1e. Education and Training

Scope and Introduction

Virtual Worlds (VW) possess the potential to revolutionise learning and teaching by offering immersive and interactive environments that are valuable across multiple levels of education and training. At the institutional level, virtual classrooms and campuses can simulate complex, real-world scenarios, provide hands-on learning experiences, and facilitate collaboration across geographical boundaries. At the teacher/trainer level, VW enable personalised learning paths, immediate support, real-time adaptive assessments, and innovative methods tailored to individual needs. At the student/trainee level, VW can enhance engagement, improve learning and training outcomes, and better prepare future generations for a digital world.

VW offer significant value for both educational and training purposes, each with distinct yet complementary goals. While education is primarily concerned with the acquisition of knowledge—concepts, theories, and critical thinking—training focuses on the development of practical skills and competencies necessary for performing specific tasks. VW are uniquely suited to support both: they can host rich, contextualised content for cognitive learning as well as realistic, interactive simulations for skill acquisition.

For education, VW offer opportunities to explore abstract concepts through visualisation, engage in collaborative problem-solving, and immerse learners in historical, scientific, or mathematical contexts that foster deeper understanding. They also provide dynamic and authentic settings for language learning and intercultural exchange, enabling students to practise in real-time, interact with virtual agents or peers, and receive immediate feedback, including AI-assisted language analysis.

For training, they provide safe, controlled, and repeatable environments in which learners can practice procedures, decision-making, and situational awareness—especially valuable in high-stakes and technically complex domains such as healthcare, engineering, emergency response, or technical maintenance. By simulating realistic scenarios, VW enable trainees to learn from mistakes without real-world consequences, build competence through repetition, and gradually transfer their skills to authentic professional settings.

By combining education and training in shared virtual environments, institutions and organisations can create holistic learning ecosystems that address both knowledge and skill development. This integration not only supports lifelong learning, teaching, and training but also ensures that learners and trainees are both informed and capable—ready to meet the demands of an increasingly complex, technology-driven world.

Use Cases

This section outlines key research areas within the education and training domain, detailing their significance, current challenges, and proposed objectives for future innovation.

School Education and Academics

1e.1 Primary and secondary education

VW transform how students learn by providing immersive, interactive, and dynamic environments that support both conceptual understanding and hands-on learning across a wide range of subjects. They facilitate the support of individual learners' progress and needs, thereby enhancing learning efficiency within an integrated blended learning approach. Simultaneously, these environments inclusively prepare all learners for a digitised society where physical and digital experiences seamlessly converge.

Challenges and opportunities: Current primary and secondary education primarily relies on classroom instruction, textbooks, and limited access to labs or field trips, making it difficult to provide personalised and deep learning experiences, especially for abstract or dynamic concepts. Widespread integration of VW remains low due to high costs, limited high-quality software and hardware, insufficient teacher training, and challenges with curriculum integration. Pedagogically, many VW lack strong instructional design, underrepresent 21st-century skills, and underutilize AI and machine learning for content creation or dialogue-based learning.

Research and Innovation Objectives: Develop pedagogically grounded content designed with strong instructional principles that align with effective teaching practices (2c). Create safe, child-friendly AR/VR/XR hardware and subject-specific interactive modules aligned with national curricula (2a). Conduct research into developmental effectiveness, cognitive load, and equity of access in virtual learning environments (2f, 3a).

1e.2 Higher education and research

VW transform how students and university staff engage with complex learning topics by providing immersive, interactive environments that support theoretical understanding and applied learning. They create new opportunities for advanced teaching and collaborative research by offering immersive access to complex systems, simulations, and real-world data. In higher education, these environments serve as interfaces to live databases, computational models, and scientific infrastructure.

Challenges and opportunities: Higher education typically relies on lectures, textbooks, and static digital resources, with limited access to hands-on labs or real-world applications. Instructors face constraints such as large class sizes and limited tools for personalised support. Advanced research often requires costly equipment, limited lab access, and fragmented collaboration tools, with large datasets and complex models often visualised in 2D. Adoption of VW solutions remains low to medium, primarily experimental, with limited integration into institutional systems and research data pipelines.

Research and Innovation Objectives: Develop pedagogically sound content with advanced instructional design principles aligned to higher education teaching and learning strategies (2c). Create secure, user-friendly AR/VR/XR hardware suitable for adult learners and complex academic use, with robust instructor dashboards for managing virtual classrooms and assessments (2a). Research usability, learning efficacy, cognitive load management, impact on scientific productivity, and ensuring equitable access for all students (2f, 3a).

1e.3 Supporting intercultural learning and integration

VW support foreign student integration in host institutions by facilitating language and culture learning and providing access to cultural experiences adapted to different language levels. Students can access immersive environments that reproduce cultural experiences, catering to the rich diversity of European cultures, including minority ones, and facilitating engagement and language learning in meaningful contexts.

