Artificial Intelligence-Based Digital Twins Metaverse to Improve STEM Work Skills and Adaptive Expertise in Higher Education: A Conceptual Framework

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However, existing learning systems remain fragmented and lack an integrated, human-centered framework capable of supporting high-fidelity simulations, adaptive learning, and real-time competence development aligned with Industry 5.0. This study addresses this gap by developing a unified AI-Based Digital Twins Metaverse (AI-DTM) framework aimed at strengthening STEM employability skills and fostering adaptive expertise in higher education. This research employed a conceptual–analytical design comprising three structured phases: (1) a systematic conceptual synthesis guided by PRISMA 2020 to identify technological, pedagogical, and human-centric mechanisms relevant to AI, DTs, and Metaverse-based learning; (2) construction of an integrated AI-DTM framework through theoretical mapping, concept modeling, and iterative refinement; and (3) expert validation involving specialists in AI systems, digital twins, immersive learning, and instructional design. Evidence from interdisciplinary literature across AI-enhanced learning, DT-enabled simulation, and XR-based experiential environments informed the development of the framework. The resulting AI-DTM framework integrates AI-driven learner modeling, real-time DT simulations, and immersive Metaverse environments to create a unified, adaptive ecosystem. Key outcomes include: (a) personalized learning pathways supported by intelligent analytics and automated feedback; (b) high-fidelity, risk-free simulations that replicate authentic STEM work processes; (c) immersive and collaborative virtual experiences that enhance engagement, problem-solving, and teamwork; and (d) continuous competence profiling enabling the development of adaptive expertise. The framework demonstrates strong alignment with Industry 5.0 principles, supporting human–AI collaboration, data-driven decision-making, and future workforce readiness. This study provides a novel, scalable model that advances the design of AI-enabled learning ecosystems by integrating AI, Digital Twins, and Metaverse technologies into a cohesive architecture. The AI-DTM framework offers theoretical and practical contributions for enhancing STEM employability skills, strengthening adaptive expertise, and guiding institutions." } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/14-1376/v1", "name": "Artificial Intelligence-Based Digital Twins Metaverse to Improve STEM..." } } ] } Home Browse Artificial Intelligence-Based Digital Twins Metaverse to Improve STEM... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Syarifuddin S, Kurra T, Susanto A et al. Artificial Intelligence-Based Digital Twins Metaverse to Improve STEM Work Skills and Adaptive Expertise in Higher Education: A Conceptual Framework [version 1; peer review: 1 approved] . F1000Research 2025, 14 :1376 ( https://doi.org/10.12688/f1000research.174205.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Systematic Review Artificial Intelligence-Based Digital Twins Metaverse to Improve STEM Work Skills and Adaptive Expertise in Higher Education: A Conceptual Framework [version 1; peer review: 1 approved] Syarifuddin Syarifuddin 1,2 , Titus Kurra 1,3 , Aris Susanto 4,5 , [...] Hendra Dani Saputra 1,6 , Marko Ayaki Lumbantobing 1,7 , Ratna Pancawati https://orcid.org/0000-0003-2297-6533 1,7 , Karlina Wong Lieung 8,9 , Andry Tanggu Mara https://orcid.org/0009-0007-5981-6847 1,3 Syarifuddin Syarifuddin 1,2 , Titus Kurra 1,3 , [...] Aris Susanto 4,5 , Hendra Dani Saputra 1,6 , Marko Ayaki Lumbantobing 1,7 , Ratna Pancawati https://orcid.org/0000-0003-2297-6533 1,7 , Karlina Wong Lieung 8,9 , Andry Tanggu Mara https://orcid.org/0009-0007-5981-6847 1,3 PUBLISHED 08 Dec 2025 Author details Author details 1 Technology and Vocational Education, Universitas Negeri Yogyakarta Program Pascasarjana, Yogyakarta, Special Region of Yogyakarta, Indonesia 2 Department of Physics Education, Universitas Halu Oleo Fakultas Keguruan dan Ilmu Pendidikan, Kendari, South East Sulawesi, Indonesia 3 Information Technology Education, Stella Maris University, Tambolaka, East Nusa Tenggara, Indonesia 4 Educational Research and Evaluation, Universitas Negeri Yogyakarta Program Pascasarjana, Yogyakarta, Special Region of Yogyakarta, Indonesia 5 Primary School Teacher Education, STKIP Pelita Nusantara, Buton, Southeast Sulawesi, Indonesia 6 Department of Automotive Engineering, Universitas Negeri Padang, Padang, West Sumatra, Indonesia 7 Mechanical Engineering Education, Universitas Palangka Raya, Palangkaraya, Central Kalimantan, Indonesia 8 Primary Education, Universitas Negeri Yogyakarta, Yogyakarta, Special Region of Yogyakarta, Indonesia 9 Primary School Teacher Education, Universitas Musamus Fakultas Keguruan dan Ilmu Pendidikan, Merauke, Papua, Indonesia Syarifuddin Syarifuddin Roles: Conceptualization, Funding Acquisition, Investigation, Project Administration, Supervision, Writing – Original Draft Preparation Titus Kurra Roles: Conceptualization, Project Administration, Resources, Software, Writing – Review & Editing Aris Susanto Roles: Data Curation, Formal Analysis, Investigation, Supervision, Visualization Hendra Dani Saputra Roles: Data Curation, Methodology, Resources, Software, Supervision Marko Ayaki Lumbantobing Roles: Data Curation, Investigation, Project Administration, Resources, Validation, Writing – Review & Editing Ratna Pancawati Roles: Formal Analysis, Investigation, Methodology, Supervision, Validation, Writing – Review & Editing Karlina Wong Lieung Roles: Data Curation, Investigation, Project Administration, Resources, Visualization Andry Tanggu Mara Roles: Conceptualization, Data Curation, Methodology, Software, Visualization, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Artificial Intelligence and Machine Learning gateway. This article is included in the Advances in Metaverse, Gaming and Virtual Reality collection. Abstract Rapid advances in Artificial Intelligence (AI), Digital Twin (DT) technology, and the Metaverse have created new opportunities for transforming STEM education and workforce preparation. However, existing learning systems remain fragmented and lack an integrated, human-centered framework capable of supporting high-fidelity simulations, adaptive learning, and real-time competence development aligned with Industry 5.0. This study addresses this gap by developing a unified AI-Based Digital Twins Metaverse (AI-DTM) framework aimed at strengthening STEM employability skills and fostering adaptive expertise in higher education. This research employed a conceptual–analytical design comprising three structured phases: (1) a systematic conceptual synthesis guided by PRISMA 2020 to identify technological, pedagogical, and human-centric mechanisms relevant to AI, DTs, and Metaverse-based learning; (2) construction of an integrated AI-DTM framework through theoretical mapping, concept modeling, and iterative refinement; and (3) expert validation involving specialists in AI systems, digital twins, immersive learning, and instructional design. Evidence from interdisciplinary literature across AI-enhanced learning, DT-enabled simulation, and XR-based experiential environments informed the development of the framework. The resulting AI-DTM framework integrates AI-driven learner modeling, real-time DT simulations, and immersive Metaverse environments to create a unified, adaptive ecosystem. Key outcomes include: (a) personalized learning pathways supported by intelligent analytics and automated feedback; (b) high-fidelity, risk-free simulations that replicate authentic STEM work processes; (c) immersive and collaborative virtual experiences that enhance engagement, problem-solving, and teamwork; and (d) continuous competence profiling enabling the development of adaptive expertise. The framework demonstrates strong alignment with Industry 5.0 principles, supporting human–AI collaboration, data-driven decision-making, and future workforce readiness. This study provides a novel, scalable model that advances the design of AI-enabled learning ecosystems by integrating AI, Digital Twins, and Metaverse technologies into a cohesive architecture. The AI-DTM framework offers theoretical and practical contributions for enhancing STEM employability skills, strengthening adaptive expertise, and guiding institutions. READ ALL READ LESS Keywords Artificial Intelligence; Digital Twins; Metaverse; Industry 5.0; Adaptive Expertise; STEM Employability Skills; Human–AI Collaboration; Immersive Learning; Higher Education; Future Skills; Intelligent Learning Environments Corresponding Author(s) Andry Tanggu Mara ( [email protected] ) Close Corresponding author: Andry Tanggu Mara Competing interests: No competing interests were disclosed. Grant information: The authors gratefully acknowledge the financial and institutional support provided by the Indonesian Education Scholarship (BPI), Doctoral Scholarship Program for Indonesian Lecturers (PDDI), Center for Higher Education Funding and Assessment (PPAPT), Ministry of Higher Education, Science and Technology of the Republic of Indonesia, and the Indonesian Endowment Fund for Education (LPDP). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2025 Syarifuddin S et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The author(s) is/are employees of the US Government and therefore domestic copyright protection in USA does not apply to this work. The work may be protected under the copyright laws of other jurisdictions when used in those jurisdictions. How to cite: Syarifuddin S, Kurra T, Susanto A et al. Artificial Intelligence-Based Digital Twins Metaverse to Improve STEM Work Skills and Adaptive Expertise in Higher Education: A Conceptual Framework [version 1; peer review: 1 approved] . F1000Research 2025, 14 :1376 ( https://doi.org/10.12688/f1000research.174205.1 ) First published: 08 Dec 2025, 14 :1376 ( https://doi.org/10.12688/f1000research.174205.1 ) Latest published: 08 Dec 2025, 14 :1376 ( https://doi.org/10.12688/f1000research.174205.1 ) 1. Introduction 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) RQ1: