The present study examined how empathy (affective and cognitive), basic psychological need satisfaction (autonomy, competence, and relatedness), and demographic factors (gender and academic achievement) jointly predict prosocial behavior during emerging adulthood. Grounded in Self-Determination Theory, this research explored whether relatedness need satisfaction mediates the relationship between empathy and prosocial tendencies. A total of N=889 undergraduate students from a large public university in the southeastern United States completed self-report measures assessing empathy, psychological needs, and prosocial behavior. Path analysis revealed that affective empathy and relatedness satisfaction were significant predictors of prosocial behavior. Relatedness also partially mediated the link between empathy and helping actions. Furthermore, gender and GPA contributed to prosocial outcomes, with female students and those with higher academic achievement reporting greater prosocial tendencies. These findings suggest that fostering emotional engagement and supporting students’ psychological needs—particularly the need for relatedness—may be key mechanisms for promoting prosocial development in educational settings during the critical stage of emerging adulthood.

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.

Globalization, digitalization, and evolving national regulations have intensified the need for rigorous quality-assurance systems to secure accreditation in Higher Education Institutions (HEIs). This study asks: What theoretical contributions underpin HEI accreditation, and how have research themes evolved? Employing the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) and Bibliometric Analysis via Biblioshiny and Vantage Point, we examined 1,252 documents indexed in Scopus® (781) and Web of Science™ (471) from 2012 to May 2025. Findings delineate three production phases—Foundation Consolidation (2012–2017), Expansion and Diversification (2017–2020), and Sustained Transformation and Innovation (2020–2025)—and three thematic perspectives: (a) Teaching and Learning Quality, (b) Technology and Sustainability as Quality Catalysts, and (c) Governance, Management, and Accountability. Multiple Correspondence Analysis (MCA) identified three Motor Theme clusters—[1] Sustainable Development and Institutional Change, [2] Technological Pedagogy and Student Experience, and [3] Governance and Regulation—led by Spain, the United States, Chile, Colombia, the UK, Australia, and India. Conclusions underscore accreditation’s dual role as a strategic lever for institutional improvement and a competitive mechanism, with emerging focus on competency, e-learning, employability, machine learning, and sustainability. Future research should explore cross-border accreditation dynamics; the impact of AACSB and NAAC standards on business-school curriculum design and program quality; accreditation’s pedagogical effects; and leadership practices for effective implementation.