1 Introduction

The trajectory of digital technology integration within primary mathematics education is marked by a continual evolution that reflects broader technological advances in society. Historically, the advent of personal computers and the Internet instigated a paradigm shift in educational practices, fostering new pedagogical opportunities and challenges (Selwyn, 2017). This shift was not merely a transition to digital formats but a fundamental change in how educators approach instruction and students engage with mathematical concepts (Blannin, 2022). In contemporary educational settings, digital technology has become integral to teaching and learning. Interactive whiteboards (Jang & Tsai, 2012), digital textbooks (Rezat, 2021), and online educational resources are commonplace, providing dynamic platforms for instruction, practice, and assessment (Cheung & Slavin, 2013; Drijvers et al., 2021). These technologies offer unique affordances for visualising mathematical problems, facilitating collaborative learning, and tailoring instruction to individual student needs (Oikarinen et al., 2022; Polly, 2014). However, integrating such technologies is not uniform across different educational contexts, particularly when comparing urban and rural settings.

The disparity in technology integration between urban and rural schools, often called the “digital divide”, remains a critical issue in educational equity (Warschauer, 2004). Rural schools frequently grapple with limited access to high-speed Internet, fewer technological resources, and less professional development for teachers using educational technology (Hohlfeld et al., 2008; Li & Ranieri, 2013). These disparities can impede the adoption and effective implementation of digital tools in rural mathematics classrooms, potentially disadvantaging students in these areas. Additionally, teachers’ attitudes and perceptions towards technology are pivotal in successfully integrating digital resources into mathematics education (Ertmer et al., 2012). A teacher’s belief in the value of technology, their confidence in using it, and their willingness to incorporate it into their pedagogy are critical determinants of technology adoption (Hew & Brush, 2007). Studies have shown that positive attitudes towards technology are associated with higher levels of technology use in the classroom and more innovative teaching practices (Inan & Lowther, 2010). Conversely, teachers with apprehensions or negative perceptions may resist integrating technology, regardless of its availability. In synthesising these points, it is evident that the successful integration of digital technology in primary mathematics education is contingent upon a complex interplay of historical progression, current technological advancements, existing inequalities, and teacher attitudes. Understanding and addressing these factors is imperative for ensuring that teachers and students have equitable opportunities to benefit from the educational potential that digital technologies can offer.

Some studies have consistently underscored a substantial gap in the comparative analysis of Technological Pedagogical Content Knowledge (TPACK) (Mishra & Koehler, 2006) proficiency and attitudes towards technology integration among mathematics teachers (Honey, 2018; Marbán & Sintema, 2021), with a pronounced disparity between urban and rural educators (Wu et al., 2019). In China’s diverse educational landscape, this divide is not merely a reflection of unequal access to digital resources but also a broader indicator of the inequities in educational delivery and outcomes (Wu et al., 2019). Indeed, such disparities are particularly acute in rural settings, where limited technological infrastructure and teacher professional development opportunities could further entrench educational inequities (Bingimlas, 2009). Addressing this gap is crucial for ensuring equitable educational practices and harnessing the full potential of digital technologies in mathematics education across varied geographic locales.

The current study is designed to interrogate the TPACK framework and related attitudes towards digital technology within the domain of primary mathematics education in China. It aims to illuminate the contrasts between urban and rural schoolteachers in TPACK proficiency and digital attitudes. This investigation is conducted against the backdrop of the Artificial Intelligence (AI) era, characterised by the emergence of adaptive learning systems and AI tools such as ChatGPT that hold transformative potential for educational practices (Mishra et al., 2023). By exploring these dimensions, the study seeks to provide a detailed understanding of how digital technologies are being adopted and perceived by primary mathematics teachers in disparate environments. Additionally, the significance of this research extends beyond academic discourse, offering critical insights for policymakers, curriculum developers, and professional development facilitators. By gaining a nuanced understanding of the factors influencing TPACK adoption and the attitudes towards technology integration, stakeholders can devise more strategic and informed approaches to technology deployment and teacher professional development programs. Such strategies would be tailored to primary mathematics teachers’ unique challenges and resources in urban and rural settings. To achieve these goals, two research questions are formulated:

  1. 1.

    What are the differences in TPACK levels and attitudes towards digital technology between mathematics teachers in rural and urban primary schools in China?

  2. 2.

    How do demographic factors (e.g., age and gender) influence mathematics teachers’ TPACK and attitudes towards digital technology integration in classroom teaching?

2 Literature review

2.1 TPACK framework in mathematics education

The TPACK framework, as conceptualised by Mishra and Koehler (2006), represents a significant advancement in understanding how technology can be effectively integrated into educational contexts. At its core, TPACK converges three primary forms of knowledge: Content Knowledge (CK), Pedagogy Knowledge (PK), and Technology Knowledge (TK). The intersections of CK, PK, and TK are crucial for educators in the digital age, as they encapsulate the necessary competencies for integrating technology into teaching to enhance learning (Koehler & Mishra, 2009; Mishra & Koehler, 2006). The TPACK framework is crucial in mathematics education, particularly given the discipline’s inherent abstraction and complex problem-solving nature (Li et al., 2024). Thoughtful integration of technology can significantly enhance the learning and teaching of these concepts. A study by Kramarski and Michalsky (2010) highlights how proficiency in TPACK among mathematics teachers is strongly associated with their ability to integrate digital tools effectively into their curriculum. This integration extends beyond mere technology usage; it requires a profound understanding of how digital tools can illuminate and deepen students’ comprehension of mathematical concepts (Yigit, 2014). Niess and Gillow-Wiles (2014) further emphasise this point, illustrating how technology can transform mathematics instruction into a more engaging and conceptually rich experience when used within the TPACK framework.

Loong and Herbert (2018) delve into mathematics teachers’ challenges in developing this intricate knowledge blend. They highlight that while technological tools offer vast opportunities for innovative teaching, their practical use requires teachers to understand the technology and adapt their pedagogical strategies to optimise these tools (Loong & Herbert, 2018). This adaptation is an ongoing process and involves continuous learning and professional development. Moreover, the TPACK framework’s application in mathematics education extends to how teachers design instruction and assessments. Harris and Hofer (2011) argue that mathematics educators using TPACK can create more engaging, student-centred learning experiences that encourage exploration and critical thinking. This approach is efficient in mathematics, where students often benefit from visual and interactive representations of complex problems (Stoilescu, 2015). Additionally, Voogt et al. (2013) pointed out that successfully implementing TPACK in mathematics education requires supportive institutional policies and professional development programs. Teachers may struggle to keep pace with evolving technologies and pedagogical models without these. This support is crucial in ensuring that TPACK’s potential in enhancing mathematics education is realised (Voogt et al., 2013). It can be said that the TPACK framework offers a comprehensive lens through which the integration of technology in mathematics education can be viewed and enhanced (Li et al., 2024). Its emphasis on the intersection of technology, pedagogy, and content knowledge provides a robust foundation for educators to craft meaningful and effective mathematics teaching experiences in an increasingly digital world.

