Integrating Extended Reality and Artificial Intelligence in Education: Training, Challenges, and Reflections from Pre- and In-Service Teachers

Chapter Integrating Extended Reality and Artificial Intelligence in Education: Training, Challenges, and Reflections from Preand In-Service Teachers Stavros Pitsikalis and Ilona-Elefteryja Lasica Abstract This paper presents the design, implementation, and evaluation of a professional development programme conducted in Greece (Rhodes Island) during 2024–2025, focusing on the integration of extended reality (XR) and artificial intelligence (AI) in education. A total of 101 teachers (53 pre-service, 48 in-service) participated in five training sessions that combined theoretical input, hands-on exploration, and scenario-based instructional design. Grounded in Digital Competence of Educators (DigCompEdu), Technological Pedagogical Content Knowledge (TPACK), and the Cognitive Affective Model of Immersive Learning (CAMIL), the programme positioned teachers as designers of immersive and AI-enhanced learning activities rather than passive tool users. Mixedmethods evaluation, including questionnaires, focus groups, artefact analysis, and observation, revealed strong intentions to integrate XR/AI, heightened self-efficacy, and recognition of motivational gains for students. Key supports identified included access to ready-to-use lesson scenarios, mentoring, device availability, IT assistance, and leadership backing. Barriers centred on time constraints, infrastructure, and curricular rigidity. The findings underscore the importance of extending existing competence frameworks to address immersive pedagogy and ethical AI use, while situating XR/AI integration within the realities of institutional and national contexts. The paper concludes with recommendations for sustainable adoption, emphasising infrastructure investment, leadership engagement, ethical safeguards, and continuous communities of practice. Keywords: adult education, teacher professional development, extended reality, artificial intelligence, virtual reality, augmented reality 1 1. Introduction The educational landscape is changing fast. Technologies like extended reality (XR) – including virtual reality (VR), augmented reality (AR), and mixed reality (MR) – and artificial intelligence (AI) are no longer just experimental; they have become standard classroom tools [1]. XR often stands out for its set of affordances that matter for learning: presence, interactivity, adaptability, and scalability. Together, these elements create learning experiences that go beyond what traditional classrooms can typically offer [2]. Within the broader rise of metaverse-style learning spaces, XR and AI can be combined to create persistent, shared environments where teachers and students work together, practice difficult tasks, and create resources from different locations, all related to curriculum goals. Typical examples include the safe practice of high-risk procedures, virtual field trips, embodied exploration of abstract ideas, and spatial problem-solving with peers who are not in the same room [1–6]. In parallel, AI supports personalised practice, instruction that adapts to the learner, and content generation, which can improve daily teaching if used wisely [7, 8]. In short, XR and AI can be combined to design inquiry-based, hands-on, contextrich activities across different subjects, which can lead to better knowledge, motivation, and performance [9]. In teacher training, XR also provides a safe space to practice managing a classroom or having difficult conversations, while AI can give clear prompts and analyses that encourage reflection [10]. Government policies at the national and European levels include both areas in their plans for digital and media skills; the EU’s Digital Education Action Plan mentions that they are important for both initial and continuing teacher education [11, 12]. In reality, practice lags behind what is possible. Tools arrive before teachers have enough time or structured support to integrate them [13]. Established frameworks, such as Digital Competence of Educators (DigCompEdu) [11], have improved confidence with general digital tools, but they have not been revised to fully include the details of immersive, multi-sensory settings or the practical issues of AI-driven data analysis and customized teaching [4]. Studies also point out a group of needs that arise when schools try to use XR/AI: spatial instructional approaches, immersive assessment, presence management, ethics and privacy concerns in avatar-mediated spaces, and ways to manage a classroom and student well-being [5, 6, 13]. Many teachers feel comfortable with mainstream technology but report limited familiarity when the context shifts to immersive media or AI-supported teaching [10]. This is more visible where device access, support from school leaders, and clear policy guidance vary across regions. Given this situation, this chapter discusses a systematic evaluation of a teacher professional development (TPD) programme delivered in 2024–2025. The programme ran across five sessions with 101 participants (53 pre-service; 48 inservice) and focused on activities using XR and AI. Instead of focusing on basic technical skills, the program emphasised scenario-based, practice-centred design, drawing on DigCompEdu [11], the Technological Pedagogical Content Knowledge (TPACK) model [14], and the Cognitive Affective Model of