Education based on social entrepreneurship (SE) practices is of importance at this time to shape the personality of students who are more responsible towards the surrounding environment. This SE requires high motivation from students to ensure success in education based on social entrepreneurship can be achieved. However, the factors that support the motivation for social entrepreneurship are still poorly identified, particularly in the setting of undergraduate students in Malaysia. Data were collected from 15 selected Malaysian universities involving undergraduate students who are actively involved in the Enactus program. A set of questionnaires was administered to 294 respondents online. The data analysis involved confirmatory factor analysis (CFA) to measure the construct validity of the measurement model, and covariance-based structural equation modelling (CB-SEM) to establish the relationship between the independent variables and dependent variables. The results revealed self-efficacy and entrepreneurship education provide a relationship in motivation toward social entrepreneurship by undergraduate students. However, social support does not relate to motivation toward social entrepreneurship. Overall, this study adds to the notion of factors that influence social entrepreneurship motivation by supplementing the literature in the areas of educational management and entrepreneurship. In practice, this study contributes significantly to the formation of government policies to further strengthen the motivation of social entrepreneurship that can enhance the community economy and local communities.

This article examines the influence of the university environment and learning programs on students' entrepreneurial intentions and attitudes, considering the moderating roles of entrepreneurial self-efficacy and locus of control. The empirical analysis employs a multilevel (hierarchical) linear model, utilizing responses from 713 students across 30 universities in Kazakhstan who participated in the GUESSS 2021 project survey. Our findings reveal that students' entrepreneurial intentions are directly influenced by entrepreneurial self-efficacy and locus of control. However, the locus of control is also indirectly influenced by the university environment and learning program. The learning program's effect on both entrepreneurial aspirations and attitudes is mediated by self-efficacy. Locus of control, conversely, negatively affects both entrepreneurial attitudes and intentions in program learning. The study's results underscore that student entrepreneurship is shaped by personal factors such as self-efficacy and locus of control, alongside the university context. Interestingly, the findings also indicate interdependencies between these factors, further influencing students' entrepreneurial intentions and attitudes.

Artificial intelligence (AI) has revolutionized higher education. The rapid adoption of artificial intelligence in education (AIED) tools has significantly transformed educational management, specifically in self-directed learning (SDL). This study examines the factors influencing Indonesian higher education students' intention to adopt AIED tools for self-directed learning using a combination of the Theory of Planned Behavior (TPB) with additional theories. A total of 322 university students from diverse academic backgrounds participated in the structured survey. This study utilized machine learning it was Artificial Neural Networks (ANN) to analyze nine factors, including attitude (AT), subjective norms (SN), perceived behavioral control (PBC), optimism (OP), user innovativeness (UI), perceived usefulness (PUF), facilitating conditions (FC), perception towards ai (PTA), and intention (IT) with a total of 41 items in the questionnaire. The model demonstrated high predictive accuracy, with SN emerging as the most significant factor to IT, followed by AT, PBC, PUF, FC, OP, and PTA. User innovativeness was the least influential factor due to the lowest accuracy. This study provides actionable insights for educators, policymakers, and technology developers by highlighting the critical roles of social influence, supportive infrastructure, and student beliefs in shaping AIED adoption for self-directed learning (SDL). This research not only fills an important gap in the literature but also offers a roadmap for designing inclusive, student-centered AI learning environments that empower learners and support the future of SDL in digital education.

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.