How can an AI-enabled Digital Twins Metaverse ecosystem enhance STEM employability skills and foster adaptive expertise among higher education learners? RQ2: What roles do AI-driven personalization and real-time adaptive feedback play in optimizing learning within Digital Twin–based Metaverse environments? RQ3: How do Digital Twins contribute to the authenticity, fidelity, and transferability of STEM work practices simulated in the Metaverse? RQ4: Which components of the integrated AI–Digital Twins–Metaverse framework most significantly influence the development of learners’ adaptive expertise? RQ5: In what ways does immersion in a Metaverse environment mediate or moderate the relationship between simulation-based learning and employability skills? RQ6: How can the proposed conceptual framework inform future instructional design and assessment strategies for Industry 4.0/5.0-ready STEM education? 2. Hypotheses (H) These hypotheses assume your study may later use SEM, PLS-SEM, or a hybrid quantitative design. H1: AI-driven personalization has a positive and significant effect on the development of STEM employability skills in the Digital Twins Metaverse. H2: AI-generated adaptive feedback positively influences learners’ problem-solving, decision-making, and collaboration skills. H3: The fidelity and authenticity of Digital Twin simulations positively affect learners’ ability to transfer skills to real-world STEM contexts. H4: Real-time performance data from Digital Twins has a significant positive effect on learners’ adaptive expertise. H5: Immersion in Metaverse environments mediates the relationship between simulation-based learning and employability skills. H6: Higher levels of interactivity and collaboration in Metaverse environments significantly enhance learners’ adaptive expertise. H7: The combined use of AI, Digital Twins, and Metaverse technologies has a greater positive effect on STEM employability skills than any single technology used independently. H8: Adaptive expertise mediates the relationship between experiential learning (enabled by DT + Metaverse) and employability skill development. H9: Learners’ prior digital literacy moderates the effectiveness of AI-driven Metaverse learning, such that the effect is stronger for higher levels of digital literacy. H10: Perceived usefulness moderates the impact of Metaverse immersion on employability skill acquisition. 2. Method 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. 2.1 Research design A conceptual design approach is appropriate because AI-driven digital twin systems, metaverse-based learning, semantic communications, and Industry 5.0 infrastructures remain emergent and require theoretical integration before empirical deployment. Similar methodological approaches have been used in AI-enabled healthcare ( Kchaou et al., 2025 ), digital twin urban systems ( Kalfas et al., 2025 ), immersive metaverse technologies ( Hamidouche et al., 2024 ), construction education ( Jelodar, 2025 ), and metaverse-based learning environments ( Waquar et al., 2025 ). The present study synthesizes cross-disciplinary evidence from metaverse research, digital twin applications, AI design principles, 6G-enabled communication systems, and emerging educational technologies to conceptualize an integrated model for STEM higher education. 2.2 Phase 1: Systematic conceptual synthesis Produce a rigorous, reproducible synthesis of technological and pedagogical literature that will (a) identify core constructs and mechanisms for AI-DTM, (b) map theoretical underpinnings linking technology to learning outcomes (STEM employability & adaptive expertise), and (c) generate an evidence-based codebook to drive framework construction ( Debnath & Srivastava, 2025 ). 2.2.1 Databases and sources Searches will be performed in multiple bibliographic and grey-literature sources to ensure interdisciplinary coverage: a. Scholarly databases: Scopus, Web of Science, IEEE Xplore, PubMed (for health/assistive tech literature), SpringerLink, Emerald, ScienceDirect, and Google Scholar. b. Preprint and technical repositories: arXiv, SSRN. c. Policy and standards: EU AI Act documents, white papers, and relevant regulatory briefs. d. Conference proceedings: IEEE, ACM, InC4, and major metaverse/AI/education events. 2.2.2 Timeframe and language a. Timeframe: Publications from 2018 through 2025 (captures Industry 4.0 → 5.0 transitions, recent metaverse and DT work). b. Language: English. 