1 Introduction

In an era characterized by evolving educational paradigms, including the integration of artificial intelligence (AI) in education (Celik et al., 2022), educators face one of the greatest challenges: preparing future generations to navigate ever-growing apparent educational inequities (Fraillon et al., 2019). In meeting these challenges, ongoing teacher professional learning (OTPL) may significantly affect the quality of education and student outcomes (Hidajat et al., 2023). This may be especially true when OTPL is characterized by reflective practices and pedagogical reasoning and action (PR&A—Loughran, 2019). In this paper, we explore the significance of OTPL from the onset of preservice education, examining ‘what works and why’. Furthermore, the paper offers practitioners and researchers adaptive tools for OTPL, taking into account diverse proficiency levels and unique requirements. These tools are especially pertinent given the evolving educational landscape driven by digital technologies.

As shown in the graphic organizer (Fig. 1), we will outline three drivers behind changes in education as a result of technological advancements, which influence OTPL dynamics and potential pathways to educational equity and quality. These drivers include: (a) the growing prominence of big data and learning analytics, (b) the increasing use of Artificial Intelligence (AI), and (c) the shifting contours of the professional teacher identity. These factors were selected due to their considerable impact on education, both individually and in their interconnectedness.

Fig. 1
figure 1

Graphic organizer of the paper structure: drivers’ influence on OTPL and ultimately education; boundary object’s bridging action to guide changes towards equitable and quality education through their foundations in OTPL

Full size image

Big data and learning analytics may provide unprecedented insights into student learning behaviors and characteristics. This can facilitate personalized learning experiences and data-informed decision-making (McCarthy et al., 2023; Ouyang & Jiao, 2021). However, there are valid concerns about data privacy and the potential for undermining student confidentiality and institutional trust (Ahn et al., 2021).

AI technologies, often leveraging big data, offer opportunities for sophisticated analysis and interpretation of educational data, potentially improving teaching practices by identifying patterns to optimize student engagement (Lee et al., 2023) or automating tasks. Yet, there are concerns about algorithmic bias and the perpetuation of inequities of access (Williamson et al., 2024; Zhang, 2022).

Teacher identities are also shifting, influenced by technological advancements and evolving educational paradigms (Rosdi, 2020). The roles teachers assume and the changes necessary to be effective are continuously being revised (Arantes, 2022). While these changes offer potential improvements, it is crucial to acknowledge the growing concerns about discrimination, privacy, student learning, and teacher agency and reasoning, as highlighted by recent reports (Williamson et al., 2024). Moreover, the three phenomena are deeply interconnected in their driving force to impact education. Big data and learning analytics provide the data infrastructure for AI to carry out analyses and provide algorithms to interpret such data in turn. Both are challenging the understanding of the teaching profession, possibly expanding the skillset required of a teacher (Starkey, 2020) or requiring a new positioning of teachers in relation to technologies (Lai & Jin, 2021).

We argue for the necessity of OTPL and support to intercept these drivers’ actions to maximize their potential while limiting their potential disruptive effects on education in terms of ethical and equity issues (Cardona et al., 2023; De Groot et al., 2023). Given the drivers’ non-educational core nature, in this conceptual paper we aim to identify and explore the role of boundary objects in OTPL contexts to steer the modifications prompted by these drivers towards equitable and high-quality education in digital realities.

