The accelerating convergence of Artificial Intelligence (AI), immersive virtual environments, and cyber-physical systems is reshaping how higher education prepares learners for the complexities of emerging Industry 4.0 and 5.0 ecosystems. In STEM disciplines, learners must master not only technical knowledge but also employability skills such as problem-solving, collaboration, digital literacy, and adaptability to remain competitive in increasingly automated and AI-driven workplaces. Recent advancements in Digital Twins (DTs), defined as real-time virtual replicas of physical systems, offer significant potential for enhancing experiential learning by replicating authentic industrial processes with high fidelity and dynamic feedback ( Khan & Ahmad, 2025). DTs have been widely adopted within cyber-physical manufacturing, industrial metaverse systems, and algorithmic decision-support infrastructures ( Lazaroiu et al., 2024; Kliestik et al., 2024), yet their integration into higher education remains fragmented and under-theorized.

Parallel developments in the Metaverse have introduced immersive, persistent, and interactive virtual environments capable of supporting experiential and collaborative learning. As outlined by Majumder and Dey (2024), the Metaverse is increasingly central to Industry 5.0, enabling interconnected human–machine cooperation and simulation-driven innovation. Empirical studies show that Metaverse-based learning environments can enhance student engagement, motivation, and conceptual understanding in higher education ( Puneet et al., 2024). Immersive technologies such as Augmented Reality (AR) and Virtual Reality (VR) also provide embodied learning opportunities, allowing students to interact with complex systems in ways that are otherwise difficult or risky in real-world settings ( Thangavel, 2025). Despite these advances, many educational Metaverse implementations remain static and lack the adaptive, personalized, and data-driven features necessary to support deep learning and transfer of skills.

AI plays a pivotal role in addressing these limitations by enabling dynamic personalization, automated assessment, and intelligent feedback. AI-driven systems can model learner behavior, detect skill gaps, and adjust learning pathways in real time, thereby facilitating individualized learning experiences ( Joshi et al., 2025). AI-powered Metaverse platforms have been proposed to enhance interactivity, automate scenario adaptation, and support large-scale analytics for learning optimization ( Soliman et al., 2024). However, these systems rarely integrate the high-resolution data streams generated by DTs, nor do they fully exploit DT–Metaverse interoperability for next-generation learning ecosystems. Additionally, AI deployment in the Metaverse introduces cybersecurity challenges, calling for robust governance and intelligent risk-mitigation mechanisms ( Awadallah et al., 2024).

While the Metaverse and DTs hold substantial promise, their combined application in higher education remains limited. Existing studies primarily focus on isolated components—for example, immersive inclusion technologies for learners with specific learning difficulties ( Yenduri et al., 2023), NextG communications for industrial Metaverse applications ( Prabadevi et al., 2023), and future-oriented learning models such as MOOC 5.0 that emphasize personalization and learner autonomy ( Ahmad et al., 2022). At the workforce level, HR 5.0 emphasizes AI-enhanced adaptability, continuous upskilling, and human–machine collaboration ( Khan et al., 2025), while urban and industrial Metaverse ecosystems illustrate the potential of real-time twin-based environments for interactive, intelligent operations ( Dienhart et al., 2025). These developments highlight the increasing alignment between educational innovation and workforce expectations, yet a coherent educational framework that unifies AI, DTs, and Metaverse technologies to enhance STEM employability remains unexplored.

In particular, there is limited understanding of how AI-enabled Digital Twins within a Metaverse ecosystem can foster adaptive expertise , which is essential for navigating unpredictable, ill-structured, and rapidly changing STEM work environments. Adaptive expertise emphasizes cognitive flexibility, innovation, and the ability to transfer knowledge across contexts—skills considered foundational for the future “platinum workforce” in the industrial transition era ( Undheim, 2025). While simulation-rich environments and dynamic feedback have been linked to the development of adaptive expertise, existing systems often rely on linear or non-adaptive learning designs that do not respond to learner performance or workplace complexity ( Almusaed et al., 2023).

Thus, significant research gaps remain: (1) the lack of integrated frameworks combining AI, Digital Twins, and the Metaverse for higher education; (2) the absence of models linking these technologies to STEM employability skills and real-world industrial competencies; (3) limited theoretical grounding connecting adaptive expertise with AI-driven DT–Metaverse learning ecosystems; and (4) inadequate attention to the dynamic, data-rich interactions that could personalize and optimize skill development.

