Explainable Artificial Intelligence and Social Theory Integration for Advancing Educational Equity in Nepal | Chapter 2| Mathematics and Computer Science: Research Updates Vol. 8

 

This chapter examines entrenched socioeconomic disparities in Nepal’s education system through the integration of explainable artificial intelligence (XAI) and foundational social theories of equity. While Nepal has made progress in enrollment, persistent gaps in access, retention, and learning outcomes remain among groups marginalized by caste, gender, and geography. Existing policy analyses often rely on linear statistics or descriptive methods and lack operational links to sociological theory. To address this lacuna, we develop a mixed-methods framework that blends predictive machine learning with interpretability (SHAP) and qualitative inquiry to ground algorithmic findings in lived experience. Using national-level datasets — notably the Education Management Information System (EMIS) and the Nepal Living Standards Survey (NLSS)—we operationalize a Capability Index and train ensemble models (Random Forest and XGBoost) to predict capability deprivation and dropout risk. SHapley Additive exPlanations (SHAP) are applied to attribute model outputs to observable socioeconomic and school-level features. We formalize the predictive problem and its interpretability as follows: given feature set X = {x1, . . . , xn} and an outcome Y (capability index or dropout probability), we estimate \(\hat{Y}\) = f(X; θ) and decompose \(\hat{Y}\) additively into baseline and feature contributions \(\hat{Y}\) = ϕ0 +\(\Sigma\)i ϕi. This decomposition informs policy levers by quantifying marginal contributions of poverty, distance to school, caste status, and school resources. Beyond technical contributions, the chapter situates model outputs within Sen’s Capability Approach and Bourdieu’s Cultural Capital Theory to interpret how structural constraints and cultural resources shape educational opportunity. Deliverables include a resource allocation framework, SHAP-driven simulation dashboards for policymaking, and early-warning indicators for dropout prevention. Qualitative interviews with educators and community stakeholders are used to validate and contextualize the quantitative results. Together, these elements advance both theory and practice: they demonstrate how XAI can produce socially meaningful, policy-ready evidence for more equitable education in Nepal and similar low- and middle-income contexts.

 

Author(s) Details

Anmol Adhikari
Department of Computer Science, Noida International University, India.

 

Vivek Kumar Sinha
Department of Computer Science and Engineering, Noida International University, India.

 

Please see the book here :- https://doi.org/10.9734/bpi/mcsru/v8/6555