Building on Star’s conceptualization of the term (1989, 2010), Akkerman and Bakker (2011) contend boundary objects refer to artifacts, concepts, or tools that can be comprehended and utilized by diverse communities simultaneously. Their contention is aligned with Star’s (2010) conceptualization of boundary objects as means to mediate collaboration and information exchange across different social worlds within the educational context. We argue for dashboards, AI-supported OTPL and digital communities of practice as specific boundary objects, as they all are intended to serve and facilitate collaboration and knowledge sharing among educational communities. We argue that these three boundary objects may help to bridge gaps between various stakeholders (e.g. students, educators, administrators, developers) by maintaining a shared structure while allowing for diverse internal contents and uses. This paper underscores the significance of employing specific boundary objects to navigate evolving educational landscapes, to achieve high-quality, equitable education. Specifically, in this paper, technological boundary objects are considered to harness the potentialities of the drivers to possibly facilitate communication, collaboration, and understanding among diverse groups and communities in education. Through the use of technology, information, data, and knowledge can be exchanged between disparate groups, facilitating meaningful interactions (Akkerman & Bakker, 2011; Fleischmann, 2006).

In the context of this paper, three specific boundary objects— i.e. dashboards, AI-driven OTPL environments, and digital communities of practice—align with the broader understanding of technology-supported facilitators of human collaboration and information exchange. These three boundary objects were specifically chosen as they turn potential technological issues into potentialities for equitable and quality education, namely information sharing and collaboration. The dashboards, AI-driven OTPL environments, and digital communities of practice align with Star’s (2010) and Akkerman and Bakker’s (2011) definitions of boundary objects as they are meant to (a) facilitate collaboration and information sharing across different educational stakeholders; (b) provide standardized structures that are adaptable to various specific needs and contexts; (c) serve as methodological tools that translate and transform educational data and practices. Further, these tools, discussed as essential in navigating shifting perspectives on OTPL, leverage technology to mediate the relationship between educational drivers of transformation, including big data, AI, and evolving teacher identities.

Underlying the entire article is the theoretical foundation of equitable education (Willems et al., 2019). In her 2010 article, “This is Not a Boundary Object: Reflections on the Origin of a Concept,” Susan Leigh Star clarifies the concept of boundary objects, originally developed with James Griesemer in their 1989 paper. She discusses the multifaceted use and interpretation of boundary objects over time and across different fields, emphasizing their role in facilitating cooperation among diverse groups without requiring consensus. Equity provides the lens through which the intricate relationship between the boundary objects and evolving educational dynamics is examined, further emphasizing technology’s potential role in achieving quality and equitable education.

2 Digital Realities in Education

The influence of digital media on educational equity is vast and varied (Gottschalk & Weise, 2023). With their increasing affordability and ubiquity, digital technologies may enable opportunities for a broader population to access quality education, thereby reducing social and economic barriers (Evans & Annan, 2018). However, it appears that the potential is not being sufficiently utilized and may even contribute to a growing digital divide (van Deursen & van Dijk, 2019). Educational inequities are currently evident at various levels: students from less privileged households lack access to high-quality digital devices, prefer to use digital media for entertainment over education, and ultimately have lower digital skills than children and adolescents from more privileged households (Senkbeil et al., 2019; Fraillon et al., 2019).

To navigate these challenges in the pursuit of educational equity, OTPL holds the crucial role to provide teachers with the adaptive expertise they need (Timperley, 2023). OTPL serves as a linchpin in the quest for educational equity by enabling teachers to adapt to the changing demands of society and empowering them to create inclusive learning environments in which students can thrive. In this way, OTPL becomes a catalyst for breaking down barriers to educational access and success. In the following section, we provide an overview of the drivers influencing how teachers may engage with OTPL and ultimately impact the equitable development of students in a digital world (European Commission et al., 2020).

2.1 Drivers of Change in Digital Realities

Over the past decades, the use of digital tools in education has become mainstream. However, the waves of emerging changes in these tools can be overwhelming for educators as well as for the overall education systems (Bonk & Wiley, 2020). There is no doubt that these digital realities impact classroom practices but require careful adaptations to be used equitably in education. The three drivers of change identified in this paper are: big data and learning analytics, AI, and teacher professional identity. The three drivers are interdependent and highlight some of the most pressing issues, with critical impact regarding digital tools for education. The three drivers involve a response to the inundation and impact of data on education, especially with regards to their potential to promote equity. Drivers of change have the power to change education (Zawacki-Richter et al., 2019) through technology integration. These drivers may act as catalysts, propelling educators and institutions toward effective technology integration, as long as they are thoroughly explored in their potentialities and risks and harnessed from a pedagogical standpoint.

