SHELF: A Gender-Sensitive Hybrid AI Framework for Advancing Educational Equity in Underserved Regions
AI-based recommendation systems frequently fail to serve women in low-resource educational environments effectively, as they neglect critical realities, such as time constraints, limited connectivity, and socio-cultural barriers. This paper proposes a hybrid ensemble AI framework that directly addresses these contextual realities by integrating algorithmic fairness, gender-aware personalization, and low-bandwidth adaptability into a unified, women-focused framework.
Although AI-driven personalized learning has advanced rapidly, existing systems lack gender sensitivity, transparency, and contextual adaptability, limiting their effectiveness in promoting inclusive education.
A Systematic Literature Review (SLR) of 50 peer-reviewed studies (2015-2025) was conducted to analyze research trends in AI-powered recommender systems, gender-focused education, emerging technologies, and explainable AI in educational contexts.
The key addition of the study is a conceptual ensemble hybrid AI framework, namely Sensitive Hybrid Ensemble for Learning Fairness (SHELF), which provides a comprehensive framework for simultaneously incorporating gender-sensitive learner profiling, bias-conscious recommendation, and low-bandwidth adaptability. Based on a systematic review of 50 studies, the framework provides an evidence-based solution to a critical gap in gender-blind AI-driven educational systems in underserved areas.
Results show that 48% of studies focus on AI-based recommender systems, 19% on adaptive and fair conceptual models, 11% on gender-specific education, and 12% on emerging technologies such as IoT and edge computing. Systematic reviews and meta-analyses of educational recommendation systems account for 7% of studies, while only 3% address transparency and explainability, highlighting a major research gap.
Educational stakeholders should adopt gender-aware, fair, and explainable AI systems to provide personalized learning and career guidance for women learners.
Future research should emphasize inclusive AI design, bias mitigation, contextual adaptability, and explainability in educational systems.
Gender-aware AI in education can enhance women’s access to learning, reduce digital inequality, and support inclusive societal development.
Further work should validate hybrid AI frameworks across diverse educational contexts and assess long-term impacts on women learners.
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