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.

Teacher education is an important strategy for developing teachers’ technological pedagogical content knowledge (TPACK). Many schools in the world have incorporated the training into teacher education plans. However, there has been controversy in academic circles concerning the effects of teacher education intervention in promoting the development of teacher TPACK. Therefore, this study used a meta-analysis approach to review the published literature on teacher education programs to determine the impact on TPACK. The results showed that teacher education intervention positively affected TPACK (d = 0.839, p < 0.0001). Besides cultural background, experimental participants, types, sample types, intervention durations, differences in measurement methods, intervention types, and learning environments are the reasons for the differences in the effects of the interventions. The research design using random experiments had a significant positive effect on the size, which was significantly higher than that of the quasi-experiment. The longer the duration of teaching intervention, the stronger the improvement effect of teachers’ TPACK. There are significant differences in improving TPACK between teaching interventions, and the effect is more obvious. Teacher education intervention has a greater and slightly smaller impact on theoretical and practical knowledge. However, cultural background, experimental participant, sample type, and learning environment have no significant effect on teacher education intervention.

The PISA 2022 literacy assessment highlights a significant decline in math performance among most OECD countries, with the magnitude of this decline being approximately three times that of the previous round. Remarkably, Hong Kong, Macao, Taipei, Singapore, Japan, and Korea ranked in the top six among all participating countries or economies, with Taipei, Singapore, Japan, and Korea also demonstrating improved performance. Given the widespread concern about the factors influencing secondary-school students’ mathematical literacy, this paper adopts machine learning and the SHapley Additive exPlanations (SHAP) method to analyze 34,968 samples and 151 features from six East Asian education systems within the PISA 2022 dataset, aiming to pinpoint the crucial factors that affect middle-school students’ mathematical literacy. First, the XGBoost model has the highest prediction accuracy for math literacy performance. Second, 15 variables were identified as significant predictors of mathematical literacy across the student population, particularly variables such as mathematics self-efficacy (MATHEFF) and expected occupational status (BSMJ). Third, mathematics self-efficacy was determined to be the most influential factor. Fourth, the factors influencing mathematical literacy vary among individual students, including the key influencing factors, the direction (positive or negative) of their impact, and the extent of this influence. Finally, based on our findings, four recommendations are proffered to enhance the mathematical literacy performance of secondary-school students.

Digital mathematics textbooks differ from traditional printed textbooks in, among other things, their dynamic structural elements, representing a potential that traditional textbooks cannot fulfil. Notably, dynamic structural elements, i.e., multimodal representations of mathematics, could be of particular importance for learning, which is why the scientific interest in digital mathematics textbooks has increased in recent years and many digital textbooks have been developed. However, research related to predicting teacher usage behavior of digital textbooks is still limited. Therefore, this research aims to analyze the predictors that may influence the intentions of mathematics teachers and the actual usage of digital textbooks by applying the Unified Theory of Acceptance and Use of Technology (UTAUT). Data were collected from 277 teachers in West Java Province, Indonesia, and analyzed using structural equation modeling (SEM). The results indicated that Performance Expectancy (PE) is the biggest significant factor, followed by Social Influence (SI), that influences the Behavioral Intention (BI) of mathematics teachers to use digital textbooks in Indonesia. Effort Expectancy (EE) does not affect the intention to use a digital textbook. In turn, BI has the largest and most significant effect on teachers’ actual usage of digital textbooks. This result contributes to the understanding of the predictors that can increase the use of digital textbooks by mathematics teachers.

Academic stress and burnout are the predominant factors that can negatively affect student performance and sustainable learning. Therefore, it is important to analyze the factors related to student academic burnout in preservice teachers in western China. 212 respondents from public universities in Guangxi Province participated, and the data were analyzed using partial least-squares structural equation modeling (PLS-SEM) to check reliability, validity, and initial hypothesis testing. The results show that perfectionism, excessive self-efficacy, and workload are the main factors causing academic stress and burnout in preservice teachers. These problems can be reduced by increasing self-efficacy and coping strategies of preservice teachers. In addition, this study provides important knowledge to universities based on factors related to preservice teachers’ academic stress and burnout, as well as strategies and solutions to reduce these problems in students.