Big data and learning analytics are often positioned as means that allow educators to measure student performance more accurately and efficiently (Fischer et al., 2020). Through the collection, analysis, and visualization of student data, educators may improve their timely, data-driven decisions to optimize instructional strategies and learning personalization (Knobbout & Van Der Stappen, 2020). This could be particularly fostered, e.g., by involving teachers in the design of these learning analytics systems (Dyckhoff et al., 2012). However, significant concerns emerge regarding the very data collection and analysis, due to privacy concerns and possible misuse of users' personal information and digital footprints (Harahap & Fithriani, 2024; Regan & Jesse, 2019). Ethical considerations surrounding informed consent and the creation of user profiles become crucial (Akgun & Greenhow, 2022; Cardona et al., 2023). Aggregating data may lead to unintended consequences, such as decisions being made on inaccurate or discriminating data based on algorithms with built-in bias (Cardona et al., 2023). Khan et al. (2024) argue that these biases may further exacerbate inequities, particularly among marginalized student populations. This underscores the need for critical assessment and the mitigation of potential risks within data analytic practices. For example, Afreen et al. (2024) emphasize the necessity for ethical standards and data governance to protect sensitive information and prevent data breaches. The inclusion of big data and learning analytics in the classroom requires a careful OTPL approach that combines technical proficiency, pedagogical insight, and a supportive community to empower teachers in improving instructional practices (Bondie & Dede, 2024). For example, OTPL that incorporates training that focuses on the “how to”, as well as open discussions focusing on, e.g., ensuring the privacy and security of the data collected, may be of vital importance (Khulbe & Tammets, 2023).

AI-supported instruction has been predicted to transform education (Zawacki-Richter et al., 2019). AI may provide educators with opportunities to plan, implement, and assess instruction more effectively (Celik et al., 2022), along with fostering personalized learning experiences and administrative task automations (Alam, 2021). However, AI-based tools have received little attention from the pedagogical perspective, although teachers are among the most crucial stakeholders in AI-infused education (Celik et al., 2022; Seufert et al., 2020). Reflective engagement with AI is both a prerequisite and a challenge, given the rapid and intricate nature of technological advancements in this domain. As AI-technologies pivot on big data, they may reflect and perpetuate the risk of algorithmic biases, societal inequalities and prejudices present in the data on which they are trained (Zhang, 2022). Hence, overreliance on AI use may lead to the exacerbation of inequities in educational practices with unintended loss of teacher/student agency (Cardona et al., 2023). Moreover, AI-technologies today lack a firm understanding of human learning processes and are not based on sound pedagogical principles. Overemphasis on efficiency and scalability through automation may overlook the essential role of human interaction, empathy, and creativity in effective teaching and learning experiences (García-Pérez, et al., 2016). Thus, careful considerations of pedagogical context and human expertise for AI-driven solutions are needed (European Parliament, 2024; Williamson et al., 2024). We argue that OTPL is a powerful environment for educators to address these complex risk–benefit dynamics (Mishra et al., 2023).

