This study examines how work well-being is addressed in Finnish leadership education programs. The data consist of 91 publicly available course descriptions from Finnish leadership education programs in 2023, including those for master’s degrees from universities of applied sciences, traditional university-level leadership programs, and specialist vocational qualifications in leadership and business management. The study uses content analysis to examine the role of work well-being in leadership training. The results indicate that work well-being is often linked to organizational performance and treated as a tool for achieving economic goals, with less emphasis on the inherent value of employee well-being. This instrumental approach is prevalent across the different types of leadership training programs, including those found in the universities of applied sciences, traditional universities, and programs for specialist vocational qualifications in leadership and business management. The study also finds that leadership training programs often emphasize self-leadership and personal development, which can perpetuate a culture of individual responsibility for well-being and may lead to superficial leadership practices. The study concludes that Finnish leadership educators should prioritize holistic approaches to work well-being in leadership training, emphasizing its intrinsic value alongside its role in organizational performance, while researchers could explore methods to integrate and evaluate these balanced perspectives in diverse educational contexts.

The rapid integration of artificial intelligence (AI) technologies into the field of higher education is causing widespread public discourse. However, existing research is fragmented and lacks systematic synthesis, which limits understanding of how college and university students adopt artificial intelligence technologies. To address this gap, we conducted a systematic review following the guidelines of the PRISMA statement, including studies from ScienceDirect, Web of Science, Scopus, PsycARTICLES, SOC INDEX, and Embase databases. A total of 5594 articles were identified in the database search; 112 articles were included in the review. The criteria for inclusion in the review were: (i) publication date; (ii) language; (iii) participants; (iv) object of research. The results of the study showed: (a) The Technology Acceptance Model and the Unified Theory of Technology Acceptance and Use are most often used to explain the AI acceptance; (b) quantitative research methods prevail; (c) AI is mainly used by students to search and process information; (d) technological factors are the most significant factors of AI acceptance; (e) gender, specialty, and country of residence influence the AI acceptance. Finally, several problems and opportunities for future research are highlighted, including problems of psychological well-being, students’ personal and academic development, and the importance of financial, educational, and social support for students in the context of widespread artificial intelligence.