Immersive Learning (CAMIL) [15]. Hands-on exploration, reflection, and collaborative design were key components, intended to reveal both the benefits and limitations as perceived by different groups of teachers. Metaverse, MetaIntelligence and Infinite Worlds with AI 2 This chapter has two main contributions. First, it gives a roadmap for using XR/ AI in teacher education in a way that is sustainable and inclusive, based on data. It shows how a structured XR/AI TPD affects intentions and confidence and identifies the teacher skills that are most affected: professional engagement, immersive scenario design, assessment, and learner empowerment. Second, it identifies the pedagogical, technical, and institutional supports that teachers say they need to keep using XR and AI regularly in the Greek context, translating those insights into practical plans for teacher preparation, institutional policy, and technology design. The chapter uses a mixed-methods design – questionnaires, focus groups, participants’ artefacts, and reflective observation – to address two research questions: (RQ1) How does taking part in a structured XR/AI professional development program affect teachers’ intentions and confidence in using these technologies in their teaching? (RQ2) What pedagogical, technical, and institutional supports do teachers identify as necessary for implementing XR and AI in a sustained way? 2. Theoretical framework This section reviews how XR and AI are used across school, vocational, and higher education, and what tends to help or hinder adoption. It describes key ideas, typical applications in teacher education, and TPD models that move beyond ‘tools training’ to effective instruction. It then talks about core competence frameworks – DigCompEdu, TPACK, and CAMIL – and notes how these are being extended for immersive contexts. It ends with a framework that guided the training design and later analysis. 2.1 XR and AI in education and TPD: Current state and potential XR is now used across all educational levels. Three mechanisms appear repeatedly in evaluations [1, 3]. First, presence and agency: learners act within a coherent 3D scene rather than watching from the outside. This can support attention and motivation when cognitive load is managed. Second, embodiment and spatiality: abstract structures (like anatomical, architectural, and geographic) become manipulable in space, helping learners connect multiple representations. Third, safe rehearsal: procedures and teaching methods can be practised without real-world risk and then reviewed in debrief [15, 16]. Reviews and project syntheses report steady gains in involvement and conceptual understanding when experiences are purposefully designed and paired with activities before and after the experience [16–19]. In teacher education, common uses include classroom-management simulations, 360° video cases for practicing sensitive conversations, role-playing parent–teacher meetings, micro-teaching and observation in simulated classrooms, practicing accommodations in special education, and socio-emotional awareness scenarios. Each of these is usually paired with a structured introduction and guided review so that ideas can be transferred back to classroom practice [13, 16–18]. AI is used with XR and in standalone ways. In immersive activities, AI can offer prompts that adapt to the learner, perform data analysis, convert speech to text, and provide translation to increase access, as well as enable easy content creation [8, 10]. Separately, AI supports lesson and assessment design (drafting objectives, rubrics, Integrating Extended Reality and Artificial Intelligence in Education… DOI: http://dx.doi.org/10.5772/intechopen.1012606 3 and question banks), conversational tutoring, automated feedback on writing or code, and analytics integrated with learning platforms [7, 8]. Most guidance recommends using AI where it supports activities and feedback while keeping pedagogy and curriculum in mind [8]. At the policy level, EU and national initiatives increasingly recognise XR and AI in teacher‑development agendas and are taking practical steps to integrate them into training [16]. The Council of the EU has urged member countries to embed ‘digital pedagogy’ – including immersive XR and AI – within initial teacher education and to support in‑service upskilling [20]. European strategy documents similarly note that AI, VR/AR, and related technologies are reshaping educators’ roles and expect teachers to co‑design effective learning experiences [21]. Projects across Europe reflect this. In Greece, the Erasmus + PAX project proposed a pedagogical model for XR in teacher education with modules on literacies, immersive scenario design, and assessment [18, 22]. MetaCivicEdu implemented immersive civic education scenarios supported by AI‑driven design tools such as CADMOS and ChatGPT, offering blended training for future instructional designers [9, 10, 23]. The shared aim is to align initial and continuing preparation with technological innovation so teachers can adopt XR/ AI with confidence and purpose. TPD programmes for XR and AI tend to have more impact when they frame educators as designers of learning, not only as tool users [10, 13]. Research on teachers as designers shows that professional growth increases when educators iteratively plan, enact, and reflect on technology-enhanced activities with authentic curriculum goals in mind, supported by design representations