Teacher professional identity naturally evolves over time as experience and reflection in education shift in a dynamic process (Sutherland et al., 2010). Teachers’ professional identity also evolves as they move through their programs of teacher education and assume teaching positions in their school environments (Beauchamp & Thomas, 2009; Rodrigues & Mogarro, 2019). The influx of digital realities into the classroom introduces new challenges, potentially diminishing the teacher role into that of a data analyst and increasing the workload due to evolving technological requirements (Cardona et al., 2023). The digital realities of the twenty-first century have changed so quickly that these identities need further support in tackling the changes in their roles and expectations. The digitalization rush may limit teachers' autonomy and expertise, with instructional formats driven more by economic or statistical trends than educational dynamics. Consequently, teachers may face challenges in adopting new technologies and pedagogical approaches. Factors impacting the development of a teacher’s identity include a sense of appreciation, of connectedness, of competence, of commitment, and imagining a future career trajectory (van Lankveld et al., 2017). These identities do not develop in a vacuum, but in a context that considers social and cultural forces as well as the meaning and associations that other people assign to the role of the teacher (Chiu et al., 2024). Given the significant impact of teachers' identities on their utilization of technology for different instructional purposes and quality (Lai & Jin, 2021), robust OTPL becomes imperative, despite potential limited resources, time constraints, and institutional barriers.

We argue that these drivers’ impact OTPL both independently and via their interconnectedness, challenging teachers’ conceptualizations and practices, and learning opportunities in the digital age. A roadmap for successful technology integration in education can be developed by identifying and leveraging these drivers in OTPL.

2.2 Navigating Change in Teacher Professional Practice

In this section, we delve into the influence of the three drivers of change–big data and learning analytics, AI, and changing teacher identities – on various facets of teachers’ professional practice with a particular focus on OTPL (Timperley, 2015) and its dimensions of PR&A (Loughran, 2019) and reflective practices (Stăncescu et al., 2019).

OTPL is “an active process of systematic inquiry into the effectiveness of practice to improve student engagement, learning, and well-being” (Timperley, 2015, p. 798). This definition underscores OTPL’s dynamic and reflective nature, pivotal in equipping educators with the knowledge and skills necessary to thrive in changing realities (Forkosh-Baruch et al., 2021). An ongoing orientation to “continued [teacher] professional learning requires an individual teacher commitment to being a student of one’s own practices and the nuances of student learning, and a collective professional investment in the dynamic nature of professional expertise” (Brown et al., 2021, p. 3).

OTPL’s optimal outcome is adaptive expertise: an identity shift beyond the simple change of single practices that signifies a meaningful shift in identity, beyond the acquisition of pedagogical strategies (Timperley et al., 2017). Teachers gain a deep, sophisticated, and contextualized understanding of student learning and a deep commitment to supporting all students. Brown et al. (2021) contend that OTPL requires teachers to engage in collaborative communities of practice where teachers reflect on their ways through reviewing their pedagogical reasoning and actions.

Pedagogical reasoning and action (PR&A) refers to cognitive processes teachers use when making decisions about instructional strategies, content, assessments (Loughran, 2019) and—in today’s digital era—technology. Tackling teacher assumptions and biases regarding learning and technology is crucial to ensuring teacher PR&A remains inclusive, equitable, and responsive in the digital landscape (UNESCO, 2019). OTPL environments support, extend and enhance teachers’ PR&A through ongoing inquiry and iterative reflection cycles in and around digital contexts.

Reflective practice is a pillar of OTPL and involves introspection, self-evaluation, and continuous improvement, both individually and collaboratively (Stăncescu et al., 2019). Especially in digitally saturated settings, educators must rethink their professional identities, roles, and teaching practices in light of changing paradigms and adapt accordingly (Drossel & Eickelmann, 2017; Darling-Hammond & Flook, 2020; Howard & Tondeur, 2023).

In the context of OTPL, reflective practice and PR&A are essential components for professional growth and development. Pedagogical reasoning helps teachers make decisions about instructional strategies, content, and assessments, while reflective practice involves introspection, self-evaluation, and continuous improvement. Successful OTPL engages teachers in collaborating with colleagues to consistently evaluate and refine their PR&A, all with the overarching goal of creating more conducive and effective learning experiences for each and all students (UNESCO, 2019). This process is not confined to a single event or training session; rather, it signifies an ongoing commitment to professional growth and a dedication to better meeting the evolving needs of diverse learners in today’s dynamic landscape (European Commission, Joint Research Centre et al., 2020).