2.2 Technology integration in education: Urban vs. rural disparities

Technology integration in educational settings has been transformative, reshaping instructional methodologies and enhancing student learning outcomes (Drijvers et al., 2018; Hew & Brush, 2007). This evolution, however, is not uniformly experienced across different educational landscapes. In urban and rural schools, the disparity in technology integration, often called the “digital divide,” is pronounced and impactful (Warschauer, 2004). For instance, Warschauer and Matuchniak (2010) comprehensively analyse the digital divide, illustrating how access to and use of technology in educational settings vary significantly between urban and rural areas. Their study, based on a survey of 30 urban and 30 rural schools in California, highlights that rural schools often encounter challenges such as limited broadband access and fewer technological resources, which can impede the integration of digital tools in education (Warschauer & Matuchniak, 2010). Additionally, in examining the implications of this divide, researchers note that these disparities can lead to significant differences in educational outcomes (Gou et al., 2020; Hohlfeld et al., 2008). In rural areas, students and teachers may not only have limited access to technology in school but also lack the necessary support and training to effectively utilise these tools, thus hindering students’ academic growth and technological fluency. For example, Kalonde (2017) found that rural middle school mathematics and science teachers faced significant challenges in integrating technology due to inadequate access and insufficient professional development. The research highlighted that these teachers, despite having some personal experience with technology, lacked the formal training provided by their districts, resulting in limited use of educational technologies in their classrooms.

Given the highlighted challenges of the digital divide in rural settings, the role of professional development emerges as a critical solution in bridging this gap. Loong and Herbert (2018) emphasise the importance of improving infrastructures and equipping teachers with the skills and knowledge to integrate technology effectively. This need is often more pronounced in under-resourced rural settings (Kalonde, 2017). Without adequate training and support, even when technology is available, its potential to enhance education remains unrealised (Wu et al., 2019). Here is an example. A study by Hsu (2016) involving K-6 teachers in urban, suburban, and rural areas of the midwestern United States found that a lack of training and technical support significantly hindered the effective integration of technology in classrooms. This indicates that without proper professional development and support, the benefits of technology in education are limited. Moreover, socio-economic factors exacerbate the digital divide. Guo and Wan (2022) discuss how students in lower socio-economic rural areas are often doubly disadvantaged, facing limited technology access at school and home. This lack of access impacts their immediate educational experiences, long-term digital literacy, and preparedness for a technology-driven future. For example, a study conducted in rural areas of Kenya by Kara (2021) demonstrated how the lack of reliable Internet and limited access to digital devices in students’ homes severely impacted their ability to engage with e-learning platforms. This digital divide not only hindered their academic progress but also limited their exposure to digital literacy skills, crucial for their future success in a technology-dependent global economy (Kara, 2021).

To address these disparities, a multifaceted approach is required. Previous research underscores the importance of providing technological resources and supporting primary mathematics teachers through training and development programs, particularly in rural areas. However, the post-pandemic era introduces new challenges and amplifies existing ones, making it essential to revisit and expand these strategies. The rapid shift to digital platforms has highlighted gaps in access, training, and support, particularly in under-resourced rural settings. Therefore, there is a critical need to explore these emerging issues further. This study seeks to identify and understand these post-pandemic disparities, emphasizing the necessity for targeted interventions and comprehensive support systems to ensure equitable educational experiences for all students in the evolving educational landscape.

2.3 Attitudes towards technology in education

The successful integration of technology within educational settings is intricately linked to teachers’ attitudes towards these digital tools (Ertmer & Ottenbreit-Leftwich, 2010; Li, 2023). Researchers highlight the significant role of teachers’ perceptions in shaping technology adoption in the classroom (Eickelmann & Vennemann, 2017). Positive attitudes towards technology, fostered by beliefs in its educational value and efficacy, can lead to more frequent and innovative uses of digital tools in teaching (Jin & Harp, 2020; Marbán & Sintema, 2021). Conversely, apprehensions or negative attitudes towards technology can pose substantial barriers to integration (Ertmer et al., 2012). For example, Hew and Brush (2007) pointed out that resistance to change and scepticism about the benefits of technology are significant universal barriers to technology integration in K-12 educational settings, as evidenced by their comprehensive analysis of 48 empirical studies across the United States and other countries, demonstrating the widespread nature of these challenges in enhancing student learning. Indeed, such resistance can stem from various sources, including limited educational infrastructure, lack of confidence, limited technological competence, or prior negative experiences with technology (Hofer et al., 2021; Mueller et al., 2008).

Furthermore, Khong et al. (2023) explored how factors such as personal experience with technology, perceived usefulness, ease of use of technological tools, and available resources for technology integration significantly impacted teachers’ attitudes to adopting digital tools. This examination underscores the complexity and multifaceted nature of the factors influencing teachers’ attitudes toward technology, illustrating that such attitudes are not formed in isolation but result from a confluence of diverse and interrelated elements. For instance, the context in which teachers operate, including schools’ geographic locations, cultures, and policies, also influences their attitudes towards technology. Research found that a supportive environment that encourages experimentation and provides adequate training and resources is crucial for fostering positive attitudes (Lowther et al., 2008; Scherer et al., 2019). Therefore, it can be argued that teachers’ attitudes towards technology are pivotal in determining the trajectory and effectiveness of technology integration in education. These attitudes are shaped by a complex interplay of personal experiences, perceived utility, institutional support, broader educational beliefs, and educational environment (Howley et al., 2011; Khong et al., 2023; Tondeur et al., 2019). Addressing these factors is crucial for ensuring technology is used effectively and innovatively in educational settings.

While past research has thoroughly explored mathematics teachers’ attitudes towards technology and the barriers to its integration, there is a need to understand how these attitudes and barriers have evolved in the post-pandemic context. The rapid shift to online learning has intensified the need for effective technology use, making it imperative to re-examine and address the digital divide that affects both students and primary mathematics teachers. This study aims to fill this gap by investigating the current state of technology integration in education, with a particular focus on the post-pandemic era.