2.2.3 Search strategy and example search strings Develop domain-specific search strings combining keywords and Boolean operators. Example search strings (to be adapted per database syntax): (“digital twin” OR “digital twins”) AND (education OR learning OR “higher education” OR STEM) (“metaverse” OR “extended reality” OR “virtual reality” OR “augmented reality”) AND (learning OR education OR training) (“artificial intelligence” OR “AI” OR “machine learning” OR “reinforcement learning”) AND (“metaverse” OR “digital twin”) (“adaptive expertise” OR “adaptive learning” OR “transfer of learning”) AND (“simulation” OR “digital twin” OR “metaverse”) Each search records the database, date run, search string, and total hits. 2.2.4 Inclusion and exclusion criteria Inclusion: a. Empirical studies, reviews, conceptual papers, standards/whitepapers, and high-quality preprints that address one or more of: Digital Twins, Metaverse/XR for learning, AI for adaptive learning, STEM competency development, or adaptive expertise. b. Studies discussing technological infrastructures relevant to low-latency, edge/6G, semantic communications, or DT-AI governance. Exclusion: a. Papers not relevant to learning or human competency (purely industrial DTs with no educational implications) unless they include transferable methodology. b. Non-English publications. c. Short abstracts with insufficient methodological detail. 2.2.5 Screening and selection a. Deduplication using reference manager (EndNote/Zotero). b. Title–abstract screening by two independent reviewers. Records marked include/uncertain/exclude. Disagreements resolved through discussion; unresolved items adjudicated by a third reviewer. c. Full-text screening using same two-reviewer process. Reasons for exclusion recorded. Quality/consensus metric: Compute Cohen’s kappa for inter-rater agreement at title–abstract and full-text stages; target κ ≥ 0.70. Figure 1 presented the PRISMA 2020 flow diagram illustrates the structured process applied in Phase 1 to ensure transparent and reproducible literature synthesis. It traces the progression of records from initial database identification through duplicate removal, screening, eligibility assessment, and final inclusion (n = 42). This systematic mapping approach strengthens the evidence base underpinning the conceptual construction of the AI-Enabled Metaverse Digital Twins model by following established PRISMA 2020 reporting standards ( Page et al., 2021 ). Figure 1. PRISMA 2020 flow diagram for the systematic conceptual synthesis. 2.2.6 Data extraction For each included study extract standardized fields into a spreadsheet: • bibliographic details (authors, year, venue) • study type (empirical, conceptual, review, standard) • domain/setting (education, industry, healthcare, smart city) • technology focus (DT, metaverse/XR, AI, edge/6G) • pedagogical approach (PBL, experiential, constructivist, MOOC5.0, etc.) • learners/participants (STEM level, sample size) • measured outcomes (employability skills, adaptive expertise, transfer) • methodological notes (metrics, analytics, model architectures) • governance/ethical considerations (privacy, EU AI compliance) • key findings and claimed limitations • suggested future research/gaps 2.2.7 Codebook development and thematic analysis • Open coding: Two researchers independently read a random sample (≈20%) of included papers to generate initial codes (technical affordances, pedagogical mechanisms, data types, learning outcomes). • Codebook: Consolidate codes into a structured codebook with definitions, inclusion/exclusion examples, and hierarchical categories (e.g., Technology → DT fidelity → sensors; Pedagogy → PBL → scaffolding). • Intercoder reliability: Apply the codebook to another sample and compute Cohen’s kappa for major code categories; revise until κ ≥ 0.70. • Full coding: Use qualitative analysis software (e.g., NVivo or Atlas.ti) to code all included texts. Thematic synthesis will follow Braun & Clarke’s six-step procedure adapted for concept mapping: familiarization, code generation, searching for themes, reviewing themes, defining/naming themes, and producing the synthesis. 2.2.8 Concept mapping and mechanism elaboration From themes, build evidence-based concept maps that show: • Technological affordances → pedagogical mechanisms → learner processes → outcomes. • Example: DT fidelity (affordance) → realistic problem complexity (mechanism) → iterative practice + feedback (process) → transfer & adaptive expertise (outcome). Maps will be created using diagram tools (e.g., Miro, Lucidchart), annotated with supporting citations and confidence levels (high/medium/low evidence). 2.2.9 Synthesis outputs Deliverables from Phase 1: Phase 1 generated several synthesis outputs that establish the conceptual basis for the AI-Enabled Metaverse Digital Twins (AI-MDT) framework. A PRISMA-structured search log was produced to ensure transparent reproducibility of the systematic conceptual synthesis. An integrated extraction matrix organized coded evidence on AI-driven personalization ( Joshi et al., 2025 ), Digital Twin simulation fidelity ( Khan & Ahmad, 2025 ), and Metaverse-enabled immersion ( Puneet et al., 2024 ), along with documented relationships to employability skills and adaptive expertise. A structured codebook standardized definitions across technological, pedagogical, and competency constructs to support analytic coherence. The thematic map and linkage model illustrated how AI, DT, and Metaverse affordances connect to cognitive, metacognitive, and experiential learning mechanisms ( Majumder & Dey, 2024 ; Soliman et al., 2024 ). A gap matrix identified key underserved intersections—most notably the limited research linking Digital Twins to adaptive expertise in higher education. Finally, concise evidence statements summarized empirical support for core propositions in the framework, such as the benefits of AI personalization ( Iqbal et al., 2024 ), DT–simulation-based transfer ( Nicoletti, 2023 ), and immersive metaverse collaboration ( Jalhotra et al., 2024 ). 