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.

This study examines teachers’ perceptions of assessment and their relationship with instructional practice in primary education. It examines whether teachers perceive assessment as a tool to support student learning and development or as a mechanism for ensuring educational quality through teaching evaluation and exam preparation. The research is based on a survey conducted with 396 primary school teachers in Croatia, including both lower and upper grades. Descriptive statistics, Pearson’s correlation coefficients, multiple regression analysis, and one-way ANOVA were employed to analyze the data. The findings indicate a statistically significant correlation between teachers’ perceptions of assessment and the assessment strategies they implement in the classroom. Formative assessment is widely supported, yet summative methods remain prevalent. Lower primary teachers apply formative strategy more frequently than their upper primary counterparts. Teachers with mentor status demonstrate a stronger inclination toward student-centered assessment practices and report applying them more consistently. The results highlight the prevalence of a hybrid assessment model in which teachers balance formative and summative approaches, seeking to meet institutional requirements while simultaneously supporting student development. The findings underscore the need for continuous professional development and systemic support to empower teachers in adopting assessment practices that effectively enhance student learning and elevate educational quality. Training programs should be designed to address the specific needs of primary teachers, recognizing differences between lower and upper levels, as well as subject-specific requirements in grades 5 through 8. A targeted approach would facilitate the effective integration of contemporary assessment strategies into everyday teaching, supporting student progress and educational improvement.

The emergence of artificial intelligence (AI) has transformed higher education, creating both opportunities and challenges in cultivating students’ critical thinking skills. This study integrates quantitative bibliometric analysis and qualitative systematic literature review (SLR) to map global research trends and identify how critical thinking is conceptualized, constructed, and developed in the AI era. Scopus served as the primary data source, limited to publications from 2022 to 2024, retrieved on February 8, 2025. Bibliometric analysis using Biblioshiny R and VOSviewer followed five stages—design, data collection, analysis, visualization, and interpretation—while the SLR employed a deductive thematic approach consistent with PRISMA guidelines. A total of 322 documents were analyzed bibliometrically, and 34 were included in the qualitative synthesis. Results show that Education Sciences and Cogent Education are the most productive journals, whereas Education and Information Technologies have the highest citation impact. Several influential documents and authors have shaped global discussions on AI adoption in higher education and its relationship to critical thinking. Thematic mapping identified five major research clusters: pedagogical integration, ethical and evaluative practices, technical and application-oriented AI models, institutional accountability, and socio-technical systems thinking. Conceptually, critical thinking is understood as a reflective, evaluative, and metacognitive reasoning process grounded in intellectual autonomy and ethical judgment. Across the reviewed literature, strategies for fostering critical thinking converge into three integrated approaches: ethical curriculum integration, pedagogical and assessment redesign, and reflective human–AI collaboration. Collectively, these strategies ensure that AI strengthens rather than replaces human reasoning in higher education.