3 Objectives and Research Question

This article considers the impact of the three primary drivers of change–namely, big data, AI, and teacher professional identity–on OTPL and its components PR&A and reflective practice (see Fig. 1). It aims to uncover how such impact and follow-up implications for practice may be channeled towards high quality, equitable education through the implementation of boundary objects (Akkerman & Bakker, 2011).

Hence, the research question leading this narration is:

4 How do boundary objects intersect with OTPL in response to the key drivers of educational transformation and how does this intersection impact educational equity?

Therefore, specific boundary objects will be described, i.e., dashboards, epistemic network-based educational strategies and digital communities of practice. These will be analyzed in their intersection with OTPL in response to the key drivers of educational transformation, including big data, AI, and evolving teacher professional identity. Finally, these boundary objects will be evaluated in their impact on educational equity.

5 Boundary Objects to Scaffold Shifts to New Educational Realities

Learning boundaries include the teachers’ expertise, identity and external impacts on the teaching profession (Akkerman & Bakker, 2011). However, since teachers’ identity includes multidisciplinary, pedagogical, and didactic components, learning also requires mobility and flexibility between teaching practices (Hermans & Hermans-Konopka, 2010). Boundary objects, a concept developed by Star (1989, 2010) and adapted for education by (Akkerman and Bakker (2011), may describe such mobility, enabling effective teaching and learning endeavors. Boundary objects are entities, ideas, or instruments which are adaptable to be comprehended by diverse communities or viewpoints, promoting cooperative efforts to bridge well-understood concepts/practices with new, less defined or understood ones. In this paper, we champion the creation of boundary objects that capitalize on the potentials of the drivers mentioned above in OTPL by enhancing teachers' PR&A and reflection. This continuous professional learning trajectory is considered from its inception during initial teacher training to its application throughout the teaching career.

Boundary objects are those objects that “both inhabit several intersecting worlds and satisfy the informational requirements of each of them.… [They are] both plastic enough to adapt to local needs and the constraints of the several parties employing them, yet robust enough to maintain a common identity across sites. They are weakly structured in common use, and become strongly structured in individual site use” (Star, 1989, p. 393). This makes boundary objects platforms for knowledge integration across different problem-solving contexts (Carlile, 2002). They include artifacts and concepts that enable integrating knowledge from different disciplinary areas—e.g., simulation models, databases, and software platforms (Caccamo et al., 2023) crossing boundaries beyond one’s comfort zone, and thereby developing expertise beyond the classic professional parameters.

In education, boundaries may refer not only to teachers’ domain-specific expertise, but also competence reach in the digital age. The term “boundary crossing” refers to how professionals – in this case, teachers – enter unfamiliar territories, which may not be part of their core qualification (Suchman, 1994). Boundary crossing enables teachers to face and overcome challenges in which a combination of different areas of knowledge and proficiency is needed, thereby bridging practices that intersect (Engeström, 2004). Establishing continuity across (physical and virtual) spaces and mindsets, boundary objects and boundary crossing practices for teachers should be encouraged (Akkerman & Bakker, 2011; Pimmer, 2016).

In our exploration, technology may serve as a boundary object in the dynamic educational landscape, and we identify some specific tools that may help in shaping and mediating teaching and learning (Malinverni et al., 2021). We highlight three boundary objects, each underpinned by distinct drivers of change. Being technologically based, they connect deeply to the digital drivers of educational transformation described earlier. However, being grounded and developed within a strong OTPL perspective, they are meant to stir digital-influenced educational change towards equity and quality, curbing at least some of the major risks outlined above.

The first boundary object connects with the big data and learning analytics driver, realized in a digital dashboard. Powered by big data and learning analytics, it acts as a compass for educators coping with the complex terrain of contemporary education (Ogata, 2021). We argue that dashboards serve as boundary objects by bridging big data analytics and equitable education in an OTPL setting, offering a shared interface that translates complex data into accessible visual formats for diverse stakeholders.

