Enhancing Organizational Sensemaking and Evidence-Based Decision-Making: An Explainable AI (XAI) Approach to Higher Education Graduate Employability

Aim/Purpose: The use of machine learning models in graduate employability prediction is constrained by limited transparency. This limitation weakens their value for organizational decision-making in higher education. Background: An Explainable Artificial Intelligence (XAI) framework is proposed to fill this void. Using this framework, the predictive output generated will be translated into insights that are interpretable and useful for sensemaking and evidence-based management. Methodology: The experiments were conducted on a database containing 225,384 graduate records. The best accuracy results were obtained using the CatBoost algorithm, and global and local explanations were generated using SHAP and ICE. Contribution: Predictive accuracy is integrated with interpretability in this study. A framework is produced as a result, designed to support accountable, data-driven decision-making processes within higher education institutions. Findings: Education level and language proficiency are identified as dominant predictors of employability outcomes. Substantial influence is also exerted by GPA, university ranking, and internship experience, while demographic attributes such as gender make a negligible contribution. Both aggregate feature importance and heterogeneous individual effects are exposed through the explainability analysis. Recommendations for Practitioners: Institutions should prioritize language development, targeted academic support, and expanded internship pathways. Interpretable analytics should also be embedded in curriculum planning and policymaking. Recommendation for Researchers: Explainable AI should be examined within broader educational landscapes in future research. Additional variables should be incorporated, with particular attention directed toward fairness, bias, and model robustness. Impact on Society: The results indicate potential for more equitable and effective policy making. They also align with broader objectives related to workforce readiness and Sustainable Development Goals on education and employment. Future Research: Future work should rely on longitudinal and multi-institutional data. The practical impact of explainable AI in real decision-support environments also requires systematic evaluation.