that make pedagogical intent visible [24–26]. Short conceptual inputs combined with hands-on design and experimentation – e.g., micro-teaching in simulations or rapid prototyping of immersive scenarios – help build confidence and practical judgment [27]. These programs commonly have the same structure: a short introduction to explain goals and roles, a guided experience with XR/AI (often in pairs or small groups, rotating through teacher, learner, and observer roles), and a debrief using artefacts (screenshots, short clips, notes) to link experience to assessment, accessibility, and classroom management [9, 10, 13, 18]. This structure enables participants to connect interaction patterns – like taking turns in spaces with many users or giving prompts in an AI tutor – to inclusion and curriculum goals, while developing a common language for feedback. Working together between developers and educators is also important. A recent European project [17] uses a stepwise Capture → Plan → Realise → Apply process to move curriculum needs to implementations that are ready for the classroom. Capture explains the learning problem and context; Plan specifies didactic flow, roles, assessment evidence, and accessibility checkpoints; Realise selects or builds the XR/ AI experience; and Apply focuses on classroom trials and changes. Following this has led developer teams to add features teachers ask for (pause/resume, timers within the experience, data analysis that can be exported), which improves usability and integration [19]. Instructional design guidance specific to immersive media – such as task size, navigation hints, and review examples – supports creating designs that are scalable and useful for teaching [2, 28]. Within TPD, AI is increasingly applied beyond its role in classroom scenarios. Teacher-design teams mainly use AI to draft learning objectives, create rubrics, generate question banks and scenario descriptions, or synthesise peer feedback into Metaverse, MetaIntelligence and Infinite Worlds with AI 4 concrete next steps [7, 8]. In our view, this accelerates the process without displacing pedagogical judgment: teachers still set intent, verify accuracy, and adapt outputs to learner profiles and equity aims [29]. For collaborative XR tasks, emerging theory suggests that clear role scripts, shared task views, and reflection prompts make a difference – elements that can be rehearsed in TPD and later reused in classroom practice [15]. Finally, practical support tools help enactment. Scenario cards mapped to DigCompEdu domains, setup and safety checklists for different devices, and readyto-use reflection protocols for both teachers and students create continuity across groups and contexts [9, 18]. New XR-specific competence descriptions give quick prompts – what to explain, observe, and document for assessment – that are useful to novices and experienced educators alike [2, 4]. 2.2 Digital competence frameworks The European Framework for the DigCompEdu sets out 22 educator competences across six areas: professional engagement, digital resources, teaching and learning, assessment, empowering learners, and facilitating learners’ digital competence. It also describes progression across six proficiency levels (A1–C2). In practice, it helps with goal-setting, self-assessment, and programme planning and evaluation [11]. Immersive, multi-user environments add requirements beyond the original descriptors. Teacher groups working with XR report common needs, such as managing synchronous 3D activities (clear roles, turn-taking routines, and virtual safety zones), managing presence and agency while pacing cognitive load, gathering evidence from interaction data (e.g., logs, screenshots) for assessment, and planning review examples that support reflection. Accessibility, physical safety, and data protection also need explicit attention in shared virtual spaces [16–18]. In response, recent initiatives suggest XR-oriented extensions to the DigCompEdu – often in the form of card sets and planning aids that align each domain with real classroom practices and prompts for briefing, enactment, and debrief – so teachers can work within a familiar framework while addressing XR-specific decisions [2, 4, 16–18]. TPACK adds to DigCompEdu by asking how content, pedagogy, and technology come together in a particular lesson design, rather than treating these domains separately. In XR/AI contexts, this is often enriched with constructs from immersive-learning research, especially the CAMIL, which explains how presence, agency, and cognitive/affective load shape learning and offers practical guidance on giving signals, managing speed, and reviewing [14, 15]. A growing stream of work translates this into ’enriched TPACK’ ideas for immersive lessons: align XR tasks with explicit outcomes and qualification frameworks, specify navigational hints and interaction granularity, and plan for assessment examples that can be captured during or after the experience [2, 28]. Where AI is also part of the design process, complementary guidance helps teams decide where AI supports planning (e.g., drafting objectives, creating rubrics), feedback, and analytics while maintaining educators’ autonomy [29]. Used together, DigCompEdu (and its XR-focused extensions), TPACK, CAMIL, and related design guidance provide a solid basis for listing competence goals, making principled design choices, and preparing assessment and reflection processes