Generative AI enables personalized computer science education at scale, yet questions remain about whether such personalization supports or undermines learning. This scoping review synthesizes 32 studies (2023–2025) purposively sampled from 259 records to map personalization mechanisms and effectiveness signals in higher-education CS contexts. We identify five application domains—intelligent tutoring, personalized materials, formative feedback, AI-augmented assessment, and code review—and analyze how design choices shape learning outcomes. Designs incorporating explanation-first guidance, solution withholding, graduated hint ladders, and artifact grounding (student code, tests, and rubrics) consistently show more positive learning processes than unconstrained chat interfaces. Successful implementations share four patterns: context-aware tutoring anchored in student artifacts, multi-level hint structures requiring reflection, composition with traditional CS infrastructure (autograders and rubrics), and human-in-the-loop quality assurance. We propose an exploration-firstadoption framework emphasizing piloting, instrumentation, learning-preserving defaults, and evidence-based scaling. Four recurrent risks—academic integrity, privacy, bias and equity, and over-reliance—are paired with operational mitigation. Critical evidence gaps include longitudinal effects on skill retention, comparative evaluations of guardrail designs, equity impacts at scale, and standardized replication metrics. The evidence supports generative AI as a mechanism for precision scaffolding when embedded in exploration-first, audit-ready workflows that preserve productive struggle while scaling personalized support.

Real-world decision-making often involves uncertainty, incomplete data, and the need to evaluate alternatives based on both quantitative and qualitative criteria. To address these challenges, this study presents FAS-XAI, a unified methodological framework that integrates fuzzy clustering and explainable artificial intelligence (XAI). FAS-XAI supports interpretable, data-driven decision-making by combining three key components: fuzzy clustering to uncover latent behavioral profiles under ambiguity, supervised prediction models to estimate decision outcomes, and expert-guided interpretation to contextualize results and enhance transparency. The framework ensures both global and local interpretability through SHAP, LIME, and ELI5, placing human reasoning and transparency at the center of intelligent decision systems. To demonstrate its applicability, FAS-XAI is applied to a real-world B2B customer service dataset from a global ERP software distributor. Customer engagement is modeled using the RFID approach (Recency, Frequency, Importance, Duration), with Fuzzy C-Means employed to identify overlapping customer profiles and XGBoost models predicting attrition risk with explainable outputs. This case study illustrates the coherence, interpretability, and operational value of the FAS-XAI methodology in managing customer relationships and supporting strategic decision-making. Finally, the study reflects additional applications across education, physics, and industry, positioning FAS-XAI as a general-purpose, human-centered framework for transparent, explainable, and adaptive decision-making across domains.

Objectives: Traffic accidents cause severe social and economic impacts, demanding fast and reliable detection to minimize secondary collisions and improve emergency response. However, existing cloud-dependent detection systems often suffer from high latency and limited scalability, motivating the need for an edge-centric and consensus-free accident detection framework in IoV environments. Methods: This study presents a real-time accident detection framework tailored for Internet of Vehicles (IoV) environments. The proposed system forms an integrated IoV architecture combining on-vehicle inference, RSU-based validation, and asynchronous cloud reporting. The system integrates a lightweight ensemble of Vision Transformer (ViT) and EfficientNet models deployed on vehicle nodes to classify video frames. Accident alerts are generated only when both models agree (vehicle-level ensemble consensus), ensuring high precision. These alerts are transmitted to nearby Road Side Units (RSUs), which validate the events and broadcast safety messages without requiring inter-vehicle or inter-RSU consensus. Structured reports are also forwarded asynchronously to the cloud for long-term model retraining and risk analysis. Results: Evaluated on the CarCrash and CADP datasets, the framework achieves an F1-score of 0.96 with average decision latency below 60 ms, corresponding to an overall accuracy of 98.65% and demonstrating measurable improvement over single-model baselines. Conclusions: By combining on-vehicle inference, edge-based validation, and optional cloud integration, the proposed architecture offers both immediate responsiveness and adaptability, contrasting with traditional cloud-dependent approaches.

Occupational safety in high-risk sectors, such as mining, depends heavily on understanding and predicting workers’ behavioural risks. However, existing approaches often overlook the psychological dimension of safety, particularly how psychological-contract violations (PCV) between miners and their organizations contribute to unsafe behavior, and they rarely leverage interpretable artificial intelligence. This study bridges that gap by developing an explainable AutoML framework that integrates AutoGluon, SHAP, and LIME to classify miners’ safety behaviors using psychological and organizational indicators. An empirically calibrated synthetic dataset of 5000 miner profiles (20 features) was used to train multiclass (Safe, Moderate, and Unsafe) and binary (Safe and Unsafe) classifiers. The WeightedEnsemble_L2 model achieved the best performance, with 97.6% accuracy (multiclass) and 98.3% accuracy (binary). Across tasks, Post-Intervention Score, Fatigue Level, and Supervisor Support consistently emerge as high-impact features. SHAP summarizes global importance patterns, while LIME provides per-case rationale, enabling auditable, actionable guidance for safety managers. We outline ethics and deployment considerations (human-in-the-loop review, transparency, bias checks) and discuss transfer to real-world logs as future work. Results suggest that interpretable AutoML can bridge behavioural safety theory and operational decision-making by producing high-accuracy predictions with transparent attributions, informing targeted interventions to reduce unsafe behaviours in high-risk mining contexts.