Challenges and opportunities: There is a need for less costly equipment and easy-to-use authoring tools that allow for the integration of customised assets into truly interactive experiences. Professional upskilling of existing educators and integration into teacher education curricula are also required. A universal design approach, including accessibility services for diverse users, and increased awareness of the VR potential are crucial. Adoption remains low, particularly in language and culture learning within higher education.

Research and Innovation Objectives: Develop intuitive user interface design for XR environments (2b). Create content templates and AI-based tools for easy content creation (2c, 2f). Conduct research into usability and impact on learning and integrate accessibility services for a universal design approach (3a).

Lifelong and Professional Learning

1e.4 Lifelong, vocational, and corporate learning

VW offer powerful opportunities for adult learning across lifelong education, vocational training, and corporate development by enabling immersive, interactive, and practice-oriented experiences. These environments transform how adult learners engage with knowledge—whether they are acquiring foundational skills, preparing for technical roles, or advancing professional expertise—by simulating complex real-life scenarios. For companies, they enable experiential, task-specific, and collaborative learning that supports onboarding, leadership development, operational excellence, and cross-functional communication.

Challenges and opportunities: Adult education—across lifelong learning, vocational education, and corporate training—continues to rely heavily on static resources (e.g., lectures, PDFs, or slide-based e-learning), with limited opportunities for experiential learning or hands-on application. Vocational learners face restricted access to specialised equipment, while corporate learners often lack time, support, or immersive tools that reflect complex workplace realities. Challenges include high costs, fragmented access to training infrastructure, limited personalisation, and the absence of scalable systems for peer learning and situated expertise transfer.

Research and Innovation Objectives: Develop content grounded in adult learning principles, aligned with real-world tasks, occupational standards, and collaborative work practices (2c). Create user-friendly and ergonomic AR/VR devices tailored for extended use in diverse settings, including workshops, offices, and homes (2a). Implement adaptive learning pathways, scenario editors with conditional logic, and analytics for individual and team performance tracking (2d, 2f, 3a). Develop high-fidelity 3D models of equipment and environments to support hands-on training and safety-critical rehearsal (2e). Conduct research on the effectiveness, inclusion, engagement, tacit knowledge transfer, and return on investment of immersive training approaches (3a, 3b).

1e.5 Immersive and adaptive training for high-risk technical professions

Immersive and adaptive VW support training in high-risk technical professions by combining realistic simulations with AI-driven personalisation. These environments replicate complex workspaces and procedures while responding dynamically to individual learners' performance, enabling scalable, safe, and effective skill development. This accelerates skill acquisition, increases engagement, and allows high-fidelity practice of emergency or failure conditions impractical in the real world.

Challenges and opportunities: Traditional training relies on physical mock-ups, classroom theory, and supervised hands-on practice, which are costly, limited in scope, and hard to scale, especially for dangerous scenarios. Trainers struggle to personalise support, and learners may not receive timely or objective feedback. Equipment access, instructor availability, and learner safety remain key barriers. Fully adaptive, AI-enhanced systems are still in early stages or pilot use, with hardware cost, integration complexity, and content obsolescence hindering scaling.

Research and Innovation Objectives: Develop high-fidelity 3D models of industrial equipment and facilities (DTs) and real-time behaviour tracking (e.g., gaze, hesitation, error type) (2e). Create AI agents for adaptive tutoring and procedural guidance, along with scenario editors featuring conditional logic and semantic workflows (2f, 2c). Conduct research into long-term retention, trust in AI tutors, and transfer of training from virtual to real-world contexts (3a, 3e).

1e.6 Teacher training and professional development

Training teachers using VW that simulate real classroom settings enables them to develop teaching skills, classroom management strategies, and student engagement techniques through active, experiential learning. These environments support personalised growth by monitoring individual progress and tailoring experiences accordingly, preparing educators for modern classrooms. They allow teachers to interact with student avatars in realistic simulations, practice techniques, and receive immediate feedback.

Challenges and opportunities: Teacher training typically relies on lectures, limited classroom observation, and short practicum periods, leading to restricted access to diverse teaching situations, limited feedback opportunities, and high variability in training quality. Complex classroom dynamics and uncommon situations are difficult to simulate. Adoption of VW solutions remains very low, primarily due to cost, lack of awareness, and limited integration into teacher education curricula.

Research and Innovation Objectives: Develop immersive classroom simulation platforms with behavioural AI for dynamic student interaction (2b, 2f). Create scenario creation and customisation tools, along with learning analytics and feedback systems (2c). Conduct research into skill transfer, pedagogical impact, and user experience in virtual teacher training environments (2f, 3a).

Special Education

1e.7 Inclusive and emotionally adaptive learning for special education

VW support inclusive, adaptive learning for students requiring special education, such as those with cognitive, sensory, emotional, or physical challenges. These environments offer personalised instruction, adjustable pacing, and multimodal content tailored to diverse learning needs. With real-time biometric feedback and AI adaptation, they respond to stress or disengagement, helping learners stay motivated and confident.

