenAI) systems (i.e., systems capable of generating novel content in response to prompts) like Large Language Models (LLMs), are increasingly transforming higher education worldwide by reshaping teaching, learning, assessment, research, and institutional management. These applications introduce both new opportunities and critical challenges. Universities are exploring ways to integrate these technologies to enhance learning processes while simultaneously addressing concerns about academic integrity, algorithmic bias, tool transparency, and data protection [1][2][3][1,2,3]. While a growing body of research examines individual applications of AI in universities, the literature remains fragmented across diverse disciplinary and policy perspectives. Few works provide an integrated, state-of-the-art synthesis that consolidates established knowledge about pedagogical integrity, AI literacy, and policy integration. This entry seeks to address this gap by offering a comprehensive, evidence-based overview of mature knowledge that can serve as a reference point for educators, researchers, and policymakers engaged in shaping the future of AI-enhanced higher education.
The adoption of AI in higher education has accelerated significantly, with a wide range of applications now actively reshaping the educational landscape [4][5][4,5]. Previously published work, such as Crompton and Burke’s [6] review of AI’s potential in universities, highlight benefits such as personalized learning and enhanced instruction, while also addressing ethical concerns related to academic integrity and plagiarism detection. Although several studies, e.g., [7][8][7,8], report that AI improves learning performance and critical thinking and acknowledge the risks of over-reliance on such systems, these often rely on short-term or cross-sectional designs, limiting the ability to draw causal conclusions. Meta-analyses, e.g., [9], further suggest positive impacts of ChatGPT on academic performance, but the presence of novelty effects and lack of long-term follow-up reduce the strength of the evidence.
In addition, Ganjavi et al. [10] emphasize the urgent need for academic journals to develop explicit policies on the ethical use of GenAI in scholarly writing, advocating for greater transparency in AI-assisted authorship. Similarly, Cheng et al. [3] propose concrete recommendations for responsible AI usage in academic composition, underscoring the importance of human oversight and critical evaluation of AI-generated content.
A primary dimension of AI integration in higher education involves the practical adoption of AI technologies to enhance personalized learning, instructional efficiency, and administrative processes. An additional essential dimension of GenAI integration is student education on its ethical and effective use. Hazari [11] and Vashishth et al. [12] highlight the need for dedicated curricula focused on AI literacy, aiming to equip students with a robust understanding of both the capabilities and limitations of such technologies. Ajani et al. [13] further call on educational institutions to reform their academic programs by incorporating both theoretical and practical training in AI, ensuring institutional readiness.
Furthermore, safeguarding academic integrity remains a central concern. Jarrah et al. [14] examine the complex relationship between ChatGPT and plagiarism, urging the implementation of strict, transparent policies on the use of generative AI in academic work. These challenges, as Farahani and Ghasmi [15] also argue, necessitate well-structured governance strategies that account for the pedagogical, ethical, and social consequences of AI deployment in higher education. Additionally, AI’s impact varies markedly across disciplines, with STEM fields benefiting differently compared to humanities and social sciences. These findings underscore the necessity of careful pedagogical design, continuous human oversight, and comprehensive institutional policies to harness AI’s benefits without compromising educational values. In sum, GenAI presents a promising yet demanding technological advancement—one that calls for balanced, carefully managed integration into university environments.
Based on the above, this entry constitutes a state-of-the-art, evidence-based synthesis of current uses of AI in higher education. A state-of-the-art review tends to address more current matters in contrast to the combined retrospective and current approaches of the literature review. The review intends to offer new perspectives on an issue and highlight potential areas in need of further research [16]. By avoiding speculation or futuristic projections, it outlines established practices, institutional policies, educational applications, and pedagogical implications, serving as a reference point for educators, researchers, and policymakers alike. However, as an Encyclopedia entry, the purpose of this manuscript is not to provide exhaustive analysis of any single domain, but rather to integrate mature knowledge across pedagogy, ethics, and policy.
This entry is based on a targeted review of the recent scholarly literature, institutional reports, and policy documents published primarily between 2023 and 2025, with selective inclusion of earlier seminal works (e.g., on Intelligent Tutoring Systems) to provide historical context. Sources were identified through academic databases such as Scopus, Web of Science, and PubMed, as well as policy documents from international organizations (e.g., UNESCO and OECD). Sources were selected to provide a comprehensive, evidence-based overview of mature, evidence-based findings that illustrate established applications of AI in higher education. Emphasis was placed on well-established findings and expert consensus to inform educators, researchers, and policymakers. This approach ensures that the entry synthesizes robust and representative knowledge for reference purposes.