This study investigates disparities in Technological Pedagogical Content Knowledge (TPACK) and attitudes towards digital technology integration among primary mathematics teachers in urban and rural China. In response to the post-pandemic era’s rapid technological advances, this research highlights the digital divide in primary education. A survey of 366 teachers assessed TPACK proficiency, technology access, professional development, and demographic impact factors like age and gender. The instrument, refined through Exploratory Factor Analysis (EFA) and confirmed with Confirmatory Factor Analysis (CFA), provided reliable measures. Data analysis employed descriptive statistics and non-parametric tests (Mann-Whitney U and Kruskal-Wallis) to explore differences across demographics. Findings reveal stark contrasts between urban and rural educators. Urban teachers exhibited higher TPACK proficiency and more favourable attitudes towards technology, likely due to enhanced access to resources and professional development. Conversely, rural teachers, challenged by limited access and support, displayed lower proficiency and less positive attitudes. Furthermore, younger teachers showed greater ease with technology integration than older counterparts, with no significant gender differences. The study’s implications highlight the need for tailored professional development in rural areas and equitable policymaking for technology access across all educational environments. These findings illuminate the urban-rural digital divide in China’s primary education and contribute to the global understanding of technology integration in diverse educational settings. Also, this research enriches the academic discourse on technological equity in education, providing a framework for comparative international studies and policy development.

This study presents an in-depth analysis of the factors influencing the adoption and utilisation of Artificial Intelligence (AI) in primary mathematics education. Employing a quantitative research design, the study integrates the Technology Acceptance Model (TAM) and Technological Pedagogical and Content Knowledge (TPACK) with empirical data collected from primary mathematics teachers in China. The analysis utilises Partial Least Squares Structural Equation Modelling (PLS-SEM) to explore the relationships among key constructs, including teacher attitudes, contextual factors, educational challenges, and parental and community involvement. The findings reveal that teacher attitudes play a critical role in AI adoption. The study extends existing literature by focusing on AI in primary mathematics education, highlighting the need for targeted professional development initiatives to foster positive attitudes and enhance teacher proficiency in AI technologies. Additionally, the research underscores the significance of TPACK in its critical role in equipping teachers with the knowledge required for effectively integrating AI into mathematics education. Moreover, the study explores the complex dynamics of contextual factors and educational challenges in AI integration, emphasising the need for systemic approaches that include policy changes and institutional support. The nuanced influence of TPACK on AI utilisation, particularly through indirect pathways, and the role of parental and community involvement are also examined, offering new insights into the multifaceted nature of technology adoption in educational settings. It contributes valuable insights to the academic discourse on technology integration in education and offers practical implications for educators, policymakers, and stakeholders.

This study is a systematic review of Technological Pedagogical and Content Knowledge (TPACK) studies concerning primary mathematics education published between 2005 and 2022. The aim of the systematic review was to identify the common features of previous TPACK research on primary mathematics education and identify the research gaps based on their contexts. The study used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) procedure to investigate TPACK-related studies published during the last 17 years in the primary mathematics education domain and to evaluate the characteristics of TPACK instruments used in primary mathematics education. We identified five foci of these studies of TPACK in primary mathematics education research: designing lessons, evaluating mathematics teachers’ knowledge of integrating digital technologies, designing the assessment, evaluating training programs, and informing professional development program designs. Findings from this systematic review of the literature can assist educators in better designing professional development programs to help primary mathematics teachers improve their ability to integrate digital technology into classroom teaching. Also, the findings can assist researchers in locating TPACK instruments that are appropriate and relevant for their research. Finally, we argue that there is a research gap concerning how to measure primary mathematics teachers’ TPACK, how to design a TPACK instrument that includes contextual factors, and how to develop TPACK-oriented teacher training programs for primary mathematics teachers.

This study investigated primary mathematics teachers’ technological, pedagogical, and content knowledge (TPACK) and attitudes towards information and communication technology (ICT) integration in mainland China during the post-pandemic period. Quantitative data were collected through a web-based self-determined questionnaire. Statistical analysis was performed using independent sample t-tests, and Mann-Whitney U tests to assess gender and teaching experience in years. Spearman’s correlation test also examined the relationship between TPACK and teachers’ attitudes towards ICT integration. The findings revealed that most mathematics teachers had adequate non-technology-related knowledge but lacked technology-related knowledge. However, they were willing to incorporate digital technologies in their teaching after experiencing large-scale online teaching. There was no significant gender or teaching grade differences in TPACK, but teaching experience positively correlated with TPACK. The study emphasizes the importance of assessing teachers’ practical circumstances before introducing TPACK development programs. Finally, the implications for teachers, educators, and policymakers were discussed at the end of this paper.

This quantitative study examined the dynamics of the Technological Pedagogical Content Knowledge (TPACK) framework within mathematics education, centring on the role of Contextual Knowledge (XK). The research, conducted with middle school mathematics teachers in Chongqing, China, employed structural equation modelling (SEM) to explore the relationships between various TPACK components. The study establishes discriminant validity and demonstrates an excellent fit for the SEM model. Notably, it uncovers significant correlations within the TPACK framework, with a special emphasis on the influence of XK. The findings indicate that XK, in conjunction with Pedagogical Knowledge (PK), Pedagogical Content Knowledge (PCK), and Technological Content Knowledge (TCK), considerably impacts the overall TPACK construct. The research highlights the critical influence of XK on key TPACK components, such as Technological Pedagogical Knowledge (TPK), PCK, and TPACK itself. These results underline the importance of integrating XK in professional development programs focused on TPACK, accentuating its vital role in effectively integrating technology in mathematics education. This study significantly contributes to the academic understanding of TPACK’s complex dynamics. It provides essential insights for enhancing technology integration in mathematics education, offering valuable guidance for educational practitioners and policymakers.

This study investigates the integration of artificial intelligence (AI) in primary mathematics education, focusing on the influence of internal and external factors on AI utilization. Employing second-order Structural Equation Modeling (SEM) with data collected from 516 primary mathematics teachers in a southwest city in China, the research explores the dynamics between teachers’ Technological Pedagogical Content Knowledge (TPACK), attitudes towards technology, and the role of external environmental factors such as educational policies, technological infrastructure, and parental and community engagement. The findings reveal that internal factors, including teachers’ attitudes and TPACK, and external factors, notably institutional support and societal attitudes towards technology, significantly influence AI adoption and effective use in teaching and learning processes. The study confirms the necessity of a dual-faceted approach to AI integration, emphasizing the enhancement of educators’ internal capacities through professional development and the cultivation of a supportive external ecosystem. The research contributes to the knowledge of technology integration in education by providing empirical evidence of the critical interplay between internal capabilities and external supports in facilitating effective AI utilization in primary mathematics education. This study offers a pragmatic blueprint for educational stakeholders to navigate the complexities of educational innovation in the digital era, advocating for systemic approaches to foster both technological competencies and supportive infrastructures.