Augmented Reality (AR) and Virtual Reality (VR) are poised to revolutionize education by offering immersive and interactive learning experiences. This research comprehensively evaluates the educational applications of AR and VR, specifically emphasizing their impact on student motivation, learning outcomes, engagement, and overall learning experiences. The analysis explores how AR and VR can improve student learning, knowledge retention, and skill acquisition by systematically reviewing existing the literature from diverse educational domains, including K-12 education, higher education, STEM education, professional training, and lifelong learning. Additionally, the research investigates the pivotal role of AR and VR in fostering immersive and interactive learning environments, unveiling how these technologies promote active learning, collaboration, and critical thinking through simulations and interactive experiences. The evaluation considers the potential of AR and VR beyond traditional classroom settings in distance education and assesses the feasibility of virtual classrooms, web-based learning environments, and Massive Open Online Courses (MOOCs). A significant aspect of the study involves understanding student attitudes toward AR and VR technologies and their influence on intrinsic motivation, interest, and enthusiasm for the learning material. Based on a thorough analysis of relevant literature, the research aims to provide practical recommendations for educators to effectively incorporate AR and VR into education practices. The recommendations prioritize a pedagogically sound design, educator training, and accessibility consideration to ensure equitable access for all learners. In summary, this extensive research reveals the significant impact of AR and VR on education by understanding the strengths, limitations, and challenges of making informed decisions on utilizing these technologies to create engaging, impactful learning experiences, fostering a generation of technologically proficient and knowledge-driven learners.

The profound impact of artificial intelligence (AI) on the modes of teaching and learning necessitates a reexamination of the interrelationships among technology, pedagogy, and subject matter. Given this context, we endeavor to construct a framework for integrating the Technological Pedagogical Content Knowledge of Artificial Intelligence Technology (Artificial Intelligence—Technological Pedagogical Content Knowledge, AI-TPACK) aimed at elucidating the complex interrelations and synergistic effects of AI technology, pedagogical methods, and subject-specific content in the field of education. The AI-TPACK framework comprises seven components: Pedagogical Knowledge (PK), Content Knowledge (CK), AI-Technological Knowledge (AI-TK), Pedagogical Content Knowledge (PCK), AI-Technological Pedagogical Knowledge (AI-TCK), AI-Technological Content Knowledge (AI-TPK), and AI-TPACK itself. We developed an effective structural equation modeling (SEM) approach to explore the relationships among teachers’ AI-TPACK knowledge elements through the utilization of exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The result showed that six knowledge elements all serve as predictive factors for AI-TPACK variables. However, different knowledge elements showed varying levels of explanatory power in relation to teachers’ AI-TPACK. The influence of core knowledge elements (PK, CK, and AI-TK) on AI-TPACK is indirect, mediated by composite knowledge elements (PCK, AI-TCK, and AI-TPK), each playing unique roles. Non-technical knowledge elements have significantly lower explanatory power for teachers of AI-TPACK compared to knowledge elements related to technology. Notably, content knowledge (C) diminishes the explanatory power of PCK and AI-TCK. This study investigates the relationships within the AI-TPACK framework and its constituent knowledge elements. The framework serves as a comprehensive guide for the large-scale assessment of teachers’ AI-TPACK, and a nuanced comprehension of the interplay among AI-TPACK elements contributes to a deeper understanding of the generative mechanisms underlying teachers’ AI-TPACK. Such insights bear significant implications for the sustainable development of teachers in the era of artificial intelligence.

Although there has been some progress, the integration of artificial intelligence into higher education remains far from sufficient. The demand for teachers will persist for some time; however, with the introduction of AI-based robots into classrooms, the role of teachers has reduced to a minimum. The purpose of the current study was to evaluate Chinese higher education students’ intentions to adopt AI-based robots for educational purposes. Based on the TAM3 model, the current study proposes 14 hypotheses to evaluate students’ intention to adopt AI-based robots in education. The students’ data were collected and analyzed using PLS-SEM. The study findings revealed that 12 hypotheses were accepted and two were rejected. The results indicate that students are willing to accept AI-based robots in their education. However, the findings revealed an insignificant influence of job relevance and robot anxiety on perceived usefulness and ease of use, respectively. The findings of this study will provide insight into university administrations regarding the significance of AI-based robots in education. Moreover, the findings will help robot developers, policy makers, and university administrators design and implement AI-based robots that fulfill contemporary education needs.

