Agentic education · Learning science

Actionable companion · Assessment & reasoning

Learning with agency: Sustainable adoption of artificial intelligence and the Metaverse in higher education: an environmental, social, and governance–based analysis of pedagogical innovation and perceived student learning outcomes

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Higher education. Verify the participant population and task before transferring the findings.

Original authors’ abstract, reproduced under the linked source license:

Abstract The rapid convergence of Artificial Intelligence (AI) and Metaverse technologies is reshaping the higher education landscape by enabling immersive, personalized, and adaptive learning experiences. However, the long-term sustainability of such innovations remains uncertain without addressing environmental, social, and governance (ESG) considerations. This study develops and empirically validates an ESG-informed framework for Sustainable AI–Metaverse Adoption (SAAM) in higher education. A quantitative research design was employed, collecting data from 280 university students across diverse disciplines through a structured survey. Structural Equation Modeling (SEM-PLS) was applied to assess measurement reliability, convergent and discriminant validity, and to test the proposed hypotheses. The empirical results demonstrate that ESG dimensions exert differential effects on sustainable adoption, with environmental and social factors showing stronger direct associations than governance-related variables. Environmental sustainability, through energy-efficient AI systems, significantly enhances SAAM. Similarly, social dimensions, particularly inclusive AI access and student acceptance, exert robust positive effects on sustainable adoption, whereas faculty readiness influences adoption indirectly. Conversely, governance-related factors exhibit comparatively weaker direct effects: institutional policy support enhances digital infrastructure but does not directly influence SAAM, whereas ethical AI use has a limited impact, reflecting student prioritization of usability over ethics in early stages of adoption. Importantly, the outcomes highlight that SAAM substantially fosters digital pedagogical innovation (DPI) and enhanced student learning outcomes (ESLO), confirming its transformative potential. The study contributes theoretically by integrating ESG principles into technology adoption research, offering a multidimensional lens that enriches the understanding of sustainable digital transformation in higher education. Practically, it provides institutions and policymakers with evidence-based insights to design environmentally conscious, socially inclusive, and governance-supported strategies for AI–Metaverse integration. Future research should expand to cross-cultural contexts, larger samples, and longitudinal designs to validate and generalize these findings. Keywords: artificial intelligence, digital pedagogical innovation, ESG framework, higher education, Metaverse, student learning outcomes, sustainable adoption

What kind of evidence is this?

Intelligent tutoring & adaptive learning · Survey / observational study

Stage: Higher education. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

The survey questionnaire designed for this study was constructed using validated scales from previous existing studies. We updated and modified the questionnaire in the context of ESG-conscious Metaverse adoption. Following Nunnally and Bernstein (1994), constructs were defined methodically to significantly cover the content area. Items were adopted and modified from authorized sources, such as TAM, and recent Metaverse in education studies (Alfaisal et al., 2024; Maghaydah et al., 2024; Roy et al., 2023; Dhingra and Abhishek, 2024). We engaged experts to review the content validity of the questionnaire. Three education technology experts and ESG frameworks familiarized themselves with the instrument to assess the clarity, relevance, and representativeness of items. Based on their recommendations, ambiguous items were rewritten, and redundant items were removed. To establish reliability and validity, a pilot study with 55 participants (Saudi Arabian university students and teachers well-versed in digital technologies) assessed reliability using Cronbach’s alpha and composite reliability measures, where all the constructs were above the recommended 0.70 threshold level (Hair et al., 2017; Soomro et al., 2024). Construct validity was established by performing exploratory factor analysis, where acceptable loadings and convergent validity (AVE > 0.50) were ensured. Discriminant validity checking using the Fornell–Larcker and HTMT criteria provided further evidence. A 5-point Likert scale for all items (range 1 = strongly disagree to 5 = strongly agree) was utilized. A 5-point scale was chosen to obtain subtle variations in the response of the participants, avoid central tendency bias, and increase reliability. This technique has been highly advocated for technology adoption and SEM investigations for enhanced sensitivity of the scale (Preston and Colman, 2000).

Limitations and uncertainty · source passage

Higher education has experienced sustained digital transformation over the past two decades; however, the Coronavirus disease 2019 (COVID-19) pandemic marked a decisive acceleration in this process (Zawacki-Richter et al., 2020; Bond et al., 2020). The abrupt transition to remote and hybrid learning environments exposed both the potential and limitations of existing digital education tools, including learning management systems, massive open online courses (MOOCs), and video-conferencing platforms. Prior studies have observed that while these technologies expanded access and flexibility, they also amplified longstanding challenges related to student motivation, digital inequality, and institutional readiness for large-scale digital delivery (Dahri et al., 2025b). These challenges have intensified calls for more advanced, adaptive, and resilient educational technologies that support meaningful learning experiences beyond emergency remote instruction.

Design the system before the pilot

Document the learner model, mastery rule, hint sequence and adaptation signal. Compare adaptive routing with the same content in a non-adaptive sequence. Check whether early errors lead to persistent low-expectation pathways.

  • Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.
  • Co-design accommodations with learners and specialists. Check access and error burden across groups without inferring subgroup effectiveness from small or unreported samples.

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Blind-score reasoning and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Jehad Alqurni. Sustainable adoption of artificial intelligence and the Metaverse in higher education: an environmental, social, and governance–based analysis of pedagogical innovation and perceived student learning outcomes. Frontiers in Artificial Intelligence (2026-03-04). https://doi.org/10.3389/frai.2026.1738730

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.