2.2.10 Validation and sensitivity checks • Triangulation: Cross-validate themes against policy/regulatory documents (e.g., EU AI Act) and industry white papers to ensure technological feasibility and governance awareness. • Sensitivity check: Re-run searches with alternative keywords and check for missing seminal works. • Expert spot-check: Present preliminary maps to 2–3 domain experts for face validity and to identify overlooked literature. 2.2.11 Timeline and resources Estimated effort: 8–10 weeks for Phase 1 (scoping: 1 week; searching & deduplication: 1–2 weeks; screening: 2 weeks; extraction & coding: 2–3 weeks; mapping & validation: 1–2 weeks). Team: 2 coders + 1 adjudicator + PI. 2.3 Phase 2: Framework construction The second phase involved translating the synthesized theoretical foundations into a coherent AI-DTM conceptual framework. This process began by integrating the technological elements identified in Phase 1—AI-driven analytics, digital twin modeling, and metaverse-based immersive environments—into a unified structure aligned with established principles of technology-enhanced learning ( Laurillard, 2012 ; Wong et al., 2022 ). Key constructs such as real-time data capture, learner modeling, adaptive feedback, and virtual task simulation were iteratively mapped to STEM work-skill indicators and adaptive expertise characteristics ( Bransford & Schwartz, 1999 ; Nishi, Hatano & Inagaki, 2017 ). Framework construction proceeded through continuous refinement cycles in which theoretical assumptions were compared with practices reported in digital twin research and extended reality pedagogy ( Fuller et al., 2020 ; Radianti et al., 2020 ). Expert consultation supported the validation of logical linkages between system components and targeted learning outcomes, ensuring internal consistency and domain relevance. This iterative conceptual prototyping allowed the team to evaluate multiple structural configurations and select the one with the strongest alignment between AI functionality, metaverse affordances, and the progression of learner competence. The resulting framework articulates how AI-enabled digital twins operate within metaverse learning spaces to simulate authentic STEM work processes, personalize learning pathways, and foster continuous adaptive expertise development ( Rejeb et al., 2023 ). The AI-DTM conceptual framework was developed using an integrative modelling process that connected: a. AI cognitive engines, including generative AI and reinforcement learning ( Narottama et al., 2025 ) b. Dynamic digital twin models for simulation-based learning ( Kalfas et al., 2025 ; Masaracchia et al., 2022 ) c. Immersive metaverse layers enabling real-time interaction ( Hamidouche et al., 2024 ; Shastry & Mohan, 2024 ) d. Edge and 6G communication layers for low-latency learning environments ( Sharma et al., 2024 ) e. Learner adaptation mechanisms, such as personalized feedback loops, skill assessment agents, and performance prediction ( Jelodar, 2025 ) The resulting model describes how AI-driven digital twins and metaverse experiences provide adaptive, data-driven, and scalable pathways for STEM competency development. 2.4 Phase 3: Expert review and validation Figure 2 presents the structured validation workflow implemented in Phase 3 of the study. A panel comprising five domain experts—specialists in artificial intelligence systems, digital twin technologies, metaverse-based education, instructional design, and STEM pedagogy—systematically evaluated the proposed AI-Enabled Metaverse Digital Twins framework. The evaluation encompassed four core dimensions: (a) conceptual clarity and internal coherence of the framework’s constructs, (b) technological feasibility and systems-level implementability, (c) pedagogical appropriateness with respect to instructional design principles, and (d) alignment with prevailing regulatory and ethical standards. The review process followed established validation protocols used in digital health AI frameworks ( Kchaou et al., 2025 ) and metaverse educational models ( Waquar et al., 2025 ). Expert feedback was incorporated through iterative refinement cycles until a consensus-based confirmation of validity was achieved . Figure 2. Expert review and validation procedure. 2.5 Ethical and regulatory considerations Although this study did not involve human participants, the framework design aligns with emerging AI governance models, particularly the EU AI Act for general-purpose AI systems and digital twin environments ( Borrelli et al., 2025 ). Considerations for data security, algorithmic transparency, accessibility, and inclusiveness echo principles discussed in digital health, metaverse, and Industry 5.0 literature ( Shastry & Mohan, 2024 ; Kchaou et al., 2025 ; Lee, 2025 ). 