2.4 Age and gender dynamics in educational technology integration

The relationship between educators’ age, gender, and their engagement with technology in teaching provides a nuanced view of technology integration in education (Cai et al., 2017). Younger educators exhibit a propensity to embrace technological tools in their pedagogical practices, likely due to their inherent familiarity with digital environments (Song et al., 2021). This contrast with older educators, who might not have been exposed to such technology-centric training during their formative years, suggests a generational divide in comfort and proficiency with educational technology (Sanchez-Prieto et al., 2020). Indeed, the generational differences in technology integration within post-pandemic primary mathematics education have not been extensively explored, underscoring the need for in-depth investigation in this area. It is crucial to understand how these age-related disparities impact teaching practices and learning outcomes, especially as the educational landscape continues to evolve with digital advancements.

The exploration of gender dynamics in the integration of technology within mathematics educational contexts is a prevalent and frequently discussed topic (Bakar et al., 2020; Zhang & Wang, 2016). This focus acknowledges the evolving discussions and inquiries into how male and female educators differently engage with and perceive technology in their teaching environments, highlighting the need for a nuanced understanding of gender influences on technology adoption in education. Earlier research pointed to a disparity where male teachers showed a higher TPACK and attitude towards technology integration compared to their female counterparts (Bulut & Isiksal-Bostan, 2019; Jordan, 2013). However, more recent studies challenge this notion, illustrating a closing gap, with female educators increasingly adopting technology in their teaching methodologies (Li, 2023; Marbán & Sintema, 2021). This shift could be attributed to broader societal changes, improved access to technology, and enhanced professional development opportunities that are inclusive and gender-neutral. While recent studies, such as those by Li (2023) and Marbán and Sintema (2021), show a narrowing gender gap in technology integration within education, indicating an increasing adoption rate among female educators, there is still a notable gap in the literature regarding the post-pandemic era. This period, characterized by rapid technological advancements and shifts in educational practices, remains underexplored, especially in terms of how gender dynamics influence technology integration in this new context.

2.5 Disparities in technology integration and TPACK in urban and rural schools

The Chinese educational landscape, particularly in technology integration, has undergone significant changes in recent years, markedly influenced by the COVID-19 pandemic and the rapid advancement of AI tools in education (Li, 2023; Qiu et al., 2022). The pandemic has accelerated the adoption of digital technologies across educational sectors, leading to an unprecedented reliance on online and remote learning modalities. Yao and Zhao (2022) analysed the pandemic’s impact on education in China, noting a marked increase in the use of digital tools and platforms. However, this sudden shift has also highlighted disparities between urban and rural schools. Despite efforts to enhance digital infrastructures, rural areas struggle with limited Internet access and a lack of digital devices, essential for effective remote learning (Guo & Wan, 2022). Additionally, in recent years, integrating AI tools in education, which has seen a significant uptick, presents opportunities and challenges (Wardat et al., 2023). For example, while AI technologies such as adaptive learning systems offer the potential for personalised and enhanced learning experiences, their accessibility remains uneven. del Olmo-Muñoz et al. (2023) conducted a study in Castilla-La Mancha, Spain, involving 133 elementary students, demonstrating that while AI technologies such as the HINTS system offer personalized learning opportunities, their effective use in socio-economically disadvantaged rural areas is challenged by disparities in technology access and utilisation at school and home. By contrast, adopting AI tools in Chinese education, particularly in rural areas, presents a largely uncharted territory in current research.

The disparity in technology integration and access has implications for mathematics teachers’ TPACK and attitudes towards technology integration (Teo et al., 2019). This gap, especially pronounced between urban and rural educators, influences their ability to integrate technology effectively, shaping the quality of teaching and learning outcomes (Wu et al., 2019). A recent study by Gou et al. (2020) reveals that although urban teachers have demonstrated greater ease in adapting to technologically enriched teaching environments, their rural counterparts, particularly those in China’s Western regions, continue to encounter difficulties in acquiring TPACK required for effective teaching. Zhao and Frank (2003) emphasise that understanding this divide is essential for comprehending the overall educational disparities within the country. As China continues to navigate the post-pandemic era and embraces the increasing integration of AI in education, comprehensive policy initiatives, equitable resource distribution, and continuous professional development for primary mathematics teachers, particularly in rural areas, become imperative. These efforts are crucial to ensure that the potential of digital technologies in enhancing educational outcomes is realized across the country, bridging the digital divide and fostering a more equitable educational landscape (Liu, 2021). Hence, it is essential to explore the disparities in TPACK between urban and rural primary mathematics teachers. Understanding and addressing these differences in TPACK proficiency is vital for mitigating the digital divide and improving the quality of education in diverse settings.

3 Method

3.1 Research design

This study adopts a quantitative research design, utilising a survey-based approach widely recognized for its effectiveness in educational research (Creswell & Clark, 2018). The survey method was specifically chosen because it is suitable for addressing the research questions that require the analysis of measurable data to identify patterns and correlations across various educational settings. Cohen et al. (2018) advocate using surveys in educational research, particularly when exploring perceptions and attitudes across a broad range of participants. Therefore, aligning with their views, the current study employs a survey-based approach to collect data from a diverse cross-section of mathematics teachers in urban and rural areas of China. This facilitates an extensive comparison of primary mathematics teachers’ TPACK and attitudes towards technology integration.

Furthermore, the quantitative aspect of the survey facilitates the use of statistical analysis techniques, as Bryman (2016) advocates, to evaluate the relationships and disparities within the collected data. The selection of a quantitative survey is further supported by its efficacy and practicality in reaching participants across a wide geographical area, which is crucial for encompassing diverse educational settings throughout China. Additionally, aligning with the perspectives of Cohen et al. (2018), the survey method is chosen for its standardisation in data collection, ensuring consistency and comparability in responses. This standardisation is paramount given the study’s objective to generate generalisable insights that can inform broader educational practices and policies. Hence, this study’s quantitative, survey-based research design is firmly grounded in established research methodologies within educational research. It is deliberately chosen to address the research questions and to offer a robust framework for examining the complexities of technology integration within China’s educational landscape, particularly across contrasting urban and rural environments.

3.2 Participants

The study encompassed a diverse cohort of 366 primary school mathematics teachers randomly selected from various regions across Chongqing, capturing a broad spectrum of educational settings in both urban and rural areas. The participant pool reflected a gender imbalance, typical of the teaching profession in China, with a higher representation of female teachers (307 females) compared to male teachers (59 males). This gender distribution aligns with existing trends in the field of education, where female predominance is often observed, particularly in primary school teaching. In terms of age demographics, the participants ranged from early career teachers in their twenties to experienced educators in their fifties, providing a comprehensive perspective across different stages of teaching careers. Most participants fell within the 30–39 age group, indicative of a workforce with a balance of experience and adaptability to technological advancements in education (See Table 1).