Next, we explore AI-driven OTPL environments, specifically integrating epistemic network analysis (ENA). Such an environment serves as a boundary object by bridging artificial intelligence and equitable education through personalized learning algorithms that adapt content to individual student needs, facilitating tailored learning experiences. ENA can further develop and support teachers’ PR&A within OTPL. By potentially harnessing AI-based data, visualizations like ENA’s can highlight nuanced patterns and connections in a teacher’s educational context, highlighting nuanced patterns and connections involved in their PR&A (Trevisan et al., 2021; Yang et al., 2021b).

Lastly, we connect to evolving teacher identities and their impact on educational practices. Here, a digital community of practice emerges as a boundary object to foster meaningful OTPL and collaboration among educators who are navigating changes in their roles and professional selves. Digital communities of practice serve as boundary objects by bridging the shifting contours of professional teacher identity and equitable education, creating physical/metaphorical platforms for teachers to share knowledge, resources, and support, fostering collaborative professional learning and development.

5.1 Equity-Driven Learning Dashboard

Dashboards may serve as a dynamic boundary object in supporting, extending and enhancing data-driven decision-making to transform pedagogical practices responsively (Luan et al., 2020). As boundary objects, they present different data sets and metrics depending on the user’s needs (e.g., administrators, teachers, students) while maintaining a common structure. They provide a standardized way to visualize and interact with data, making it easier for various stakeholders to understand and use the information. Furthermore, they may translate complex data into accessible visual formats, facilitating decision-making and collaboration (Star, 2010). Moreover, can analyze data according to indicators of inequity, i.e., race or socioeconomic status, with an emphasis on student outcomes, learning strategies, time management, and prior knowledge. Then, in a cycle of research and development, these can be refined according to further needs (Sloan-Lynch & Morse, 2024).

Teachers find themselves facing the challenge of harnessing technology like learning analytics to foster equitable and pedagogically sound learning environments (OECD, 2016; UNESCO, 2019). The concept of an Equity-Driven Learning Dashboard, inspired by the work of Ogata and his team (Luan, 2020; Ogata, 2021; Yang et al., 2021a, 2021b), has the potential to guide and support teachers in their pedagogical shifts to more personalization of learning while harnessing the power of big data and learning analytics. It may offer real-time access to comprehensive data, illuminating student processes as well as learning outcomes, and areas of improvement (Valle et al., 2021). In an OTPL perspective, this boundary object may not only inform professional learning but also steer teachers’ PR&A by placing real-time data-driven decision-making at the forefront of their designs for learning (Luan et al., 2020). However, such potential is contingent on a sound OTPL pedagogical intentionality to curb the risks a learning analytics-based dashboard may carry (Seufert et al., 2020; Verbert et al., 2013).

Ongoing research and open dialogue among educators, technologists, and policymakers are paramount in this endeavor. For example, Verbert and colleagues (2020) used a participatory strategy to extract successful patterns from existing dashboards and develop evaluation strategies grounded in learning and visual sciences. They argue for participatory design methods to help tailor dashboards to the needs of stakeholders, capitalizing on learning analytics’ multimodal data acquisition techniques, while curbing overreliance on technology which may undermine teachers’ professional judgment. This approach can illustrate an OTPL perspective in dashboards by incorporating PR&A and reflective practices with the goal to maintain equilibrium between evidence-based insights and preserving teachers’ unique understanding of students and their context (see also Wiedbusch et al., 2021). Once again, the focus of these dashboards is to support better human sense-making and decision-making by getting stakeholders to collaboratively discuss and interpret data (Verbert et al., 2013).