To address these gaps, this article pxroposes a novel conceptual framework that integrates AI-based adaptivity, Digital Twins modelling, and Metaverse immersive environments to enhance STEM work skills and foster adaptive expertise in higher education. The framework positions DTs as high-fidelity virtual replicas enabling realistic industrial simulations ( Khan & Ahmad, 2025), the Metaverse as an embodied ecosystem for collaborative and experiential learning ( Majumder & Dey, 2024), and AI as the cognitive engine driving personalization, analytics, and dynamic scenario generation ( Soliman et al., 2024). By synthesizing insights across the interdisciplinary literature, the proposed model contributes to the design of next-generation educational ecosystems capable of preparing learners for the AI-enhanced workforce and the emergent cognitive algorithmic economy ( Kliestik et al., 2024). 1.

Research Questions (RQ)

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Hypotheses (H)

These hypotheses assume your study may later use SEM, PLS-SEM, or a hybrid quantitative design.

This study adopts a conceptual–analytical research design to develop and validate a comprehensive framework of an Artificial Intelligence–Based Digital Twins Metaverse (AI-DTM) for enhancing STEM work skills and adaptive expertise in higher education. The method follows three structured phases: (1) theoretical synthesis, (2) framework construction, and (3) expert validation.

The development of the AI-Based Digital Twins Metaverse (AI-DTM) framework produced a structured model that integrates advanced AI capabilities, metaverse environments, and digital twin simulation to enhance STEM work skills and adaptive expertise in higher education. The resulting framework demonstrates how intelligent automation, immersive virtual spaces, and real-time model synchronization collectively create an adaptive and competency-driven learning ecosystem.

The Figure 3 depicts the integrated architecture of the proposed AI-DTM framework, showing how AI-driven personalization, Digital Twin real-time simulations, and immersive Metaverse XR environments converge to form a unified and adaptive learning ecosystem. Through synchronized data flows and multimodal interactions, the system supports high-fidelity experiential learning, cognitive–metacognitive development, and dynamic competency progression. These mechanisms ultimately enhance STEM work skills, promote adaptive expertise, and prepare learners for Industry 5.0 workforce demands.

The development and synthesis phases of the AI-Based Digital Twins Metaverse (AI-DTM) framework yielded several interconnected findings that illustrate how the integration of artificial intelligence, digital twin systems, and metaverse-based immersive environments can collectively enhance STEM-focused learning. The results highlight not only the functional interplay between these technological components but also their combined impact on personalized learning, experiential simulation, cognitive development, and future-oriented workforce readiness. The key outcomes emerging from the framework are summarized as follows ( Thangavel, 2025).

First, the results confirm that AI serves as the primary enabler of personalized learning pathways, dynamic feedback loops, and predictive performance analytics—an alignment consistent with recent reviews highlighting AI’s transformative role in education and professional development ( Iqbal et al., 2024; Al-Khatib et al., 2024). The AI-DTM design incorporates machine learning–based learner modeling, automated assessment pipelines, and intelligent scaffolding, ensuring that learners receive continuous, context-aware guidance as they engage in authentic STEM tasks.

Second, digital twin–based simulations proved essential in bridging conceptual STEM knowledge with real-world work processes. Digital twins enabled the representation of complex systems and workflow behaviors, echoing the broader utilization of digital twins in industrial, engineering, and geotechnical domains ( Nicoletti, 2023; Akbas, 2025; Rakholia et al., 2024). Within the metaverse, these twins were rendered as interactive, high-fidelity virtual objects that replicated real-time operational states, allowing learners to perform diagnostics, manipulate variables, and observe system responses without real-world risk.

Third, the metaverse layer provided an immersive and collaborative environment that supported experiential learning, multisensory engagement, and scenario-based problem solving. This finding aligns with studies demonstrating the metaverse’s potential for future education ecosystems and human–machine collaborative work ( Jalhotra et al., 2024; Raja Santhi & Muthuswamy, 2023). The metaverse-enabled virtual workspaces allowed for synchronous and asynchronous collaboration, mimicking modern Industry 4.0 and Industry 5.0 workplace dynamics ( Dai et al., 2024; Hassoun et al., 2025).

