As the educational landscape evolves, understanding and fostering student adaptability has become increasingly critical. This study presents a comparative analysis of XAI techniques to interpret machine learning models aimed at classifying student adaptability levels. Leveraging a robust dataset of 1205 instances, we employed several machine learning algorithms with a particular focus on Random Forest, which demonstrated highest accuracy at 91%. The models’ precision, recall and F1-score were also evaluated, with Random Forest achieving a precision of 0.93, a recall of 0.94, and an F1-score of 0.94. Our study utilizes SHAP, LIME, Anchors, ALE, and Counterfactual explanations to reveal the specific contributions of various features impacting adaptability predictions. SHAP values highlighted ‘Class Duration’ significance (mean SHAP value: 0.175); LIME explained socio-economic and institutional factors’ intricate influence. Anchors provided high-confidence rule-based explanations (confidence: 97.32%), emphasizing demographic characteristics. ALE analysis underscored the importance of ‘Financial Condition’ with a positive slope, while Counterfactual scenarios highlighted the impact of slight feature variations of 0.5 change in ‘Class Duration’. Consistently, ‘Class Duration’ and ‘Financial Condition’ emerge as key factors, while the study also underscores the subtle effects of ‘Institution Type’ and ‘Load-shedding’. This multi-faceted interpretability approach bridges the gap between machine learning performance and educational relevance, presenting a model that not only predicts but also explains the dynamic factors influencing student adaptability. The synthesized insights advocate for educational policies accommodating socioeconomic factors, instructional time, and infrastructure stability to enhance student adaptability. The implications extend to informed and personalized educational interventions, fostering an adaptable learning environment. This methodical research contributes to responsible AI application in education, promoting predictive and interpretable models for equitable and effective educational strategies.

Depression among university students is a critical global problem that affects academic performance, well-being, and dropout rates. Although machine learning models offer strong predictive power, their opacity limits their adoption in educational and clinical contexts. Most existing approaches emphasize either global or individual explanations, leaving a gap in multi-level interpretability that can inform institutional policy and personalized support. This paper introduces a novel multi-level explainable AI (XAI) framework that unifies SHAP for global importance ranking, H-LIME for hierarchical subgroup explanations, and counterfactual reasoning for actionable individual recourse. By establishing a generalizable theory of hierarchical interpretability that bridges global, group, and local levels of explanation, the framework advances both methodological rigor and practical relevance. Using a dataset of 27,901 students with demographic, academic, lifestyle and psychosocial factors, Random Forest achieved a strong predictive performance (accuracy = 0.839, ROC–AUC = 0.916). The results consistently identified suicidal ideation, academic pressure, and financial stress as dominant factors, while subgroup and individual analyses revealed contextual and personalized nuances that global explanations alone obscure. Individual-level counterfactuals further suggested feasible workload or lifestyle adjustments, offering practical recourse for at-risk students. Limitations include dependence on self-reported survey data and the fact that the sample is from a single country, which may limit the generalizability of the findings. This unified approach offers a reusable methodological framework for achieving multi-level interpretability in sensitive AI applications. It also provides evidence-based guidance for the early identification of at-risk individuals, customized interventions, and the development of equity-based institutional policies.

The rapid advancement of ICT, robotics, data science, and cyber-physical systems (CPS) has intensified the need for agile, future-ready human resource development (HRD) strategies. This scoping review explores current HRD practices, challenges, and opportunities across these technological domains. Guided by the PRISMA-ScR framework, 791 records were initially retrieved from four databases, with 43 peer-reviewed publications ultimately included following a rigorous screening process. Thematic analysis revealed a strong emphasis on digital and AI literacy, interdisciplinary training, and the adoption of immersive technologies such as virtual reality and AI-driven competency tracking systems. Persistent gaps, including misalignment between academic training and industry needs, a lack of scalable HRD models, and underdeveloped policy frameworks. Statistical comparisons revealed no significant differences across fields, indicating the cross-cutting relevance of the identified strategies. The findings underscore the need for structured, empirically grounded, and industry-integrated HRD approaches to prepare the workforce for the demands of the Fourth Industrial Revolution.

Large language models (LLMs) such as ChatGPT, Codex, and GitHub Copilot are transforming programming education by offering personalized code generation, real-time explanations, and virtual tutoring. Despite rapid adoption, their pedagogical effectiveness and limitations remain underexplored. This systematic review synthesizes findings from 25 empirical studies published between January 2022 and May 2025 to evaluate the educational settings, benefits, challenges, and learning impacts associated with LLM use in programming instruction. Our analysis reveals that LLMs are predominantly used in university-level introductory courses, with tutoring and explanation being the most common use case. Reported benefits include enhanced student engagement, improved short-term performance, and reduced instructor workload. However, challenges such as overreliance, code inaccuracy, equity gaps, and academic integrity concerns persist. While most studies report positive short-term outcomes, long-term skill development and equitable access remain uncertain. The evidence suggests that the pedagogical value of LLMs depends on thoughtful integration strategies that combine automation with human oversight. This review provides actionable insights for educators and policymakers seeking to leverage LLMs responsibly in diverse programming education contexts.

As the educational landscape evolves, understanding and fostering student adaptability has become increasingly critical. This study presents a comparative analysis of (XAI) techniques to interpret machine learning models aimed at classifying student adaptability levels. Leveraging a robust dataset, we employed several machine learning algorithms with a particular focus on Random Forest, which demonstrated a 91% accuracy. Our study utilizes (SHAP), (LIME), Anchors, (ALE), and counterfactual explanations to reveal the specific contributions of various features impacting adaptability predictions. Consistently, 'Class Duration' and 'Financial Condition' emerge as key factors, while the study also underscores the subtle effects of 'Institution Type' and 'Load-shedding'. This multi-faceted interpretability approach bridges the gap between machine learning performance and educational relevance, presenting a model that not only predicts but also explains the dynamic factors influencing student adaptability. The synthesized insights advocate for educational policies accommodating socioeconomic factors, instructional time, and infrastructure stability to enhance student adaptability. The implications extend to informed and personalized educational interventions, fostering an adaptable learning environment. This methodical research contributes to responsible AI application in education, promoting predictive and interpretable models for equitable and effective educational strategies.