3. Result 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. Figure 3. The AI-Based Digital Twins Metaverse (AI-DTM) conceptual framework. 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 1. AI-DTM model results: Multidimensional outcomes and cross-layer contributions. Model component Technological layer Operational mechanisms Primary educational outcomes Associated evidence from literature AI Cognitive Engine Artificial Intelligence Layer (Generative AI, RL, Predictive AI) • Intelligent tutoring • Generative scenario creation • Automated analytics for learning behaviour • Continuous optimisation through reinforcement learning • Cognitive Development • Advanced problem-solving • Predictive decision-making • Self-regulated learning Iqbal et al. (2024) : AI enhances adaptive learning and optimisation of instructional processes. Al-Khatib et al. (2024) : AI-driven modelling transforms education and professional training. Digital Twin Core Digital Twin Simulation Layer • Real-time mirroring of tasks • High-fidelity STEM process modelling • Scenario-based adaptive simulations • Multi-source data ingestion • Technical and Procedural Skills • Operational accuracy • Real-time diagnostic ability • System-level reasoning Nicoletti (2023) : DT strengthens human–automation integration. Dai et al. (2024) : DT supports Industry 5.0 readiness and secure operational modelling. XR–Metaverse Interaction Hub Metaverse Layer (VR/AR/XR) • Immersive 3D learning tasks • Embodied interaction • Haptic and spatial feedback • Multiplayer collaborative scenarios • Experiential & Collaborative Skills • Spatial reasoning • Team-based problem-solving • Immersive task mastery Jalhotra et al. (2024) : Metaverse enhances instructional interactivity. Hassoun et al. (2025) : XR supports Industry 5.0 experiential learning. Cross-Layer Integration AI + DT + XR Fusion • Synchronised cognitive–sensor data • AI-generated improvement cycles • Dynamic performance dashboards • Personalized skill progression pathways • Adaptive Expertise • Transfer across novel tasks • Flexible knowledge restructuring • High-order metacognition Patil (2024) : AI reshapes workforce skills for adaptability. Akbas (2025) : Digital transformation frameworks accelerate competency development. STEM Workforce Alignment Output Layer (Employability) • Simulation-to-workflow transfer • High-fidelity industry-based tasks • Predictive skills-gap identification • Work Readiness & Professional Competence • Industry 5.0 aligned competencies • Human–machine collaboration ability • Data-driven decision-making Rakholia et al. (2024) : AI enables efficient manufacturing task execution. Raja Santhi & Muthuswamy (2023) : Industry 5.0 requires human–AI synergy. 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. Table 2. Mapping AI-DTM features to strategic impact. Feature category AI cognitive engine Digital twin simulation Metaverse XR interaction Strategic impact on higher education Automation & Intelligence Automated assessment, predictive tutoring Real-time simulation accuracy Behavioural immersion with automated prompts Efficient instructional delivery ( Iqbal et al., 2024 ) Immersion & Fidelity AI-generated adaptive challenges High-fidelity virtual replicas Full 3D multi-sensory environments Enhanced experiential learning ( Jalhotra et al., 2024 ) Adaptation & Personalization Reinforcement learning loops Adaptive scenario reconstruction Individualised VR task complexity Personalized learning pathways ( Nicoletti, 2023 ) Competency Development Critical thinking, reasoning Technical operations mastery Collaboration, communication Holistic skill formation ( Patil, 2024 ) Industry 5.0 Alignment Generative innovation tasks Automation-human integration Digital co-working spaces Workforce readiness ( Rakholia et al., 2024 ) 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. Figure 4. Integrated AI–Digital Twins–Metaverse (AI-DTM) architecture for enhancing STEM skills and adaptive expertise. 4. Discussion 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. b. Dynamic Competency Profiling Through Real-Time Behavioral Analytics The model introduces a mechanism that allows competencies to be updated continuously via AI-driven interpretation of behavioral traces—an advancement over static assessment models. c. A Scalable, Modular Architecture for Vocational and Professional Education Unlike earlier metaverse learning environments, the proposed design supports cross-domain scenario adaptation, improving transferability and implementation feasibility. d. Strengthened Theoretical Foundation for Immersive Learning Analytics By embedding analytics within the learning ecosystem, the study provides a new explanation for how feedback loops can accelerate skill acquisition and performance accuracy. 