The sample also encompassed teachers from all primary grades (1 through 6), ensuring a wide-ranging understanding of technology integration and pedagogical approaches at various levels of primary education. This grade-wise distribution of teachers allowed for exploring TPACK and attitudes towards digital technology that might vary with the grade levels taught, offering insights into grade-specific challenges and opportunities in technology integration. By including teachers from urban and rural settings, the study aims to unravel the nuances of technological and pedagogical integration in diverse educational contexts, addressing the existing urban-rural divide. Teachers’ distinct experiences and challenges in these differing locales are crucial for a comprehensive analysis of the current state of TPACK and attitudes towards technology integration in the Chinese educational landscape.

Table 1 Participants’ information
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3.3 Instrument

The instrument for this study was designed to assess the specific aspects of TPACK, with a focus on the technology-related components. The questionnaire’s development drew upon the scale designed by Chai et al. (2013), integrating their insights into TPACK’s various dimensions (See Appendix A). Additionally, the instrument incorporated contemporary considerations in educational technology, guided by the recent work of Li (2023), particularly addressing the role and impact of AI tools in the current educational context. For example, one item is ‘I frequently play around with the technology (e.g., Interactive Whiteboard, AI tools, and mathematics software)’. The decision to concentrate on technology-related components of the TPACK framework, namely TPACK, technological content knowledge (TCK), technological pedagogical knowledge (TPK), and TK, along with attitudes towards technology, stems from the evolving nature of educational environments (Ertmer et al., 2015; Mishra et al., 2023). Understanding teachers’ proficiency and attitudes in these specific domains is crucial in an era increasingly defined by digital and AI-driven tools (Mishra et al., 2023). These components are critical for comprehensively assessing teachers’ capabilities and readiness to integrate emerging technologies effectively into their pedagogical practices (Koehler & Mishra, 2009). Also, including items related to AI tools in the instrument reflects the growing significance of these technologies in shaping educational practices (Mishra et al., 2023; Wardat et al., 2023). This focus allows the study to capture current trends and teachers’ adaptability to the rapid integration of AI in education. This area has gained heightened relevance, especially after the COVID-19 pandemic.

3.3.1 Piloting and face validity

To ensure the instrument’s relevance and appropriateness, a pilot test was conducted with ten mathematics teachers from Chongqing. This pilot phase was instrumental in evaluating the questionnaire’s face validity, allowing for adjustments based on the feedback received from these practitioners (Cohen et al., 2018). Their inputs helped refine the instrument, ensuring the questions were straightforward, contextually relevant, and reflective of the practical realities of teaching mathematics in contemporary Chinese educational settings. The questionnaire’s emphasis on technology-related TPACK components aligns with the study’s aim to delve into the nuances of technology integration in mathematics education. By focusing on TPACK, TCK, TPK, TK, and attitudes towards technology, the instrument is tailored to explore the dimensions most affected by the advent and integration of digital technologies in education. This focus is essential for understanding the current landscape of technology use in Chinese primary schools and identifying areas where support and development are needed, especially in the context of the urban-rural divide.

3.3.2 Exploratory factor analysis

Exploratory factor analysis (EFA) was conducted to assess the factor structure of the questionnaire items and to ensure their alignment with the constructs of TPACK and attitudes towards technology integration. The EFA was executed using Principal Component Analysis with Promax rotation, a method suitable for exploring underlying factor structures in psychological and educational research (Field, 2013). The Kaiser-Meyer-Olkin (KMO) measure and Bartlett’s Test of Sphericity were employed to determine the adequacy of the sample and the appropriateness of factor analysis (Cohen et al., 2018). The KMO measure yielded a value of 0.971, indicating a high level of adequacy for conducting EFA. Bartlett’s Test of Sphericity was significant (\({x}^{2}\) = 10454.940, df = 300, p < 0.001), suggesting that the variables were sufficiently correlated for factor analysis (See Table 2). The EFA resulted in a clear factor structure that corresponded well with the intended constructs. However, certain items were removed due to their low factor loadings or lack of relevance (See Table 3).

Table 2 KMO and Bartlett’s test
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Table 3 Item removing explanation
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The EFA analysis revealed a clear factor structure for the questionnaire items. The factor loadings obtained from the EFA indicate how well each item correlates with its intended construct. High factor loadings demonstrate a strong association between the item and the construct it is intended to measure, which in this study included TK, TCK, TPK, TPACK, and Attitude towards technology integration. For example, item Q1 (“I can effectively integrate digital technology, mathematics knowledge and teaching methods in mathematics classes.“) showed a strong factor loading of 0.931 on the TPACK construct, indicating its high relevance to this specific knowledge domain. Similarly, items Q12 through Q19, which are related to Attitude towards technology integration, demonstrated high factor loadings (ranging from 0.831 to 0.967), affirming their strong alignment with the attitude construct.

Table 4 Exploratory factor analysis and Cronbach’s alpha
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The reliability of each construct was assessed using Cronbach’s Alpha (See Table 4). The values ranged from 0.908 to 0.975, suggesting a high level of internal consistency within each construct. For instance, the Attitude construct exhibited a Cronbach’s Alpha of 0.975, indicating excellent reliability. This high level of reliability is essential for ensuring that the constructs are consistently and accurately measured across the sample (Cronbach, 1951). The factor loadings and Cronbach’s Alpha values collectively provide evidence of the validity and reliability of the questionnaire. The high factor loadings confirm that each item is a good measure of its respective construct. At the same time, Cronbach’s Alpha values indicate that the items within each construct reliably measure the same underlying concept.

3.3.3 Confirmatory factor analysis: Model fit and validity analysis

Fig. 1
figure 1

CFA result of the scale

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The figure illustrates the confirmatory factor analysis (CFA) outcomes, revealing high factor loadings for the items ranging from 0.81 to 0.93 across the constructs (See Fig. 1). Attitude, TPK, TK, TCK, and TPACK demonstrate robust and statistically significant associations with their respective latent variables, indicative of a robust and well-defined factor structure (Byrne, 2016). Additionally, the CFA conducted on the questionnaire revealed satisfactory model fit indices, aligning with the guidelines established by Hair et al. (2018) (See Table 5). The Chi-Square to degrees of freedom ratio (\({x}^{2}\)/df) was 1.857, well within the acceptable range, indicating a good fit of the model. The Root Mean Square Error of Approximation (RMSEA) stood at 0.048, affirming the model’s appropriateness in capturing the data patterns, consistent with standards for a good fit. The Standardized Root Mean Square Residual (SRMR) was notably low at 0.031, falling well within the range of a good fit and suggesting minimal residual variance. Additionally, the Goodness-of-Fit Index (GFI) and Adjusted Goodness-of-Fit Index (AGFI) recorded values of 0.901 and 0.876, respectively, indicating a decent fit to the observed data. The Comparative Fit Index (CFI) and Tucker-Lewis Index (TLI) values, at 0.979 and 0.975, respectively, further demonstrated the robustness of the model.

