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

From diffusion to negotiation: generative AI and academic integrity in higher education assessment

Exploring student use of generative AI in higher education: A dual-institutional study

Although a growing body of research has examined students’ attitudes toward generative artificial intelligence (GenAI) in higher education, few studies have compared perceptions across contrasting institutional contexts or explored how students’ reported uses of GenAI relate to broader learning practices. This study addresses that gap by examining university students’ perceptions, self-reported competence, and use of GenAI at two Swedish universities with different academic profiles: a technology-oriented institution and a broader multidisciplinary institution. The study is based on an exploratory questionnaire survey administered to all enrolled students at both universities, yielding 1,097 responses (University A response rate: 11.27%, University B response rate: 14.06%) from students across diverse disciplines, including engineering, nursing, and criminology. Quantitative data were analyzed using reliability analysis, exploratory factor analysis, and non-parametric group comparisons, supplemented by thematic analysis of qualitative responses. The analysis identified three reliable constructs: perceived learning benefit, perceived institutional support and integration, and self-reported technical knowledge and competence. Across both institutions, students reported generally positive attitudes toward GenAI and described using it primarily for information retrieval, text refinement, and text analysis, but also as a discussion partner or personal tutor in ways that suggest both surface-level and more dialogic forms of engagement. Comparisons between the two universities showed broad similarity across most measures, with the only statistically significant difference relating to perceived institutional support and integration, which was rated higher by students at the technology-oriented university. Students at both institutions also viewed GenAI primarily as a complement to, rather than a replacement for, traditional teaching, while reporting only moderate trust in AI-generated outputs. These findings suggest that GenAI is already embedded in students’ study practices, but that its use is largely self-directed rather than strongly shaped by institutional context. The study thus contributes comparative empirical evidence on student engagement with GenAI across contrasting higher education settings and highlights the need for pedagogical and institutional strategies that support critical, reflective, and responsible use.

Faculty Burnout at the Intersection of Artificial Intelligence Adoption and Academic Integrity Measures: A Thematic Analysis in Higher Education.

The rapid integration of Artificial Intelligence (AI) in higher education has changed teaching and assessment practices and has introduced new risks for faculty well-being. This study, conducted in 10 public and private universities in Dhaka, Bangladesh, examines faculty burnout associated with AI adoption and academic integrity enforcement. Semi-structured interviews were conducted with 37 full-time faculty members, each with more than 5 years of teaching experience. Reflexive thematic analysis identified 5 domains of burnout: workload intensification through invisible labor, technostress driven by rapid technological change, an investigative burden that moves the faculty role from pedagogy toward surveillance, ethical strain within a policy vacuum, and disruption of professional identity. Participants described AI integration as a structural intensifier of academic labor rather than a labor-saving tool. In the absence of clear governance, faculty members absorbed the cognitive, emotional, and administrative costs of institutional transitions. The findings support the need for written institutional AI policies, formal recognition of assessment redesign labor, fair and reliable misconduct procedures, and sustained professional development so that integrity enforcement does not rest on individual instructors alone. The results carry implications for university governance, national regulatory bodies, and occupational health in academic workplaces.

Empowering the AI-Literate Learners: Evaluating the Intersection of Digital Governance and Academic Integrity

It is concluded that in order for undergraduate education to continue to be relevant in a society where AI is pervasive, governance must change toward process-oriented evaluation and relational originality.

Generative AI and Academic Integrity in Higher Education: Challenges and Future Directions

It is argued that detection-centred enforcement is a structurally weak control and proposed 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.

Navigating generative AI in higher education: Freshers’ perceptions of ethics in AI, digital learning behavior, and institutional norms

First-year university students’ perceptions of generative AI in academic work are investigated, foregrounding student agency in a Global South context and offering pedagogical and policy implications for responsible AI adoption.