4.1 Implications 4.1.1 Implications for theory The study contributes to the theoretical development of immersive learning ecosystems, particularly in three ways. First, it reinforces the shift from content-centered to data-centered learning models, building on learning analytics and AI in education theory ( Ifenthaler & Yau, 2020 ). Second, it expands the conceptualization of metaverse learning environments by positioning digital twins as mediators between learner behavior and adaptive learning pathways—offering a new construct in immersive learning design. Third, it provides empirical grounding for dynamic competency frameworks, supporting arguments for continuous, real-time assessment rather than episodic evaluations ( Lee & Hwang, 2022 ). 4.1.2 Implications for practice Practically, the model offers a clear blueprint for institutions aiming to enhance training effectiveness through immersive, AI-supported systems. Educators can employ digital twins to monitor learners’ progress with higher precision, enabling tailored remediation and skill reinforcement. Training providers and industry partners may adopt the framework to simulate workplace competencies more accurately, improving job readiness in vocational contexts. Moreover, the scalable architecture reduces long-term development costs, making it suitable for broad implementation across diverse training programs and remote-learning settings. 4.2 Limitation and future research Despite offering a comprehensive model integrating AI, digital twins, and metaverse-based learning environments, this study has several limitations that should be acknowledged. a. First, the framework was evaluated within a controlled scenario and may not fully capture the complexity and unpredictability of real-world vocational settings. External variables—such as learner diversity, infrastructure disparities, and varying levels of digital literacy—may influence the system’s performance and generalizability. Future studies should conduct multi-context field trials across institutional and industry environments to validate ecological robustness. b. Second, although the model leverages multimodal behavioral analytics, the accuracy of competence profiling is still constrained by the current capabilities of AI-driven interpretation. Certain affective or socio-emotional competencies may remain difficult to detect through digital trace data alone. Further research should explore hybrid measurement strategies that combine automated analytics with qualitative or human-coded observations to increase fidelity and avoid algorithmic bias. c. Third, the system architecture assumes stable connectivity and relatively high computational resources. These technological requirements may limit implementation in regions with limited digital infrastructure, potentially contributing to unequal access. Future research should investigate lightweight or offline-capable versions of the framework to support broader accessibility, especially in developing or underserved educational contexts. d. Fourth, the present study does not evaluate long-term learning trajectories or skill retention over extended periods. The adaptive feedback loops demonstrate immediate performance improvements, but longitudinal research is needed to determine whether competency gains persist and transfer to real-world tasks. Future work should incorporate time-series analysis and follow-up assessments to measure sustained impact. e. Finally, while the integrated model shows strong theoretical coherence, its interoperability with existing institutional systems—such as LMS, HRD platforms, or certification mechanisms—requires further investigation. Future research should examine integration pathways, data governance models, privacy frameworks, and ethical considerations, particularly concerning learner profiling and automated decision-making. 5. Conclusion 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. Figure 5. Integration of AI, digital twins, and metaverse for STEM innovation in higher education. 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. Institutional review board statement Not applicable. This study did not involve human participants, clinical procedures, or any activities requiring ethical approval under institutional or national guidelines. Informed consent statement Not applicable. No human participants were recruited, surveyed, or recorded in this conceptual and framework-development study. Data availability statement All supplementary data that support the findings of this study are openly available on Zenodo at the following DOI: https://doi.org/10.5281/zenodo.17686367 ( Tanggu Mara, A., 2025 ). Data are available under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). 