that fit XR/AI-enhanced teaching [2, 4, 11, 14–16, 18, 28, 29]. Integrating Extended Reality and Artificial Intelligence in Education… DOI: http://dx.doi.org/10.5772/intechopen.1012606 5 2.3 Challenges and considerations Findings from reviews and fieldwork tend to agree on a small set of conditions that shape adoption and continued use. Figure 1 summarises these conditions. Practically, programmes pay attention to device availability and sharing, classroom space and safety (clear play areas, cable management, supervision), network reliability, and routines for setup and maintenance (checklists, charging, updates). Institutions that specify who prepares the equipment, how long the setup takes, and where the devices are stored tend to move from pilots to regular use more smoothly [5, 16–19]. Pedagogically, effective implementations start from clear learning outcomes and bring XR/AI into a lesson only where it adds value compared to existing methods. Design work specifies brief– experience–debrief sequences, scaffolds, and evidence of learning to be captured during or after the activity. Provision for accessible alternatives is made – desktop views, captions, narration, or alternative inputs – so all learners can participate [2, 6, 24–26]. At an organisational level, there is a need for clarity around permission, privacy, and data flows in multi-user platforms, timetabling that fits device logistics, and Figure 1. Conditions that shape XR/AI integration in teaching (partially created with AI tools ChatGPT/DALL-E). Metaverse, MetaIntelligence and Infinite Worlds with AI 6 local support pathways (who to contact when something fails). These concerns align with European guidance on digital education and the integration of emerging technologies in teacher preparation and continuing professional development [12, 19–21]. Paying attention to the learner experience is also important. Short onboarding sequences help first-time users; sessions are paced with brief breaks to reduce motion sickness; captions and narration support understanding; and alternative inputs are available where needed. These measures are associated with stronger engagement and more equitable participation in evaluations of immersive activities [2, 5, 6, 16]. Finally, several enablers recur: leadership support (time allocations, device pools, micro-credentials), communities of practice (peer mentoring, co-teaching, scenario sharing), and switching between developers and educators to improve features based on classroom feedback. Ready-to-use reflection tools – for instance, structured focus-group prompts – offer reliable ways to review experience, learning, and transfer to everyday teaching [16–19, 24–26, 30]. Context still matters – policy, school culture, group size, programme aims. Approaches that adapt to local conditions while keeping a consistent pedagogical structure (brief–experience–debrief) tend to be more sustainable [16–18]. 3. Methodology This study examined the outcomes of a structured TPD programme using a mixed-methods design. The approach integrated a post-training questionnaire, researchers’ observation notes, examples of participants’ artefacts, and short semi-structured focus group discussions. The goal was to understand not only whether the programme changed self-reported competence and intentions, but also how teachers understood the pedagogical, technical, and institutional conditions for sustained use. Quantitative items provided breadth; qualitative materials added depth. A total of 101 teachers, both pre-service and in-service, took part across five independent sessions (October 2024–April 2025). The design was intended to create evidence about individual skill/confidence and about the contextual factors that help or hinder the integration of immersive and AI-enhanced pedagogies. 3.1 Research design In this research, we adopted a convergent parallel mixed-methods design [31, 32]. Quantitative and qualitative data were collected in parallel, analysed separately, and then brought together for interpretation. The focus was on three outcomes: ●self‑assessed competences, ●intentions to integrate XR/AI, and ●perceived enablers and barriers across pedagogical, technical, and institutional dimensions. Integrating Extended Reality and Artificial Intelligence in Education… DOI: http://dx.doi.org/10.5772/intechopen.1012606 7 The main data source was a post-session questionnaire. This was complemented by (a) structured observation notes recorded by the researchers (who also served as trainers), (b) thematic analysis of indicative participant artefacts (lesson plans, immersive scenarios, AI-generated resources), and (c) short semi-structured focus groups at the end of selected sessions. The combination allowed triangulation and aligns with recommendations for evaluating TPD enhanced with technology [4, 11, 14–16, 31, 32]. While the mixed-methods approach provided a detailed and multi-layered account of participants’ experiences, the study is constrained by several factors. Data were limited to immediate post-training measures and reflections, which are informative but time-bound; they do not capture longer-term transfer into classroom practice. The sample was geographically concentrated (Rhodes) and composed of a specific mix of in-service and pre-service teachers with strong academic backgrounds. The findings should be read as showing trends within this context rather than being broadly representative. 