5.2 Emerging OTPL Environments and their Realistic Potential for Change

The second boundary object championed capitalizes on the AI driver of change. Specifically, AI-based technology’s potential to boost data visualizations like the ones provided by Epistemic Network Analysis (ENA)—a tool from the field of Quantitative Ethnography (Shaffer et al., 2016). We argue that ENA may act as a boundary object in OTPL because when aligned with pedagogical intent, it serves as standardized form and common platform that adapts content based on data-driven insights. Hence, an AI-based OTPL environment using ENA has the potential to offer personalized learning experiences, tailored to individual users while maintaining a unified framework for content and feedback delivery. Further, it may capitalize on AI to enable human interpretation of diverse professional development and needs, thereby mediating the relationship between educational data and instructional strategies, and promoting equitable education by ensuring all learners have access to personalized content.

ENA quantifies and elucidates visually the structure and quality of discourse within educational settings (Shaffer et al., 2016). This technique’s contribution to education is full of potential as it offers visual insights into the complexity of learning interactions. Possibly leveraging AI mechanisms in the near future, ENA in a OTPL perspective could be adopted to scaffold teachers’ reflections on action through the visualization of their elicited PR&A (Phillips & McDougall, 2024; Trevisan, 2019). ENA visualizations account for the teacher/learner’s conceptualizations, attitudes, motivations, and reasons for action as they inform the conversational dataset used. As educators and researchers engage with this boundary object in an OTPL setting, they could be scaffolded in deciphering pedagogical puzzles, prompted to discuss inquiries and reflections, and ultimately enhance teachers’ PR&A. As we consider the integration of ENA into OTPL and PR&A frameworks, it is imperative to leverage technology (e.g. AI) while remaining cognizant of its emerging state in education. Once again, the practicalities of this AI-based technology in OTPL necessitate a prudent approach, one that acknowledges current technological capabilities and rigorously assesses potential risks, since the current state of generative AI may need further development for its application in classrooms (Phillips et al., 2021; Celik et al., 2022).

The use of ENA within an AI-based OTPL environment as a boundary object aims to bridge the gap between complex data and actionable educational insights. To do so, OTPL must focus on mediating the opportunities of incorporating AI-infused boundary objects to improve teaching (Wang et al., 2023).

The overarching goal remains clear: to ensure that technological advances in education are employed thoughtfully, ethically, and to the tangible benefit of all learners' educational experiences.

5.3 A Boundary Digital Community of Practice for Change

The third boundary object is Community of Practices (CoPs), which consist of diverse members (e.g. students, teachers, educators, administrators) who share knowledge and practices but may have different specific interests or expertise. These communities often use standardized tools and platforms (e.g., forums, shared documents) to facilitate interaction. Most importantly, they function as bridging spaces where knowledge is exchanged and new practices are developed, translating individual experiences into collective learning (Star, 2010). By fostering inclusive collaboration and ensuring that diverse perspectives are represented, CoPs promote equitable education.

CoPs aim to offer a nurturing environment of OTPL, reflection, and collaboration. This boundary object not only informs teachers’ PR&A but also extends its influence into the very core of professional teacher identities (Timperley, 2023). CoPs, as outlined by Lave and Wenger (1991), involve people with shared interests engaging in collective learning over time to address challenges and build knowledge (Wenger & Snyder, 2000). Research highlights CoPs’ focus on learning, knowledge exchange, and innovation (McDonald & Mercieca, 2021). CoPs are a dynamic space for engagement which is vital in teacher training, fostering learning from initial education to career stages, with positive impacts on teaching and student achievement (Vangrieken et al., 2017; Wei et al., 2021). Furthermore, they encourage reflection and PR&A, arriving to connect to the redefinition of teacher identity in digital times (McDonald & Mercieca, 2021).

A Digital Community of Practice (D-CoP) for teachers, as defined by Trewern and Lai (2001), involves teachers interacting digitally to access teaching resources, source new ideas, and engage in innovative teaching practices. Studies have investigated the nature and relevance of D-CoPs in teacher training and career development over the last two decades (e.g., see Morgado et al., 2020; Ulla & Perales, 2021).