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Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 08 Dec 2025 ADD YOUR COMMENT Comment Author details Author details 1 Technology and Vocational Education, Universitas Negeri Yogyakarta Program Pascasarjana, Yogyakarta, Special Region of Yogyakarta, Indonesia 2 Department of Physics Education, Universitas Halu Oleo Fakultas Keguruan dan Ilmu Pendidikan, Kendari, South East Sulawesi, Indonesia 3 Information Technology Education, Stella Maris University, Tambolaka, East Nusa Tenggara, Indonesia 4 Educational Research and Evaluation, Universitas Negeri Yogyakarta Program Pascasarjana, Yogyakarta, Special Region of Yogyakarta, Indonesia 5 Primary School Teacher Education, STKIP Pelita Nusantara, Buton, Southeast Sulawesi, Indonesia 6 Department of Automotive Engineering, Universitas Negeri Padang, Padang, West Sumatra, Indonesia 7 Mechanical Engineering Education, Universitas Palangka Raya, Palangkaraya, Central Kalimantan, Indonesia 8 Primary Education, Universitas Negeri Yogyakarta, Yogyakarta, Special Region of Yogyakarta, Indonesia 9 Primary School Teacher Education, Universitas Musamus Fakultas Keguruan dan Ilmu Pendidikan, Merauke, Papua, Indonesia Syarifuddin Syarifuddin Roles: Conceptualization, Funding Acquisition, Investigation, Project Administration, Supervision, Writing – Original Draft Preparation Titus Kurra Roles: Conceptualization, Project Administration, Resources, Software, Writing – Review & Editing Aris Susanto Roles: Data Curation, Formal Analysis, Investigation, Supervision, Visualization Hendra Dani Saputra Roles: Data Curation, Methodology, Resources, Software, Supervision Marko Ayaki Lumbantobing Roles: Data Curation, Investigation, Project Administration, Resources, Validation, Writing – Review & Editing Ratna Pancawati Roles: Formal Analysis, Investigation, Methodology, Supervision, Validation, Writing – Review & Editing Karlina Wong Lieung Roles: Data Curation, Investigation, Project Administration, Resources, Visualization Andry Tanggu Mara Roles: Conceptualization, Data Curation, Methodology, Software, Visualization, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The authors gratefully acknowledge the financial and institutional support provided by the Indonesian Education Scholarship (BPI), Doctoral Scholarship Program for Indonesian Lecturers (PDDI), Center for Higher Education Funding and Assessment (PPAPT), Ministry of Higher Education, Science and Technology of the Republic of Indonesia, and the Indonesian Endowment Fund for Education (LPDP). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (1) version 1 Published: 08 Dec 2025, 14:1376 https://doi.org/10.12688/f1000research.174205.1 Copyright © 2025 Syarifuddin S et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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This also has a rigorous methodology which demonstrates soundness and follows established standards. Moreover, this article has comprehensive scope that addresses a genuinely novel intersection that hasn't been extensively explored. The abstract sounds like a blend of buzzwords vs. being substantive. Consider whether all 3 technologies are truly necessary - what problem does bundling all 3 technologies solve that couldn't be addressed with 2? Overall, this is an article that makes a meaningful contribution to immersive learning design but needs stronger grounding in implementation realities and learning science fundamentals. Are the rationale for, and objectives of, the Systematic Review clearly stated? Yes Are sufficient details of the methods and analysis provided to allow replication by others? Yes Is the statistical analysis and its interpretation appropriate? Not applicable Are the conclusions drawn adequately supported by the results presented in the review? 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This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This is an important article that has a clear gap identification and the fragmentation problem of the lack of human-centered integration. This also has a rigorous methodology which demonstrates soundness and follows established standards. Moreover, this article has comprehensive scope that addresses a genuinely novel intersection that hasn't been extensively explored. The abstract sounds like a blend of buzzwords vs. being substantive. Consider whether all 3 technologies are truly necessary - what problem does bundling all 3 technologies solve that couldn't be addressed with 2? Overall, this is an article that makes a meaningful contribution to immersive learning design but needs stronger grounding in implementation realities and learning science fundamentals. Are the rationale for, and objectives of, the Systematic Review clearly stated? Yes Are sufficient details of the methods and analysis provided to allow replication by others? Yes Is the statistical analysis and its interpretation appropriate? Not applicable Are the conclusions drawn adequately supported by the results presented in the review? Partly If this is a Living Systematic Review, is the ‘living’ method appropriate and is the search schedule clearly defined and justified? (‘Living Systematic Review’ or a variation of this term should be included in the title.) Not applicable Competing Interests No competing interests were disclosed. Reviewer Expertise Cybersecurity, Generative AI, XAI I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Rana S. Peer Review Report For: Artificial Intelligence-Based Digital Twins Metaverse to Improve STEM Work Skills and Adaptive Expertise in Higher Education: A Conceptual Framework [version 1; peer review: 1 approved] . F1000Research 2025, 14 :1376 ( https://doi.org/10.5256/f1000research.192087.r450793) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. 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