Evidence map›Paper›PMID 41858844›Full record

ArticleFrontiers in artificial intelligence2026

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.

Jehad Alqurni

Abstract read
In one paragraph

Article in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Jehad AlqurniDepartment of Educational Technologies, College of Education, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

artificial intelligencedigital pedagogical innovationESG frameworkhigher educationMetaversestudent learning outcomessustainable adoption

Identifiers

PMID41858844
PMCPMC12996163

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.