Table 5 Model fit values (Hair et al., 2018)
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The analysis robustly supported the validity of the constructs. Composite Reliability (CR) values across all constructs exceeded the recommended 0.7 thresholds, indicating high internal consistency (See Table 6). The Average Variance Extracted (AVE) values surpassed the 0.5 benchmark for each construct, confirming convergent validity and ensuring that the constructs captured a substantial proportion of variance in the items (Byrne, 2016). Discriminant validity was corroborated by Maximum Shared Variance (MSV) values within acceptable limits, with the Heterotrait-Monotrait Ratio (MaxR(H)) aligning with the AVE, further validating the distinctiveness of each construct. The significant inter-construct correlations (p < 0.01) reinforced the theoretical underpinnings of the model, with the bolded diagonal values (\(\sqrt{AVE}\)) in the correlation matrix confirming discriminant validity. Specifically, the \(\sqrt{AVE}\) values (ranging from 0.845 to 0.918) were higher than the inter-construct correlation coefficients, all significant at p < 0.01. For example, the \(\sqrt{AVE}\) for Attitude (0.918) was more significant than its correlations with TPK (0.791), TK (0.795), TCK (0.682), and TPACK (0.732), suggesting that the Attitude construct is distinct from the other constructs in the model. This pattern was consistent across all constructs, reinforcing the discriminant validity of the scale. Therefore, the scale employed in this study has demonstrated strong reliability and validity, reinforcing its strength as a measurement tool.

Table 6 Validity analysis (Hair et al., 2018)
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3.4 Data collection

This study adopted a structured approach for data collection, focusing on primary mathematics teachers in urban and rural areas of China. Using a stratified random sampling technique, participants were selected from a larger pool of primary mathematics teachers in various regions, including urban and rural schools. The survey was administered online, leveraging digital platforms for broader reach and ease of access. The questionnaire was divided into TPACK, TCK, TPK, TK, and Attitude towards technology integration. The online distribution of the questionnaire was facilitated through a widely used social media platform in China, offering a seamless and convenient means for teachers to participate in the study.

A digital poster was created and circulated to engage participants and clarify the purpose of the research. This poster included essential details about the study’s objectives, the significance of teacher participation, and instructions on accessing the survey, such as a QR code and a direct link to the questionnaire. Participation in the study was voluntary and anonymous, with no personal identifying information collected in the questionnaire. This approach ensured the confidentiality of the respondents and adhered to ethical standards concerning participant privacy. The research team upheld strict data security measures, ensuring that the information gathered was accessible only to authorised personnel involved in the study. Moreover, an informed consent form and an explanatory statement were integrated into the online questionnaire, ensuring that participants were fully aware of the study’s nature and rights. The data collection period spanned five weeks, providing ample time for participants to complete the questionnaire at their convenience. To enhance the response rate and ensure comprehensive data collection, periodic reminders were sent to potential respondents, encouraging their participation and timely survey completion.

3.5 Data analysis

The initial data analysis phase entails a descriptive statistical examination of the primary variables: TPACK and attitudes. This analysis provides fundamental insights into the central tendencies and variability within the data. Descriptive statistics, including means, standard deviations, and frequency distributions, are calculated to portray the baseline characteristics of TPACK and attitudes stratified by urban and rural teaching environments. This analysis facilitates understanding the prevailing patterns and potential disparities in mathematics teachers’ digital competencies and attitudes across different geographical locations. Given the non-normal distribution of the data, as determined by preliminary normality tests, traditional parametric tests would not be appropriate. Instead, non-parametric statistical methods compare the TPACK levels and attitudes between urban and rural mathematics teachers. The Mann-Whitney U test is utilised to infer differences between the two independent groups. Also, the Kruskal-Wallis H Test is applied to compare TPACK and attitudes across more than two groups based on demographic variables such as age. The choice of these non-parametric tests aligns with best practices for data that violate the assumption of normality, ensuring the robustness and validity of the findings (Cohen et al., 2018). These inferential analyses will contribute to a deeper understanding of the underlying dynamics of technology integration in urban and rural educational settings and the demographic influences on teachers’ TPACK and attitudes.

4 Finding

4.1 Descriptive findings of overall TPACK and attitude

The findings commence with an analysis of the overall descriptive statistics for TPACK and attitudes towards technology integration among a sample of 366 primary mathematics teachers. This aggregate data encompasses participants from urban and rural educational settings, offering a comprehensive view of the educators’ proficiencies and dispositions. From the collective data (Table 7), the mean scores across all respondents indicate a moderate level of engagement with TPACK and its related domains and attitudes towards technology integration in mathematics education. Specifically, the overall mean score for attitude was 3.43 with a standard deviation (SD) of 0.78, TPK had a mean of 3.60 (SD = 0.82), TK was at 3.44 (SD = 0.80), TCK averaged 3.44 (SD = 0.79), and TPACK had a mean of 3.36 (SD = 0.78). These statistics suggest varied technology integration within instructional practices across the sampled cohort.

In the subsequent stratified analysis, mean scores for urban teachers were consistently higher across all constructs than their rural counterparts, illustrating a potential divide in the adoption and attitude towards educational technology. Urban educators’ attitudes towards technology integration presented a mean score of 4.19 (SD = 0.46), significantly higher than that of rural teachers, with a mean score of 2.81 (SD = 0.44). A similar trend was observed in the TPK, TK, TCK, and TPACK constructs, where urban teachers scored means of 3.96 (SD = 0.56), 3.94 (SD = 0.55), 3.77 (SD = 0.64), and 3.87 (SD = 0.58), respectively, compared to rural teachers’ means of 2.74 (SD = 0.50), 2.78 (SD = 0.53), 2.81 (SD = 0.61), and 2.84 (SD = 0.59). These findings indicate the disparities in TPACK and attitudes towards technology integration between urban and rural primary mathematics teachers. The higher mean scores among urban educators suggest a more favourable disposition and possibly more significant access to technological resources and training opportunities that facilitate the integration of digital technologies into teaching practices. Subsequently, the study conducted a reasoning analysis to understand whether a statistically significant difference exists between urban and rural mathematics teachers’ TPACK and attitudes towards technology integration.