A D-CoP supports teachers' OTPL as they navigate shifts in their PR&A and reflection strategies, responding to changes in their professional identity in the digital age. It seems particularly fitting to consider (D-)CoPs as boundary objects (Akkerman & Bakker, 2011), facilitating collaboration and knowledge sharing among/within groups with diverse perspectives and experiences. Nykvist and Mukherjee (2016) emphasized the need for teachers to develop a professional identity aligned with technology-infused pedagogical practices from their initial teacher education. As teacher identity evolves, so do PR&A and reflective practices (Felix, 2020). As a result, teachers’ self-perception within the educational ecosystem can impact how they approach OTPL and adapt to technological advances (Rosdi et al., 2020).

D–CoPs extend well beyond individual teaching practices, influencing broader pedagogical paradigms and fostering a collaborative culture of professional growth (Dille & Rokenes, 2021). In these digital spaces, educators can access a wealth of resources, participate in meaningful dialogues about their experiences, and reflect on their practices in light of new insights and challenges presented by the integration of technology in education (Kirschner & Lai, 2007).

Diverse OTPL educational settings may require different applications of D-CoPs. For instance, educators may discuss the implementation of digital tools in the classroom, share experiences with integrating technology into their teaching strategies, and collaborate on creating resources that are accessible and equitable for all students. They may also engage in mentorship roles, providing guidance and support to less experienced teachers navigating the complexities of digital-age instruction.

As educators continue to adapt to the rapid technological advancements impacting the educational sphere, the role of D–CoPs as boundary objects becomes increasingly critical. As well as being a conduit for sharing knowledge and experiences, they may also be a catalyst for transforming educational practices to meet 21st-century educational demands. Thus, we argue for D–CoPs’ vital role in both the individual and collective OTPL journey of educators in the digital era.

6 Conclusions and Recommendations

The rapid integration of digital technologies within the educational landscape necessitates a reimagining of OTPL (Stringer et al., 2024) in building confidence and skills to use these dynamic digital tools effectively, ethnically and equitably. As described throughout this paper, the pressing forces of big data and learning analytics, the permeation of AI, and the evolving identities of teachers act as catalysts for unprecedented change. However, the reliance on these technologies brings forth the risk of exacerbating educational inequities, and raises ethical and safety issues (Hidajat et al., 2023; Ouyang & Jiao, 2021). This underscores an urgent need for policies and frameworks, shaped by human authority and decision-making, that not only encourage the adoption of these technologies but also address the accompanying ethical considerations.

Following the information presented in this paper, we suggest some recommendations for policymakers, practitioners, and researchers. Policymakers should integrate boundary objects into teacher education to make digital tools implementation seamless and understandable for diverse educational communities. Regular engagement with field experts, establishing partnerships with industry and researchers, and actively seeking feedback from educators are essential steps. Such collaborations should monitor emerging technologies’ potential impact on teaching and learning. Reforming policies to align with research-based evidence is vital, providing teachers with resources for digital professional growth like the boundary objects championed here.

Practitioners need structured OTPL experiences integrating PR&A and reflective practices from day one of preservice education throughout their careers to ensure equitable and quality education. Balancing technology use in classrooms to avoid extremes of mistrust or blind trust is crucial, prioritizing effective teaching and learning. Including educators in co-designing digital learning environments and datasets use is essential to optimize learning experiences while monitoring equity.

In research, examining these and other technological drivers’ effects on teacher practices related to equity in digital realities is crucial. Studies should further explore teacher identity complexities in diverse contexts to improve teachers' well-being in the profession and the quality of their practices. Further research areas could include investigating the effectiveness of these and more boundary objects in OTPL and the impact of technology on teaching and learning inequities, examining the role of reflective practices and professional growth in curbing them. Research findings should inform policies and practices, addressing equity gaps to ensure all students access quality education.

7 Competing interest

The authors have no relevant financial or non-financial interests to disclose.