Higher-order thinking skills (HOTS) are important for students to improve their ability to analyze, solve problems, and use critical thinking. This research aims to measure the use of flipped learning to enhance students’ higher-order thinking skills. The scaffolding, questioning, interflow, reflection, and comparison (SQIRC)-based flipped learning model is used in this research. It is a combination of online and face-to-face learning that provides opportunities for students to be more active and independent in learning. This model can improve students’ critical thinking skills, as seen from learning outcomes. This research is a quasi-experimental study using 43 students in the Introduction to Accounting course, divided into a control group and an experimental group. In the Introduction to Accounting course, HOTS is essential because this course emphasizes theory and requires the application of the theory in solving problems in accounting records. The results found that implementing the SQIRC-based flipped learning model increased student learning outcomes from pre-test to post-test, and the learning outcomes of the experimental group were higher than those of the control group.

This article aims to establish a research map of the flipped classroom (FC) model in general education for the period 2014-2024, exploring publication trends, influential authors, organizations, countries, and prominent research topics, while also identifying academic gaps in this field. The study focuses on three main aspects: (1) publication trends and influential authors, organizations, and countries; (2) key research topics and academic gaps; (3) international collaboration networks in FC research. The research employs a bibliometric analysis method, utilizing the Scopus database and the VOSviewer visualization tool, to synthesize information and identify research trends. The results indicate that research on FC in K-12 education increased sharply from 2019, reflecting the impact of digital transformation in education during and after Covid-19. The United States, Hong Kong, and Taiwan are the leading research centers. Authors such as Bergmann, Bishop, and Hew have been highly influential. Prominent research trends include self-regulated learning, learner satisfaction, gamification, and the application of artificial intelligence. The international collaboration network in this field is growing, with strong participation from institutions from Southeast Asia, including Vietnam. The study recommends expanding the scope of analysis beyond Scopus and using qualitative methods and systematic reviews to further evaluate the FC model. The research will provide policymakers, teachers, and researchers with useful evidence for improving programs, enhancing professional development, and promoting digital transformation in general 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.

The strategies students use in their learning processes are crucial to their academic success, particularly in complex subjects like science. Students' preferred learning strategies can significantly influence their academic performance as they navigate scientific concepts. This study examined the relationship between academic achievement and the utilization of deep and surface learning strategies among high school students at a private institution in Carmen, Cebu, Philippines. It seeks to understand how these strategies influence science learning and suggests teaching practices to promote a practical learning experience. A mixed-method approach was employed, integrating quantitative data from a standardized questionnaire with qualitative insights from student interviews. A sample of 101 students was selected from 136 participants, determined using RAOSOFT, ensuring a 95% confidence level and a 5% margin of error. The study utilized the Revised Two-Factor Study Process Questionnaire (R-SPQ-2F) to assess learning strategies. Findings showed that high-performing students preferred deep learning strategies, though the correlation with performance was weak. Moderate academic performers used both strategies, while low performers relied on surface learning, which was linked to lower achievement. Promoting deep learning strategies alongside surface learning, especially for lower-performing students, is recommended to foster curiosity and motivation to improve academic performance and achieve long-term success.

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.

This study aims to investigate the effect of digital leadership, creativity, and proactive personality on innovative work behavior (IWB) through work engagement. The research involved 436 private school teachers in Indonesia. The research data were collected by a Likert scale questionnaire. Data analysis is done using structural equation modeling based on Partial Least Squares (SEM-PLS), with descriptive and correlational analysis as supplements. The findings indicated that digital leadership, creativity, proactive personality, and work engagement affect teacher IWB; digital leadership, creativity, and proactive personality impact teacher work engagement; and digital leadership, creativity, and proactive personality affect teacher IWB through work engagement. These findings introduce a new empirical model of the influence of digital leadership, creativity, and proactive personalities on teachers' IWB through work engagement. This model deserves to be discussed critically and in-depth, and adapted and adopted by researchers to enrich their future research. This model can also be modified and adopted by school practitioners (management) to accelerate and optimize teachers' IWB capacity based on digital leadership, creativity, proactive personality, and work engagement.