Table 7 Teachers’ TPACK and attitudes
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4.2 Comparative analysis: Urban vs. rural teachers’ TPACK and attitudes

The Mann-Whitney U test’s comparative analysis revealed significant differences in TPACK and attitudes towards technology integration between urban and rural primary mathematics teachers. The calculated effect sizes, derived from the standardised Z-scores and the sample size (\(r=\frac{\varvec{Z}}{\sqrt{\varvec{n}}}\)), provided a measure of the magnitude of these differences (Field, 2013). For TPACK, urban teachers exhibited a higher mean rank (243.58) than their rural counterparts (102.63), with a substantial effect size of -0.667, indicating a pronounced disparity favouring urban educators. Attitude towards technology integration also showed a significant urban advantage with an effect size of -0.843, one of the largest observed in the study, suggesting a markedly more positive disposition among urban teachers.

The TPK component followed a similar pattern, with urban teachers scoring higher mean ranks (252.69) than rural teachers (90.37) and an effect size of -0.768. TK and TCK constructs also displayed significant differences, with effect sizes of -0.745 and − 0.618, respectively, again indicating higher scores for urban educators (See Table 8). These effect sizes underscore statistically significant differences and substantial practical significance in the urban-rural divide in TPACK and attitudes towards technology integration. The negative values of the effect sizes across all constructs consistently indicate that urban teachers outperformed rural teachers in these domains. The findings from this comparative analysis suggest that geographical location significantly influences teachers’ technology integration capabilities and attitudes towards it. The higher scores among urban teachers may reflect broader access to resources, professional development opportunities, and exposure to technology-rich environments. In contrast, the lower scores among rural teachers might be attributed to limited resources, professional isolation, and other challenges inherent in rural educational settings.

Table 8 Mann-Whitney U test urban and rural teachers’ TPACK and attitude
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4.3 Impact of age on TPACK and attitudes towards technology integration

The impact of age on teachers’ TPACK and attitudes towards technology integration was examined through a series of non-parametric Kruskal-Wallis and Mann-Whitney U tests. These tests were selected due to the non-normal distribution of the data, offering a robust analysis of differences across age groups (Roni, 2021). The Kruskal-Wallis test results (Table 9) indicate significant differences across age groups for TPACK, TPK, TK, TCK, and attitudes, with p-values all less than 0.001. The mean ranks suggest that the youngest age group (G1: 20–29) consistently scored higher in their attitudes and knowledge domains compared to the older groups (G3: 40–49 and G4: 50–59), indicating a trend where younger teachers possess more favourable dispositions and knowledge towards technology integration.

Table 9 Kruskal Wallis test age groups comparing
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The Mann-Whitney U test outcomes among different age groups of mathematics teachers revealed distinct patterns concerning their TPACK and attitudes towards technology integration (See Table 10). Comparisons between the youngest age group (G1: 20–29) and the early middle-aged group (G2: 30–39) showed no significant differences across all constructs, indicating comparable levels of TPACK and attitudes towards technology. However, significant differences were noted in all constructs when contrasting G1 and G2 against the older age groups (G3: 40–49 and G4: 50–59). Younger teachers (G1 and G2) displayed more favourable responses than their older counterparts (G3 and G4), as reflected by p-values below 0.001 and moderate effect sizes. Moreover, the analysis between the two older groups (G3 vs. G4) did not demonstrate significant differences, suggesting similar TPACK levels and attitudes towards technology between these more experienced educators. These findings delineate a clear age-related trend in adopting technology integration practices, with younger educators showing a more substantial alignment with TPACK principles and a more positive attitude towards using technology in teaching mathematics. The absence of notable disparities between the two younger groups (G1 and G2) and between the two older groups (G3 and G4) points to a generational gap in technology integration in education.

Table 10 Mann-Whitney U test age groups comparing
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4.4 Gender differences in TPACK and attitudes

In examining the influence of gender on TPACK and its related constructs, the analysis revealed no statistically significant differences between genders in TPACK, TPK, TK, and TCK scores (See Table 11). For instance, for TPACK, females had a mean rank of 184.91 while males had a slightly lower mean rank of 176.16; however, the results were not statistically significant (p = 0.556). Attitudinal differences towards technology and pedagogy, as measured by the Attitude construct, indicated a more pronounced but non-significant trend. Females displayed a higher mean rank of 188.18 compared to 159.14 for males. The test statistic approached significance (p = 0.05). This finding suggests a slight trend where female teachers may possess a more favourable attitude towards technology use in their teaching, but this difference is not statistically robust. The TPK, TK, and TCK constructs followed a similar pattern, with females consistently showing higher mean ranks than males, yet without reaching statistical significance. For TPK (p = 0.088), TK (p = 0.229), and TCK (p = 0.354), the p-values were above the conventional threshold of 0.05. The gender comparisons across the constructs of TPACK, TPK, TK, TCK, and attitude indicate that while there may be slight differences in mean ranks with females scoring higher, these differences do not reach the level of statistical significance. Therefore, it can be concluded that in this study, gender does not significantly impact primary mathematics teachers’ perceptions and attitudes regarding integrating technology into their pedagogical practice. Also, this finding contributes to the body of research suggesting that gender may not be a determining factor in primary mathematics teachers’ TPACK.

Table 11 Mann-Whitney U test gender groups comparing
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5 Discussion

5.1 Dissecting the urban-rural divide in TPACK and technological attitudes

5.1.1 Addressing urban-rural TPACK disparity

The onset of the COVID-19 pandemic has accentuated the difficulty of comprehending the urban-rural divide in TPACK proficiency. As education systems worldwide grappled with sudden shifts to online and hybrid learning models, the disparity in technological readiness between urban and rural educators became not just apparent but also critical (Kara, 2021). In the post-pandemic era, this understanding is imperative for ensuring that all students have equitable access to quality education and that primary mathematics teachers are equipped to deliver it, irrespective of geographic location. The findings of this study, which reveal significant disparities in TPACK proficiency between urban and rural primary mathematics teachers, are particularly relevant in the current era of rapid AI development. Integrating digital technology, such as AI tools, into education can potentially revolutionise teaching and learning (Luttrell et al., 2020). However, its impact is likely unevenly distributed unless accompanied by concerted efforts to address existing inequities. As Guo and Wan (2022) have pointed out, failing to address existing educational inequities will exacerbate the digital divide as emerging technologies develop. Unless concerted efforts are made to bridge the gap in access and opportunity, integrating these advanced technologies into education systems will only marginalise those already disadvantaged further (Kalonde, 2017; Liu, 2021).