3.2 Participants In total, 101 teachers (74 female and 27 male) participated across five training sessions held between October 2024 and April 2025 on the island of Rhodes, Greece. Ages ranged from 20 to 54 years; pre-service teachers were mainly between 20 and 24, while in-service teachers covered a wider range. Recruitment utilised institutional mailing lists, partner networks, and invitations during dissemination events. Participation was voluntary and not compensated. The in-service teachers (n = 48) came from different fields – mathematics, science, IT, social studies, languages, religious education, and history. All held at least a Master’s degree, while some were current students or graduates of the MSc in New Forms of Education and Learning (NFEL) at the University of the Aegean. Their professional experience ranged from early-career (<5 years) to highly experienced teachers (>20 years). The pre-service teachers (n = 53) were bachelor-level students enrolled in the Department of Preschool Education Sciences & Educational Design (TEPAES) at the University of the Aegean. Most were in their final or second-to-last year of study. They knew the basics of educational technology but had limited hands-on experience with XR/AI in teaching. Table 1 presents the distribution of participants by session and demographic composition. Training session Total participants In-service teachers Pre-service teachers Female Male Average age 1 10 10 0 7 3 42,3 2 9 9 0 6 3 41,7 3 34 16 18 25 9 33,6 4 23 13 10 16 7 31,7 5 25 0 25 20 5 21,2 Total 101 48 53 Table 1. Participants’ distribution by training session. Metaverse, MetaIntelligence and Infinite Worlds with AI 8 The study population was geographically similar (Rhodes) but different in career stages, subject backgrounds, and qualifications – which was helpful for comparing perspectives between in-service and pre-service teachers. 3.3 Professional development programme structure The XR/AI TPD programme was delivered as five independent sessions, each lasting about three hours, structured around a brief–experience–debrief cycle common in immersive learning contexts [15, 18, 27]. Short inputs were blended with hands-on exploration, collaborative design, and guided reflection. Core thematic areas included: (a) basics of XR/AI in education (benefits, limits, ethics), (b) immersive scenario design through rapid prototyping, (c) AI-supported planning and assessment (objectives, rubrics, reflective prompts), (d) managing a classroom in XR environments with many users, and (e) assessment methods appropriate for immersive learning. The curriculum was based on DigCompEdu [11], TPACK [14], and the CAMIL [15]. Table 2 maps frameworks to XR-specific competences and example activities. Teachers were positioned not simply as technology users but as designers of learning experiences [24–26]. DigCompEdu informed the competence targets (e.g., digital content creation, pedagogical use, learner empowerment). Following the XR-specific extensions proposed by PAX [4], we Framework/ Dimension XR-enriched competences Example training activities DigCompEdu – digital content creation Design immersive resources with spatial presence and embodied interaction aligned to objectives. Group design of 360° science field trips; AI-assisted 3D objects for history. DigCompEdu – pedagogical use Adapt immersive tools to curricular goals; manage XR for many users; add accessibility. Accessible XR language modules with translation and adjustable interaction modes. DigCompEdu – learner empowerment Foster agency using roles, exploratory tasks, and adaptive feedback in XR. Peer-led immersive debates with AIgenerated reflection prompts. TPACK – technological knowledge Select/use XR/AI tools with attention to specific benefits. Headset setup; XR app choice; AI resource generation. TPACK – pedagogical knowledge Blend active learning strategies with XR/AI benefits. Project-based XR tasks in math and social studies. TPACK – content knowledge Ensure subject accuracy and connection within XR-enhanced content. XR history tours with accurate timelines, AI narration, and contextual artefacts. CAMIL – cognitive processing Manage cognitive load with short, scaffolded tasks, and debrief. Short VR lab simulations; guided debrief on results. CAMIL – emotional engagement Build emotional connection through story and perspective. VR role-plays on cultural empathy and inclusion. CAMIL – motivational alignment Balance challenge and skill; connect to inner goals. Gamified XR challenges connected to the curriculum with personalised feedback. Table 2. Mapping frameworks to XR-specific competences and example activities. Integrating Extended Reality and Artificial Intelligence in Education… DOI: http://dx.doi.org/10.5772/intechopen.1012606 9 immersive, AI-driven pedagogies and protect students’ well-being (e.g., managing cognitive load and privacy considerations). Comparing our findings with the broader literature reveals several similar trends. Consistent with prior studies [15–18], we found that teachers recognize the potential of XR/AI for enriching instruction but remain cautious in adoption. Many teachers in our program saw the benefit of these technologies to engage students, which aligns with evidence that XR can boost motivation and participation [25, 26]. However, some participants remain hesitant to use XR/AI without substantial support [15, 17]. Key barriers mentioned – such as a lack of confidence in using new