Challenges and opportunities: Special education often relies on observation alone to assess student stress and engagement, with limited support for real-time adaptation. Many schools lack the tools, training, or funding to offer consistent, inclusive experiences for complex needs like emotional regulation or executive functioning. Feedback is often delayed, subjective, and difficult to personalise at scale. Adoption of VW solutions remains low to very low, limited by hardware cost, accessibility limitations, and lack of integration with Individualised Education Programmes.

Research and Innovation Objectives: Develop AI-powered content personalisation engines and wearable biosensors for emotional state monitoring (2f, 2a). Create real-time affective computing and multimodal input analysis, along with universal design features such as text-to-speech, speech-to-text, visual simplification, and alternative inputs (2b, 3a). Conduct research into adaptive learning for neurodiverse users, cognitive load, and long-term impact of VW interventions (2f, 3a).

Remote and Distance Education

1e.8 Remote and distance education

In remote and distance learning, VW transform how learners engage with content by providing immersive and interactive settings that bring lessons to life beyond traditional screens. These environments encourage active participation and collaboration despite physical separation, offering hands-on experiences and real-time feedback. VW also support personalised learning paths and continuous progress tracking, making distance education more flexible and effective.

Challenges and opportunities: Remote education heavily relies on digital platforms and self-directed learning, often with limited real-time interaction between instructors and learners. Teachers face challenges in maintaining learner engagement and ensuring equitable access to technology. Learners encounter distractions and varying degrees of support at home, hindering motivation and progress. The absence of hands-on experiences and immediate feedback makes it harder to grasp complex ideas or practical skills.

Research and Innovation Objectives: Develop instructional content with proven pedagogical methods suitable for virtual, self-directed learning (2c). Create reliable, user-friendly AR/VR hardware optimized for home or remote settings, ensuring accessibility and comfort (2a). Conduct research into remote learning effectiveness, learner motivation, technology accessibility, and minimising cognitive overload in virtual environments (2f, 3a).

AI-Enhanced Learning

1e.9 Trainee-controlled adaptive support via embodied AI

VW empower trainees in technical and vocational training programmes to control their learning experience by selecting their desired level of AI support. An embodied AI agent, a virtual tutor powered by a Large Language Model, interacts with learners in natural language, allowing them to negotiate the degree of guidance, hints, and feedback they receive. This human-AI dialogue fosters autonomy, builds metacognitive awareness, and adapts to individual learning preferences and confidence levels.

Challenges and opportunities: Traditional training often follows a "one-size-fits-all" approach, with predefined levels of guidance that may not suit all learners, potentially limiting critical thinking or leaving some without enough support. Human instructors may lack the bandwidth to individualise support in real-time, and trainees have limited agency in directing their own learning. While intelligent tutoring systems exist in VR training, they rarely allow dynamic adjustment of support through natural conversation, and large language model-powered avatars are largely unexplored at scale in technical training.

Research and Innovation Objectives: Develop large language models integrated with lifelike, emotionally responsive avatars capable of dialogue management to interpret requests for varying support levels (2f, 2b). Create training task modularity for adjustable guidance levels and implement learning analytics to track interaction patterns and performance impact (2c). Conduct research into learner agency, metacognition, and trust in AI tutors, alongside usability studies on dialogue clarity, frustration reduction, and effectiveness (3a, 3e).

Recommendations

To fully leverage the transformative potential of VW in education and training, a strategic approach encompassing technological development, pedagogical innovation, and inclusive design is essential.

Pedagogical Content: A concerted effort is needed to develop pedagogically sound and instructionally robust content for virtual learning environments across all educational levels. This includes creating subject-specific interactive modules aligned with national curricula for primary and secondary education, as well as professionally grounded content for vocational and corporate training. The content should support both conceptual understanding and practical skill acquisition, with a focus on active, experiential learning.

Accessible Hardware: Significant investment is required in the development of accessible, user-friendly, and cost-effective AR/VR hardware. These devices must be suitable for diverse learning contexts, from child-friendly designs for primary education to ruggedised, industry-compliant equipment for vocational training, and ergonomic devices for prolonged use in professional settings. Ensuring accessibility features are embedded by design will be crucial for inclusive learning environments.

AI Integration: The integration of AI and large language models into virtual learning environments should be prioritised. This includes developing AI agents for adaptive tutoring, personalised feedback, and dynamic student interaction in classroom simulations. AI-powered content personalisation engines and real-time affective computing will enable more responsive and emotionally adaptive learning experiences, particularly for special education.

Research and Evaluation: Fostering research into the effectiveness and impact of VW on learning outcomes is paramount. This includes studies on developmental effectiveness, cognitive load, skill transfer from virtual to real-world contexts, and the long-term retention of knowledge. Research into learner agency, metacognition, and trust in AI tutors will also be vital for optimising human-AI collaboration in learning.

Professional Development: Addressing the professional development needs of educators and trainers is critical for widespread adoption. This involves integrating VW technologies into teacher education curricula and providing opportunities for professional upskilling in XR workflows, immersive storytelling, and digital asset management. Creating scenario creation and customisation tools accessible to educators will empower them to tailor learning experiences to specific needs, thereby enhancing pedagogical flexibility and confidence.