Furthermore, the findings reveal a significant contrast in TPACK proficiency between urban and rural primary mathematics teachers, with urban educators demonstrating higher levels of proficiency in the post-pandemic era. Indeed, urban primary schools are more likely to have access to advanced digital resources, enabling mathematics teachers to personalise learning, provide timely feedback, and create immersive learning experiences that engage students (Li & Ranieri, 2013; Warschauer & Matuchniak, 2010). By contrast, the lower TPACK proficiency among rural teachers and limited access to technology and professional development opportunities pose a significant challenge in the AI era. Without sufficient TPACK proficiency to effectively integrate digital resources into their teaching practices, rural educators may be left behind, unable to fully harness the benefits of transformative technology such as AI tools for their students (Gou et al., 2020). Therefore, addressing the urban-rural TPACK proficiency gap becomes crucial in the context of AI’s rapid advancement. This study highlights the need for targeted support and resource allocation in rural areas to level the playing field and ensure educators can leverage AI for more equitable and compelling learning experiences. It can be said that this study echoed previous study (Khong et al., 2023) that it is significant to address disparities in TPACK proficiency, especially in the context of the emerging AI era. However, our findings uniquely contribute by identifying specific factors within rural settings that hinder technology integration, such as limited infrastructure, lack of continuous professional development, and insufficient administrative support. These insights offer targeted solutions to address these challenges and promote effective technology use tailored to diverse educational contexts. By bridging the gap between urban and rural primary mathematics teachers and equipping them with the necessary knowledge to use digital resources in their teaching practices, they can unlock the full potential of these technologies to transform education for the better during the post-pandemic era.

5.1.2 Variance in attitudes towards technology

Attitudinal differences towards technology integration emerged as a pivotal aspect of this study, revealing distinct perspectives between urban and rural primary mathematics teachers. Urban primary mathematics teachers exhibited a more positive attitude towards technology, a trend that may stem from their regular and diverse interactions with technological tools in both professional and personal contexts. This finding aligns with Li et al. (2018), who posit that increased exposure to technology in urban environments fosters a more favourable perception and confidence in its integration into teaching methodologies. The urban teachers’ readiness to adopt and integrate technology can also be attributed to the more frequent and innovative professional development opportunities available in urban settings, as highlighted by Wu et al. (2022), which often focus on the latest technological advancements in educational settings. In contrast, even during the post-pandemic era, rural teachers displayed more reserved or less positive attitudes towards technology in education. This trend echoes the findings of Wu et al. (2022) and Kara (2021), who noted the challenges associated with limited access and support for technology use in rural schools. However, this study uniquely focuses on the post-pandemic context of primary mathematics education in China and examines urban-rural disparities in the adoption of AI tools. This investigation highlights distinct challenges and opportunities specific to integrating AI in rural educational settings, contributing new insights to the field.

Indeed, the less favourable attitudes in rural areas might be further compounded by a sense of apprehension or scepticism, potentially due to inadequate training, support from administrators, or prior negative experiences with technology (Ertmer et al., 2012; Howley et al., 2011). The work of Hohlfeld et al. (2008) also supports this, indicating that rural teachers often feel underprepared and unsupported in using technology, leading to reluctance or hesitancy in embracing technological tools in their teaching. Hence, the attitudinal differences between urban and rural primary mathematics teachers towards technology integration not only underscore the impact of environmental exposure and professional development opportunities but also highlight the critical need for tailored support and resources in rural areas to bridge the digital divide and empower all educators to harness technology’s potential in enhancing teaching and learning (Gou et al., 2020).

5.2 The influence of Age and gender in technology adoption

The impact of age on primary mathematics teachers’ engagement with technology is a pivotal aspect of this study, unveiling distinct generational differences that are noteworthy and have practical implications for the field of education. This study’s findings, which reveal that younger primary mathematics teachers, particularly those falling within the 20–29 and 30–39 age brackets, exhibit a higher level of proficiency in TPACK and harbour more favourable attitudes towards integrating technology into their teaching methods, are particularly significant when compared to their older counterparts in the 40–49 and 50–59 age groups. This observed trend is consistent with the conclusions drawn by previous researchers who have consistently noted that younger educators often display greater adeptness and receptivity towards incorporating new technologies into their teaching practices (Eickelmann & Vennemann, 2017; Yao & Zhao, 2022). The reasons behind these generational differences could be multifaceted, ranging from varying degrees of exposure to technology during professional training and early career stages to the innate ability of younger individuals to adapt to and embrace technological advancements more readily (Song et al., 2021). The higher level of proficiency observed among younger teachers underscores the paramount importance of continuous professional development that evolves in tandem with technological progressions. This notion is further supported by studies conducted by Li (2023) and Xu and Zhu (2020), which emphasise the exigency of ongoing training programs to keep pace with rapid technological changes. Differently, a unique finding of this study highlights the crucial importance of differentiated professional development in the contemporary era, where AI tools are increasingly integrated into primary mathematics education. Such tailored support ensures primary mathematics teachers, regardless of age, can effectively utilise AI tools in their teaching practices.

Regarding gender, this study’s findings, revealing no significant differences in TPACK and attitudes towards technology integration between male and female primary mathematics teachers, suggest a notable level of gender parity in the post-pandemic era. This observation challenges earlier assertions in the academic literature that indicated pronounced gender-based disparities in technology use and attitudes in educational contexts. For instance, Cai et al. (2017) conducted a meta-analysis covering studies from various countries and highlighted that male teachers exhibit more confidence and willingness to integrate technology into their teaching practices compared to their female counterparts. However, the findings of this study highlight a diminishing gender gap in technology integration within education. This emerging trend could reflect the evolving landscape of educational technology, where gender-neutral access and inclusive professional training programs have become more prevalent. The absence of significant gender differences in this study is particularly noteworthy, suggesting that gender may not be as influential in technology adoption and attitudes as it was once perceived (Li, 2023). This finding is crucial as it supports the narrative of creating equitable access and opportunities in educational technology. Therefore, the study highlights the crucial need for technology integration strategies and professional development initiatives that are accessible and inclusive, catering to primary mathematics teachers regardless of gender. These initiatives ensure Chinese primary mathematics teachers can equally benefit from technological advancements, fostering a teaching workforce adept at navigating the digital landscape in education.