tools, limited time for lesson redesign, and lack of readymade digital content – have been widely observed in the literature [15–17]. A further pattern, also seen in prior work [15], was the gap between expectations and reality: enthusiasm sometimes turned to frustration when devices did not work or sessions were disrupted. Such comparative insights highlight that the diffusion of XR in education remains gradual [24, 25] and highly dependent on teacher readiness and context. At the same time, our results add to the growing evidence of positive outcomes when integration does occur. Teachers reported that students were more curious and involved during XR lessons, reflecting the motivational benefits found in recent studies [25, 26]. Thus, our study both supports and extends existing knowledge. It points out known facilitators and obstacles but also shows how these dynamics play out in a specific national system. This context is particularly crucial in the case of Greece, where systemic and ethical considerations strongly affect the space for innovation. The Greek education system features a centralized curriculum and strict regulations that shape how and whether XR/AI tools can be used in schools. Our teachers noted that inflexible syllabi and high-stakes exams leave little room for trying new things, a challenge in line with prior studies of educational innovation [13, 16]. Moreover, infrastructural constraints in Greek schools emerged as a significant theme. Many schools (especially public ones) lack reliable broadband or Wi–Fi, and policies often restrict mobile devices in class (unless special permission is given), creating a paradox where teachers trained in XR have limited means to implement it [13]. This highlights how disparities in access and restrictive regulations can limit the impact of even welldesigned TPD initiatives – a challenge also noted in other research on digital innovation in schools [13, 24]. Ethical and equity implications also come up. Teachers focused on the importance of equal opportunities: if only some schools with support can deploy XR/AI, it could widen disparities. They also expressed concern about ensuring student safety and privacy when using AI-driven educational software, reflecting the broader ethical debates around AI in education (e.g., avoiding bias in algorithms and protecting student data). In Greece, as in other EU contexts, any adoption of AI in classrooms must follow emerging ethical guidelines and GDPR regulations, adding another layer of consideration for TPD programs. Our discussion highlights that successful integration of XR/AI is not just a technical or pedagogical issue, but a contextual one. It needs to follow local policy, infrastructural support, and robust ethical rules. Finally, it is important to acknowledge the limitations of this study. The study was relatively small in both scale and duration, which limits the generalisability of the findings [24]. Similar pilot studies integrating AR/VR in teacher training have cautioned that results may not easily transfer to all settings or scales without further Metaverse, MetaIntelligence and Infinite Worlds with AI 16 validation [24, 25]. The group of participating teachers was self-selected and motivated, which may have introduced a positive bias, also observed in similar TPD studies [15, 18]. Additionally, the evaluation of impact was short-term, focusing on immediate changes in teacher practice and student engagement following the TPD. Our results provide an initial snapshot – for example, teachers did implement XR/AI activities and observed enthusiastic student responses – but we cannot confirm if these changes last over time. Prior research has emphasized the need for longitudinal studies to determine whether initial gains are sustained once the novelty of technology fades [13, 25]. Technical and institutional constraints also shaped the implementation: device shortages, software issues, and school policies limiting mobile AR all affected the work, as seen in other resource-constrained contexts [26]. Recognising these limitations positions our study within the wider landscape of exploratory research. Future work should therefore extend the approach to larger groups, over longer periods, and across more varied school settings to assess transferability [24–26]. Despite the limitations, the findings provide practical insight into what is needed – from policy support to technical infrastructure – to effectively bring XR and AI into the TPD. 6. Conclusion This research began to explore two key questions regarding the integration of XR and AI in TPD. Addressing RQ1, the results show several factors affecting teachers’ willingness to adopt XR/AI. The most significant were external supports and internal readiness; adoption was more likely when technologies aligned with curricular aims, leadership-offered support, and infrastructure, such as devices and connectivity, was reliable. Equally important were personal factors such as the teachers’ own digital confidence and motivation to innovate. The study points out that XR/AI integration is facilitated by a supportive environment and by training that builds teachers’ self-efficacy in using these tools. In response to RQ2, the study provides concrete insights and strategies. We noticed that successful integration goes beyond introducing new tools; it involves merging XR/AI into pedagogically meaningful activities. Teachers in our program learned