Barriers to AI adoption for women in higher education: a systematic review of the Asian context - Usama Kalim; Asha Kanwar; Jiena Sha; Ronghuai Huang (Smart Learning Environments)
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.