5.3 Implications

As revealed in this study, the disparities in TPACK proficiency and attitude between urban and rural teachers are alarming and demand immediate attention from educational practitioners and policymakers. These differences highlight a significant digital divide that needs to be urgently addressed to ensure equitable access to quality education, regardless of geographical location. Several strategies can be employed to bridge this divide from an educational practice perspective. Firstly, there is an urgent need for targeted professional development programs designed to equip rural primary mathematics teachers with the necessary skills and knowledge to integrate technology into their teaching practices effectively. These programs should not only focus on technical proficiency but also aim to foster a positive attitude towards technology among rural primary mathematics teachers. For example, a proposed initiative could involve workshops and online courses tailored for rural primary mathematics teachers, focusing on practical applications of AI tools and strategies to overcome technological barriers. Peer mentoring from experienced urban primary mathematics teachers could also enhance rural educators’ skills and build a supportive community. Such interventions are crucial for bridging the TPACK gap and ensuring equitable, high-quality, technology-enhanced education for all students. Also, administrators and school leaders influence primary mathematics teachers’ attitudes towards technology adoption. Their support and adequate training and resources can significantly impact teachers’ willingness to embrace technological tools in their classrooms (Hohlfeld et al., 2008). Moreover, it is essential to recognise that the digital divide is not just about access to technology but also the ability to use it effectively. Therefore, professional development programs should go beyond mere technical training and include pedagogical guidance on integrating technology meaningfully into mathematics curriculum and instruction. Importantly, these programs should consider the generational differences among primary mathematics teachers and cater to the needs of older educators who might require additional support and training to keep pace with technological advancements.

From a policymaking perspective, addressing the digital divide requires a comprehensive approach that includes equitable resource allocation, infrastructure development, and ongoing support for rural areas. Policies should aim to provide universal access to technology and sustained support and training for rural educators (Gou et al., 2020). This includes investing in infrastructure development, such as improving Internet connectivity and providing devices and software to rural primary schools. Furthermore, policies should encourage collaboration between urban and rural primary schools to share resources and practices, fostering a more inclusive educational environment. For example, urban primary schools with advanced technological setups can partner with rural primary schools, facilitating the exchange of digital resources and co-hosting training sessions for mathematics teachers. Such initiatives not only enhance TPACK of rural educators but also build a supportive network that promotes continuous learning and innovation in teaching practices. Indeed, bridging the digital divide between urban and rural primary mathematics teachers requires a multifaceted approach encompassing educational practice and policymaking. By investing in targeted professional development programs, fostering positive attitudes towards technology among rural primary mathematics teachers, and implementing equitable policies that provide access to technology and sustained support, the urban-rural gap in TPACK and technology integration can be significantly narrowed. These measures will ensure that all educators are equipped to leverage the power of digital resources for enhanced teaching and learning. This study’s findings provide valuable implications for the global context, highlighting the need for inclusive and equitable strategies to address educational disparities in technology integration worldwide.

5.4 Limitations and directions for future research

While this research provides valuable insights into the TPACK proficiency and attitudes towards technology among primary mathematics teachers in urban and rural China, its contribution to the existing literature is limited due to its focus on a specific geographic region (Chongqing). Technology integration in education is vast and diverse, with various cultural, economic, and technological contexts worldwide. Hence, future research should focus on exploring the applicability of these findings to other regions or countries with varying educational systems and technological landscapes, as the present study may have limitations in this regard. Also, the study’s cross-sectional nature poses a limitation, as it captures only a snapshot of teachers’ technological proficiency and attitudes at a particular time. Given the rapid pace of technological advancements in education, longitudinal studies are needed to track changes over time and provide a more comprehensive understanding of educators’ adaptation to ongoing technological evolution.

Future research should also consider the rising importance of AI tools in education and investigate mathematics teachers’ adoption and utilisation of these tools. Understanding the impact of AI on teaching methodologies and learning outcomes can provide valuable insights into the effective integration of advanced technologies in educational settings. Additionally, international comparative studies would be beneficial in examining technology integration practices across different countries and educational systems, allowing for a broader perspective on global trends and challenges. By acknowledging these limitations and pursuing future research in these areas, scholars can build upon the findings presented in this study and contribute to a more nuanced and comprehensive understanding of technology integration in education. This, in turn, can inform more effective strategies for enhancing teachers’ technological proficiency and fostering positive attitudes towards technology integration, ultimately improving educational outcomes in diverse contexts worldwide.

6 Conclusion

This study embarked on a comprehensive exploration of the TPACK and attitudes towards digital technology integration among primary mathematics teachers in China, with a specific focus on the contrasts between urban and rural educators. At its core, the research sought to unravel the complexities of technology integration in the context of the rapid digital advancements catalysed by the COVID-19 pandemic and to understand how demographic factors such as age and gender intersect with these educational dynamics. The findings revealed significant disparities in TPACK proficiency between urban and rural primary mathematics teachers, underscoring a persistent digital divide of critical importance in the current era of educational technology (Gou et al., 2020; Guo & Wan, 2022). Urban primary mathematics teachers demonstrated higher proficiency and more favourable attitudes towards digital technology, reflecting their greater access to technological resources and professional development opportunities. Conversely, rural primary mathematics teachers faced challenges marked by limited access and support, translating into lower proficiency and less positive attitudes towards technology integration. This urban-rural divide serves as a stark reminder of the ongoing need to bridge educational inequities, particularly in an age where digital technology rapidly transforms educational paradigms.

The study also brought to light interesting insights regarding the impact of age on technology adoption, with younger primary mathematics teachers showing greater ease and enthusiasm in integrating technology compared to their older counterparts. Notably, the research found no significant gender differences in TPACK proficiency or attitudes, suggesting a trend towards gender parity in technology integration in primary mathematics education. The study emphasises the importance of targeted professional development programs, especially for rural educators, and the need for policies that ensure equitable access to technology and support across all educational environments. The findings advocate for a nuanced approach to technology integration that is sensitive to the unique challenges and needs of different educator demographics. Therefore, it can be concluded that this study contributes to the broader discourse on technology integration in education, highlighting the critical role of geographical, generational, and demographic factors in shaping primary mathematics teachers’ engagement with technology. The insights gleaned from this research inform current educational practice and policy and pave the way for future research endeavours aimed at fostering a more equitable, inclusive, and technologically adept educational landscape. As the world continues to navigate the complexities of the post-pandemic era, the lessons learned from this study will be invaluable in guiding the evolution of educational practices and policies to meet the demands of an increasingly digital society.