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Conference Open access Jul 2026

A Critical Analysis of Generative AI in Higher Education: Benefits, Challenges, and Future Directions

With the rapid development of artificial intelligence (AI), generative artificial intelligence (GenAI) has been widely applied in higher education, specifically bringing both opportunities and potential challenges. This review focuses on the application of GenAI in teaching, learning, assessment and institutional governance within the higher education context. By adopting a literature review approach, this paper reviews and analyses research on GenAI in education. The findings reveal that GenAI can effectively improve teaching efficacy, enable personalised learning experiences, and streamline assessment procedures. However, its implementation also draws attention to concerns regarding academic integrity, data privacy, algorithmic bias, and ethical governance. In accordance, higher education institutions should strengthen AI literacy training for faculty and students, while improving institutional guidelines and establishing responsible governance mechanisms. Future research is recommended to conduct longitudinal investigations, cross-cultural comparative analyses, and in-depth studies on teacher professional development, in order to support the sustainable and long-term integration of GenAI into higher education.

Xi Bi · 0 citations
Review Open access Jul 2026

ACADEMIC INTEGRITY IN THE ERA OF GENERATIVE AI: AUTHORSHIP, AUTHENTICITY AND FORMATIVE EXPERIENCE IN HIGHER EDUCATION

It is concluded that academic integrity cannot be reduced to rule compliance or technological surveillance, but must be understood as a constitutive dimension of educational experience, linked to intellectual honesty, responsibility, and critical formation.

Messias José dos Santos · 0 citations
Review Open access Aug 2026

Examining Generative AI Disruption: A Repeated Cross-Sectional Study of Faculty and Staff Sensemaking in Higher Education

The findings suggest that faculty and staff discourse shifted from primarily containing GenAI-related risks toward selectively integrating the technology into teaching and professional practice, and strategies for balancing innovation, integrity, equity, and the human purposes of higher education are identified.

T. Balart, Gibin Raju, Kristi J. Shryock · 0 citations
Open access Jul 2026

From production to verification: generative AI, doctoral formation, and the leadership of digital education

Generative artificial intelligence is often framed in higher education as a problem of academic integrity, assessment security, or technology adoption. This framing is necessary but insufficient for doctoral education, where writing, reading, coding, synthesizing literature, and interpreting evidence are not merely academic tasks but formative practices through which students become scholars. Based on qualitative interviews with twenty-one doctoral students at a large research university in the United States, this study examines how doctoral students understand and negotiate generative AI in their scholarly work. The study began with students in education and was extended through purposive and snowball recruitment to include students across a range of other disciplines, so that the account would reflect more than one scholarly context; interviews were semi-structured. The findings show that AI functions as an access infrastructure, lowering linguistic barriers for some students and technical barriers for others depending on the demands of their scholarly work. At the same time, students engage in careful boundary work between assistance and authorship, distinguishing grammar support, translation, coding help, and conceptual orientation from intellectual substitution. The analysis further suggests that, among these participants, generative AI is shifting doctoral labor from production toward verification: students' distinctive responsibility increasingly lies in judging the accuracy, legitimacy, ownership, and defensibility of machine-assisted work. Under conditions of policy ambiguity, doctoral students also become primary governors of their own AI use, managing disclosure, caution, verification, and risk. The article argues that the leadership of digital education should move beyond broad AI policies toward context-sensitive guidance, verification literacy, transparent disclosure norms, and process-based assessment, including the culminating site of doctoral assessment, the dissertation defense. These claims are offered as analytic propositions grounded in a single-site interpretive study rather than as generalizable findings. Generative AI has not made doctoral education less necessary; it has made its purposes more urgent.

Evelyn Wu · 1 citation

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