This study examines how Chinese and Western news media covered artificial intelligence (AI) privacy issues in higher education from 2019 to 2024. News articles were retrieved from Nexis Uni. First, non-negative matrix factorization (NMF) was employed to identify core AI privacy topics in university teaching, administration, and research. Next, a time trend analysis investigated how media attention shifted in relation to key events, including the COVID-19 pandemic and the emergence of generative AI. Finally, a sentiment analysis was conducted to compare the distribution of positive, negative, and neutral reporting. The findings indicate that AI-driven proctoring, student data security, and institutional governance are central concerns in both Chinese and English media. However, the focus and framing differ: some Western outlets highlight individual privacy rights and controversies in remote exam monitoring, while Chinese coverage more frequently addresses AI-driven educational innovation and policy support. The shift to remote education after 2020 and the rise of generative AI from 2023 onward have intensified discussions on AI privacy in higher education. The results offer a cross-cultural perspective for institutions seeking to reconcile the adoption of AI with robust privacy safeguards and provide a foundation for future data governance frameworks under diverse regulatory environments.

The continuous evolution of technology compels higher education leaders to adapt to VUCA (volatile, uncertain, complex, and ambiguous) and BANI (brittle, anxious, non-linear, and incomprehensible) environments through innovative strategies that ensure institutional relevance. While VUCA emphasizes the challenges posed by rapid change and uncertain decision-making, BANI underscores the fragility of systems, heightened anxiety, unpredictable causality, and the collapse of established patterns. Navigating these complexities requires agility, resilience, and visionary leadership to ensure that institutions remain adaptable and future ready. This study presents a bibliometric analysis of digital academic leadership in higher education transformation, examining empirical studies, reviews, book chapters, and proceeding papers published from 2014 to 2024 (11-year period) in the Web of Science—Science Citation Index Expanded (SCIE) and Social Science Citation Index (SSCI). Using CiteSpace software (version 6.3. R1-64 bit), we analyzed 5837 documents, identifying 24 key publications that formed a network of 90 nodes and 256 links. The reduction to 24 publications occurred as part of a structured bibliometric analysis using CiteSpace, which employs algorithmic thresholds to identify the most influential and structurally significant publications within a large corpus. These 24 documents form the core co-citation network, which serves as a conceptual backbone for further thematic interpretation. This was the result of a multi-step refinement process using CiteSpace’s default thresholds and clustering algorithms to detect the most influential nodes based on centrality, citation burst, and network clustering. Our findings reveal six primary research clusters: “Enhancing Academic Performance”, “Digital Leadership Scale Adaptation”, “Construction Industry”, “Innovative Work Behavior”, “Development Business Strategy”, and “Education.” The analysis demonstrates a significant increase in publications over the decade, with the highest concentration in 2024, reflecting growing scholarly interest in this field. Keywords analysis shows “digital leadership”, “digital transformation”, “performance”, and “innovation” as dominant terms, highlighting the field’s evolution from technology-focused approaches to holistic leadership frameworks. Geographical analysis reveals significant contributions from Pakistan, Ireland, and India, indicating valuable insights emerging from diverse global contexts. These findings suggest that effective digital academic leadership requires not only technical competencies but also transformational capabilities, communication skills, and innovation management to enhance student outcomes and institutional performance in an increasingly digitalized educational landscape.

The rapid development of Generative Artificial Intelligence (GenAI) in graduate education has changed human–AI interaction within knowledge-intensive environments, leading to important questions about user-side cognitive adaptation in probabilistic AI systems. While many studies focus on ethical implications, limited attention has been paid to the cognitive mechanisms underlying graduate students’ adoption of GenAI. Drawing on the Technology Acceptance Model (TAM), this study explores the cognitive and interactional mechanisms shaping graduate students’ adoption and usage of GenAI. Using thematic analysis of in-depth interviews with 20 graduate students from diverse academic backgrounds, the study identifies seven interrelated constructs: perceived usefulness, perceived ease of use, external environment, risk perception, attitude, behavioral intention, and interaction subjectivity. This study demonstrates that the adoption of GenAI is not merely a result of perceived efficiency but is shaped by cognitive calibration between trust and risk evaluation. Moreover, interaction subjectivity emerges as a metacognitive factor that determines whether engagement results in human–AI collaboration or passive automation. By integrating external environment, risk perception, and interaction subjectivity, this study provides a cognitively grounded framework for understanding human–AI adoption and interaction dynamics. Practically, the findings provide design-relevant insights for developing GenAI systems that support calibrated trust, uncertainty awareness, and adaptive cognitive participation.