to design immersive lesson plans that were tightly linked to their curriculum goals. They reported that these approaches not only engaged students through interactivity and rich visualization but also encouraged student-centered learning, as shown by increased student curiosity and participation during the XR/AI-supported lessons. However, the integration worked best when teachers adapted the technology to their context: starting with small, manageable implementations (such as a single AR-enhanced project), aligning them with existing lesson objectives, and gradually scaling up as confidence grew. This points to XR/AI adoption as an iterative process: try, reflect, and refine. Moreover, our results emphasize that TPD programs should model these strategies, providing teachers with hands-on experience in XR/AI, opportunities to collaborate and share experiences, and guidance on troubleshooting technical or classroom management challenges. By doing so, TPD can translate the promise of XR/AI into actual classroom practice. Overall, the study demonstrates that, with the right support and preparation, teachers can integrate XR and AI in ways that enrich learning experiences, addressing both research questions. The challenge and the opportunity lie in building Integrating Extended Reality and Artificial Intelligence in Education… DOI: http://dx.doi.org/10.5772/intechopen.1012606 17 the ecosystems – pedagogical, technical, and institutional – that enable that integration to take root and grow. Below, we translate these insights into actionable recommendations for stakeholders looking to build on this work and advance XR/AI integration in education: ●Align teacher training with digital competence frameworks: Design and implement TPD programs that map onto known frameworks like DigCompEdu, extending them to include XR/AI-specific skills. This alignment will ensure that professional development not only builds practical classroom techniques but also meets broader educational standards for teachers' digital competencies. ●Invest in infrastructure and technical support: Prioritise funding and resources to equip schools with the necessary technology (high-speed internet, AR/VR devices, and updated software) and on-site technical support. Reliable infrastructure is foundational for teachers to confidently integrate XR/AI tools, and such investment reflects a broader educational commitment to digital innovation and equity. ●Foster a supportive school culture and leadership engagement: Encourage school leaders to actively support experimentation with XR and AI in teaching. This can include providing teachers with dedicated time for collaborative planning, reducing the fear of failure by celebrating innovative attempts, and integrating XR/AI goals into the school’s development plans. Strong administrative backing creates an environment where teachers feel safe and valued when adopting new methodologies, in line with wider calls for transformative school leadership in digital education. ●Integrate ethics and data privacy into XR/AI initiatives: Ensure that any introduction of AI-driven tools or data-intensive XR applications in education comes with clear guidelines and training on ethical use. Teachers should be prepared to address issues such as student data privacy, informed consent for using AI recommendations, and recognizing potential biases in AI content. By embedding these discussions in TPD, educational authorities can align the micro-level implementation with macro-level priorities, like the EU’s ethical guidelines for AI in education, thus promoting responsible innovation. ●Provide continuous mentorship and communities of practice: Move beyond one-off workshops by establishing ongoing support mechanisms – such as mentoring by experienced XR/AI educators or professional learning communities (PLCs) – where teachers can share experiences, troubleshoot challenges, and collectively develop best practices. This sustained support mirrors effective PD models in other domains and helps maintain momentum, ensuring that initial gains in teacher capacity lead to long-term changes in practice. ●Plan for long-term and scalable evaluation: Incorporate evaluation plans that monitor the impact of XR/AI integration over extended periods and across multiple schools. By collecting data on both short-term successes and longerterm outcomes (e.g., how teaching strategies and student performance evolve after one year), stakeholders can make informed decisions about scaling up the Metaverse, MetaIntelligence and Infinite Worlds with AI 18 initiatives. Such a commitment to evidence-based scaling resonates with broader educational priorities of accountability and continuous improvement in innovation adoption. Acknowledgements This work was partially supported by the European Commission under the project PAX—Pedagogical Alliance for XR-Technology in (Teacher) Education— ERASMUS-EDU-2023-PI-ALL-INNO, Project No. 101139827. We also extend our thanks to all collaborators and partners involved. Author details Stavros Pitsikalis 1 and Ilona-Elefteryja Lasica 2 * 1 University of the Aegean, Department of Preschool Education Sciences & Educational Design, Rhodes, Greece 2 University of the Aegean, Rhodes, Greece *Address all correspondence to: [email protected] © 2025 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/ licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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