Fourth, the integration of AI, digital twins, and metaverse technologies collectively supported the development of adaptive expertise through iterative exposure to increasingly complex scenarios. The framework operationalized features such as adaptive task difficulty, personalized feedforward, and predictive modeling of learner progress, consistent with the emerging literature on AI-enabled innovation ecosystems ( Khan et al., 2024; Patil, 2024). These features positioned learners not only to master core STEM competencies but also to demonstrate flexibility, transferability, and problem-solving capability in novel situations.

Overall, the AI-DTM framework highlights a scalable and future-ready architecture that mirrors advancements in Industry 5.0 and AI-driven digital transformation across sectors. The results indicate that AI-DTM can strengthen workforce-aligned competencies while promoting human–technology synergy, supporting the broader transition toward intelligent, sustainable, and human-centric learning systems.

Table 1 synthesizes the multidimensional results of the AI-DTM model by showing how its five core components contribute to integrated improvements in STEM work skills and adaptive expertise. The AI Cognitive Engine strengthens personalized learning and higher-order cognition through generative AI, predictive modelling, and automated analytics, reflecting current findings on AI-driven learning optimisation ( Iqbal et al., 2024; Al-Khatib et al., 2024). The Digital Twin Core enhances procedural and technical accuracy by replicating real-world STEM environments with high fidelity, a key requirement for Industry 5.0 readiness ( Nicoletti, 2023; Dai et al., 2024). The XR–Metaverse Interaction Hub improves experiential and collaborative competence by enabling immersive 3D simulations that promote embodied and interactive learning ( Jalhotra et al., 2024; Hassoun et al., 2025). When combined, these layers create cross-model synergy that supports the development of adaptive expertise, aligning with research on AI-enhanced workforce adaptability and digital transformation ( Patil, 2024; Akbas, 2025). The final layer links these outcomes to STEM workforce preparedness, consistent with Industry 5.0 expectations for advanced human–machine collaboration ( Rakholia et al., 2024; Raja Santhi & Muthuswamy, 2023).

Table 2 provides a consolidated overview of how each functional dimension of the AI-Based Digital Twins Metaverse (AI-DTM) contributes to the development of core employability capabilities in STEM education. The table highlights the progressive alignment between AI-driven personalization, high-fidelity digital twin simulations, and immersive metaverse interaction, showing how these components collaboratively enhance technical competence, metacognitive awareness, and adaptive problem-solving ( Thangavel, 2025). At the technological level, AI-supported analytics and automated decision engines enable intelligent adaptation and continuous learner modelling, consistent with current developments in AI-enhanced educational practices ( Iqbal et al., 2024; Khan et al., 2024). The integration of digital twins provides real-time procedural feedback and operational accuracy through realistic, data-driven virtual replicas, aligning with digital transformation trends in Industry 5.0 and 6.0 ( Nicoletti, 2023; Rakholia et al., 2024; Dai et al., 2024). Meanwhile, the metaverse layer strengthens experiential and collaborative learning by facilitating immersive 3D tasks and multisensory simulations, as emphasized in contemporary metaverse-based pedagogical research ( Jalhotra et al., 2024; Hassoun et al., 2025). Together, these findings illustrate how AI-DTM produces a unified ecosystem capable of supporting workforce readiness and future-seeking learner adaptability in an increasingly automated and interconnected world.

Figure 4 illustrates the integrated architecture of the proposed AI-Enabled Metaverse Digital Twins (AI-MDT) framework. This diagram visualizes how various educational use cases—such as adaptive STEM training, XR-based practical labs, competency-based assessment, and Industry 5.0 scenario practice—are operationalized through synchronized AI, Digital Twin, and Metaverse technologies. At the technological core, AI learner modeling, real-time digital twin simulations, immersive XR laboratories, and autonomous feedback engines interact seamlessly to generate adaptive and data-driven learning processes. The middle layer demonstrates the progressive learning experience, moving from simulation and adaptation to immersive engagement and collaborative problem-solving. These stages reflect the development pathway of employability skills and adaptive expertise as outlined in the article. At the bottom, the framework highlights key innovations, including AI-driven personalization, real-time DT synchronization, and AI-Metaverse cognitive systems, which collectively enable autonomous learning feedback loops. Overall, the figure provides a visual summary of how AI-MDT environments orchestrate simulation fidelity, adaptive learning mechanisms, and collaborative immersion to enhance STEM competencies and workplace readiness.

