AI evidence extraction

Systematised evidence mapping of generative artificial intelligence (GenAI) and digital divide phenomena in higher education

Rouba Jamal Eddine, Ergun Gide, Abdallah Al-Sabbagh · Discover Computing · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

8/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.1007/s10791-026-10044-w

Methodology & findings

Study design

Systematic evidence mapping following a three-stage search methodology aligned with PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews) standards.

Sample

N = 12, 2 groups

Primary method

Systematic evidence mapping methodology with three-stage search procedure. Qualitative synthesis and thematic analysis of included publications. No statistical pooling or quantitative synthesis performed. PRISMA-ScR alignment for scoping review standards.

Main result

The study found that "four key barriers to equitable adoption were identified: limited digital infrastructure and connectivity constraints, insufficient artificial intelligence literacy among stakeholders, economic barriers in terms of cost affordability and access costs, and the absence of inclusive, equity-focused institutional policies." Additionally, "within the limited corpus, regional disparities were emphasised as being particularly severe, with Global South case studies identifying fundamental infrastructural constraints as primary barriers to effective technology integration."

Reports effect sizes.

Research paradigm

Critical realism with social constructivist orientation

Author conclusions

"without targeted interventions to incorporate infrastructure development, comprehensive literacy programs, access provisions, and inclusive policy frameworks, GenAI deployment has the potential to exacerbate rather than alleviate existing educational inequities in the contexts studied." The review identifies this as requiring "systematic examination" and makes "review-derived recommendations to policymakers, higher education institutions, and practitioners to facilitate more equitable GenAI adoption in higher education environments."

Risk of bias

Selection bias (limited corpus of empirical work available in the 2023-2025 period), publication bias (likely favoring published studies over grey literature), potential geographic bias (representation of Global South vs Global North contexts unclear), and potential funding bias from Trust in AI Report 2025 source (KPMG involvement).; Selection bias potential due to small corpus of eligible studies (n=12); publication bias toward English-language and accessible publications; geographic bias favoring Global South case studies; time-bound search (2023-2025) may miss earlier foundational work; reliance on online-first articles introduces publication lag bias.; Publication bias (only twelve included publications identified in the search period); Geographic bias (Global South underrepresentation in GenAI-equity research); Temporal bias (nascent field with limited longitudinal studies); Selection bias (inclusion criteria may have excluded gray literature or non-English publications); Confounding: inability to isolate GenAI-specific effects from broader digital divide phenomena

Limitations

The authors note that "within the limited corpus, regional disparities were emphasised" and acknowledge that "contextual survey data from the Trust in AI Report also suggest gaps between confidence in GenAI uptake and institutional readiness in many jurisdictions, particularly in emerging economies, although these findings are not specific to the higher education sector." The study is limited by the small and growing body of empirical work available during the search period.

Open questions raised

The review identifies main gaps in current research and makes review-derived recommendations to policymakers, higher education institutions, and practitioners to facilitate more equitable GenAI adoption. Specific gaps include: need for more empirical studies on GenAI and digital equity; lack of infrastructure development research; insufficient evidence on literacy program effectiveness; limited policy analysis; and underrepresentation of Global South contexts in the empirical literature.
The review identifies main gaps in current research and makes review-derived recommendations to policymakers, higher education institutions, and practitioners to facilitate more equitable GenAI adoption in higher education environments. Specific gaps include insufficient empirical work on GenAI and digital equity, limited research on regional implementation contexts, and absence of longitudinal studies tracking GenAI adoption impacts.
The review identifies main gaps in current research regarding GenAI and digital equity in higher education, particularly: limited empirical work on regional disparities in Global South contexts, insufficient research on AI literacy development programs, gaps between institutional readiness and GenAI uptake confidence, and scarcity of longitudinal studies examining equitable adoption outcomes.

Data: not_statedCode: not_statedExtracted from: pdfAgreement 57%

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