CoChat
Sign Up
Cited by 22Artificial Intelligence in EducationGender and Technology in EducationEthics and Social Impacts of AI

Barriers to AI adoption for women in higher education: a systematic review of the Asian context

Open access
Kalim, Usama, Kanwar, Asha, Sha, Jiena, Huang, Ronghuai·2025·Smart Learning Environments
DOI 10.1186/s40561-025-00390-5
Open this paper in CoChat — free accountOpen PDF

Abstract

Artificial Intelligence (AI) is transforming higher education rapidly by enabling personalized learning, enhancing administrative processes, and improving access to educational resources. However, disparities in AI adoption, particularly among women in the Asian context, raise concerns about equity, inclusivity, and access. This disparity could lead to a deficit in AI skills among women, affecting their ability to contribute as effectively as men in the future. Therefore, it is necessary to understand the current state of women's adoption of AI and the barriers they face in Asian higher education. The systematic review has been conducted using PRISMA guidelines. This review paper synthesizes the findings from the studies conducted in various contexts of Asia to present an overall picture of the state of AI adoption among women in Asia. A total of 17 studies were selected for this review, highlighting socio-cultural barriers, lack of trust, technological unawareness, biases in AI algorithms, and inadequate representation of women in AI policy formulation. Besides highlighting these barriers, the results also shed light on recommendations given by earlier studies that facilitate and encourage women to adopt AI in higher education. Based on the Asian perspective, the conclusion proposes specific recommendations for policymakers and practitioners to promote inclusive AI that empowers women in Asia to contribute more effectively to higher education.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/s40561-025-00390-5

Cites

37 of 37 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Inequities in AI writing tool adoption stem from limited digital infrastructure, policy gaps, English-centric tool design, and socio-cultural barriers.

    “These inequities stem from limited digital infrastructure, policy gaps, English-centric tool design, and socio-cultural barriers (Chan, 2023; Gustilo et al., 2024; Kalim et al., 2025).”

    From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Kalim et al., 2025

    The Discussion and conclusion (page not specified) identifies unequal access to technological tools, exclusion from AI policy formation, and cultural constraints as barriers to women’s AI adoption in Asian higher education. It does not establish English-centric design as a barrier or specifically address inequities in AI writing-tool adoption; access to tools is also narrower than a claim about digital infrastructure. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 1

  1. e37569 · DOI · Paper page

Cited by

Sorted by citations

Related

Authors
Kalim, U., Kanwar, A., Sha, J., & Huang, R.
Affiliations
Beijing Normal University (CN)
Corresponding author
Kalim, Usama
ORCID
Venue
Smart Learning Environments
Publisher
Springer Science and Business Media LLC
Published
2025-06-05
Volume
12
Issue
1
ISSN
2196-7091 (ISSN-L)
DOI
10.1186/s40561-025-00390-5
OpenAlex
W4411072798
Cited by
22
References
36
Citations by year
2026: 15 · 2025: 7
FWCI
3.38
Type
journal article
Language
en
Open access
Licence
Locations
  • Smart Learning Environments (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Social Sciences › Gender Studies › Gender and Technology in Education
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
Keywords
artificial intelligence adoption, women in higher education, gender disparities, AI adoption barriers, socio-cultural barriers, trust in AI, algorithmic bias, inclusive AI, Asian higher education

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review