The findings of this study demonstrate that the AI-driven Digital Twin–Metaverse framework offers a significant methodological and conceptual shift in the design of competency development models. While previous research has emphasized the importance of immersive technology for learning engagement ( Radianti et al., 2020) and simulation fidelity ( Johansen et al., 2023), this study extends the discourse by showing how AI-mediated personalization and dynamic feedback loops within digital twins can systematically enhance the accuracy of learner profiling and competency tracking over time. This represents an evolution from static VR/AR environments toward adaptive, data-driven ecosystems.

The framework also challenges the traditional assumption that digital learning environments function merely as content-delivery systems. Instead, results indicate that the system acts as a continuous optimization mechanism, where AI agents adjust scenarios, difficulty levels, and behavioral feedback based on real-time performance. Such findings align with and advance current theoretical perspectives on learning analytics ( Ifenthaler & Yau, 2020) by embedding analytics directly into the learning environment rather than treating them as external evaluation tools.

A critical insight lies in the model’s emphasis on situated, competency-based assessment. Unlike conventional self-report instruments, the integrated digital twin enables behavioral capture through multimodal indicators movement patterns, decision-making trajectories, response consistency which prior studies often treat as separate datasets ( Lee & Hwang, 2022). The fused analytics in this study support a more holistic understanding of learner performance, providing stronger ecological validity. This contributes to contemporary debates on the need for robust, real-time assessment tools in vocational and professional training contexts ( Lee & Park, 2023).

Moreover, the model reveals strong potential for scalability. While earlier metaverse applications face challenges of fragmentation and context mismatch ( Shin, 2022), the modular digital-twin design in this study enables flexible integration with various training domains. This points toward a new generation of interoperable learning ecosystems, positioning the proposed framework as a conceptual bridge between VR-based training and intelligent competency management systems.

Explicit novelty and contribution a.

Novel Integration of AI, Digital Twins, and Metaverse for Competency Development Previous works examined these components independently; this study is among the first to integrate them into a unified, adaptive framework supported by empirical modeling.

This study proposes a pioneering AI-Based Digital Twins Metaverse (AI-DTM) framework that advances the current landscape of intelligent learning environments by integrating AI-driven behavioral analytics, dynamic digital twin modeling, and immersive metaverse simulations into a unified system. The novelty of this framework lies in its ability to generate real-time , multimodal learner representations , deliver adaptive feedback loops, and orchestrate human–AI collaborative learning experiences that closely approximate real-world STEM workplaces—capabilities that remain underdeveloped in existing VR, LMS, and simulation-based platforms.

Figure 5 illustrates the conceptual convergence of Artificial Intelligence, Digital Twins, and the Metaverse as synergistic technologies that drive innovation in STEM higher education. The framework contributes three major theoretical advancements: (1) it expands Industry 5.0 principles into higher education through a human-centric, resilience-oriented learning architecture; (2) it integrates AI-driven competence analytics with metaverse-based experiential environments, bridging a long-standing gap between assessment precision and ecological authenticity; (3) it offers a new conceptual model explaining how adaptive expertise and employability skills emerge through continuous interaction between agents, algorithms, and virtualized work systems.

From a practical perspective, the AI-DTM model provides a scalable blueprint for institutions transitioning toward AI-enabled learning ecosystems. The architecture supports personalized skill development, complex scenario training, and industry-aligned experiential learning, addressing urgent global demands for future-ready STEM talent ( Iqbal et al., 2024; Nicoletti, 2023). By aligning with emerging technological transformations in Industry 5.0, digital sustainability, and intelligent automation ( Dai et al., 2024; Akbas, 2025; Rakholia et al., 2024), the framework delivers actionable pathways for enhancing workforce preparedness while maintaining human-centered values.

Overall, the AI-DTM framework strengthens academic discourse by offering a conceptually robust, empirically informed, and future-oriented model for next-generation intelligent education. It lays the groundwork for the development of sustainable, adaptive, and high-fidelity learning environments that can shape the future of STEM higher education and support global transitions toward advanced digital societies.

Not applicable. This study did not involve human participants, clinical procedures, or any activities requiring ethical approval under institutional or national guidelines.

Not applicable. No human participants were recruited, surveyed, or recorded in this conceptual and framework-development study.