Artificial Intelligence (AI) technology, particularly generative AI, has positively impacted education by enhancing mathematics instruction with personalized learning experiences and improved data analysis. Nonetheless, variations in AI literacy, trust in AI, and dependency on these technologies among mathematics teachers can significantly influence their development of 21st-century skills such as self-confidence, problem-solving, critical thinking, creative thinking, and collaboration. This study aims to identify distinct profiles of AI literacy, trust, and dependency among mathematics teachers and examines how these profiles correlate with variations in the aforementioned skills. Using a cross-sectional research design, the study collected data from 489 mathematics teachers in China. A robust three-step latent profile analysis method was utilized to analyze the data. The research revealed five distinct profiles of AI literacy and trust among the teachers: (1) Basic AI Engagement; (2) Developing AI Literacy, Skeptical of AI; (3) Balanced AI Competence; (4) Advanced AI Integration; and (5) AI Expertise and Confidence. The study found that an increase in AI literacy and trust directly correlates with an increase in AI dependency and a decrease in skills such as self-confidence, problem-solving, critical thinking, creative thinking, and collaboration. The findings underscore the need for careful integration of AI technologies in educational settings. Excessive reliance on AI can lead to detrimental dependencies, which may hinder the development of essential 21st-century skills. The study contributes to the existing literature by providing empirical evidence on the impact of AI literacy and trust on the professional development of mathematics teachers. It also offers practical implications for educational policymakers and institutions to consider balanced approaches to AI integration, ensuring that AI enhances rather than replaces the critical thinking and problem-solving capacities of educators.

Today, hundreds of studies on mathematics learning have been found in various literature, supported by the use of GeoGebra software. This meta-analysis aims to determine the overall effect of using GeoGebra software and the extent to which study characteristics moderate the study effect sizes to consider the implications later. This study analyzed 36 effect sizes from 29 primary studies identified from ERIC documents, Sage Publishing, Google Scholar, and repositories from 2010 to 2020, and a total of 2111 students. In order to support calculation accuracy, a Comprehensive Meta-analysis (CMA) software was used. The effect size is determined using the Hedges equation, with an acceptable confidence level of 95%. It is known that the overall effect size of using GeoGebra software on the mathematical abilities of students is 0.96 based on the estimation of the random-effect model, and the standard error is 0.08. These findings indicate that, on average, students exposed to GeoGebra-based learning outperformed math abilities, which was initially equivalent to 82% of students in traditional classrooms. This study considers the five characteristics of the study. It showed that the GeoGebra software used was more effective in sample conditions less than or equal to 30. Providing classrooms with sufficient numbers of computers allowed students to use them individually, which was necessary to achieve a higher level of effectiveness. GeoGebra software is more effective when the treatment duration is set to less than or equal to four weeks. These findings help educators consider the characteristics of studies that moderate effect sizes using the GeoGebra software in the future.

In the last decade, micro-lectures have been widely used to teach mathematics, but only a few studies have examined the factors affecting teachers’ intentions of using micro-lectures. As teachers are key to integrating modern technologies such as micro-lectures into students’ learning processes, knowledge about teachers’ intentions in this regard could be of particular importance. This study aimed to analyze the behavioral intention (BI) of mathematics teachers in using micro-lectures in mathematics in China, and identify the most influential factors involved, for the very first time. The Unified Theory of Acceptance and Use of Technology (UTAUT) model was used as a design model to investigate teachers’ BIs concerning the use of micro-lectures, and we used an online questionnaire to collect quantitative data. The participants in our research were 174 mathematics teachers from China, 166 of whom provided us with evaluable questionnaire data. Furthermore, partial least squares (PLS) regression was used, and hypothesis testing was performed with the Smart-PLS software. From the results, BI was positively affected by Performance Expectancy (PE), Effort Expectancy (EE), and Social Influence (SI). BI and facility conditions also had positive effects on user behavior; in contrast to other studies, SI had the most significant positive effect on BIs in our study. Our findings could provide insights into both future strategies for successful technology integration in mathematics classes and into mathematics teachers’ intentions towards integrating technologies into mathematics teaching.