This self-study presents Persona, Break Glass, Name Plan, Jam (PBNJ), a human-centered workflow for using generative AI to support differentiated lesson planning and problem solving. Although differentiated instruction (DI) is widely endorsed, early-career teachers often lack the time and capacity to implement it consistently. Through four iterative cycles of collaborative self-study, seven educator-researchers examined how they used AI for lesson planning, identified key challenges, and refined their approach. When engaged, the PBNJ sequence—set a persona, use a ‘break glass’ starter prompt, name a preliminary plan, and iteratively ‘jam’ with the AI—improved teacher confidence, yielded more feasible lesson plans, and supported professional learning. We discuss implications for problem solving beyond educational contexts and the potential for use with young learners.

Artificial intelligence is reshaping healthcare project management in Saudi Arabia, yet most deployments lack culturally grounded ethics. This paper synthesises global AI-ethics guidance and Islamic bioethics, then proposes a maqāṣid-al-sharīʿah-aligned conceptual framework for ANN-based decision support. Ethical signals derived from the preservation of life, dignity, justice, faith, and intellect are embedded as logic-gate filters on ANN outputs. The framework specifies a dual-metric evaluation that reports predictive performance (e.g., accuracy, MAE, AUC) alongside ethical compliance, with auditable thresholds for fairness (δ = 0.1) and confidence (α = 0.8) calibrated through stakeholder workshops. It incorporates a co-design protocol with clinicians, patients, Islamic scholars, and policymakers to ensure cultural and clinical legitimacy. Unlike UNESCO and EU frameworks, which remain principle-oriented, this study introduces a measurable dual-layer assessment that combines technical accuracy with ethical compliance, supported by audit artefacts such as model cards, traceability logs, and human override records. The framework yields technically efficient and Shariah-compliant recommendations and sets a roadmap for empirical pilots under Vision 2030. The paper moves beyond a general review by formalising an Islamic-values-driven conceptual framework that operationalises ethical constraints inside ANN–DSS pipelines and defines auditable compliance metrics. This paper combines a critical review of AI in healthcare project management with the development of a maqāṣid-aligned conceptual framework, thereby bridging systematic synthesis with an implementable proposal for ethical AI.

Brain tumors are abnormal tissue growth characterized by uncontrolled and rapid cell proliferation. Early detection of brain tumors is critical for improving patient outcomes, and magnetic resonance imaging (MRI) has become the most widely used modality for diagnosis due to its superior image quality and non-invasive nature. Deep learning, a subset of artificial intelligence, has revolutionized automated medical image analysis by enabling highly accurate and efficient classification tasks. The objective of this study is to develop a robust and effective brain tumor detection system using MRI images through transfer learning. A diagnostic framework is constructed based on convolutional neural networks (CNN), integrating both a custom sequential CNN model and pretrained architectures, namely VGG16 and EfficientNetB4, trained on the ImageNet dataset. Prior to model training, image preprocessing techniques are applied to enhance feature extraction and overall model performance. This research addresses the common challenge of limited MRI datasets by combining EfficientNetB4 with targeted preprocessing, data augmentation, and an appropriate optimizer selection strategy. The proposed methodology significantly reduces overfitting, improves classification accuracy on small datasets, and remains computationally efficient. Unlike previous studies that focus solely on CNN or VGG16 architectures, this work systematically compares multiple transfer learning models and demonstrates the superiority of EfficientNetB4. Experimental results on the Br35H dataset show that EfficientNetB4, combined with the ADAM optimizer, achieves outstanding performance with an accuracy of 99.66%, precision of 99.68%, and an F1-score of 100%. The findings confirm that integrating EfficientNetB4 with dataset-specific preprocessing and transfer learning provides a highly accurate and cost-effective solution for brain tumor classification, facilitating rapid and reliable medical diagnosis.

This paper examines how Generative AI (GenAI) reshapes digital inclusion in e-government. We develop the E-Government Divide Measurement Indicator (EGDMI) across three dimensions: D1—Breadth of the Divide (foundational access, affordability, and basic skills), D2—Sectoral/Specific Divide (actual use, experience, and trust in e-government), and D3—GenAI Gap (access, task use, and competence). The index architecture specifies indicator lists, sources, units, transformations, uniform normalization, and a documented weighting strategy with sensitivity and basic uncertainty checks. Using official statistics and qualitative evidence for Serbia, we report D1 and D2 as composite indices and treat D3 as an exploratory, non-aggregated layer given current data maturity. Results show strong foundational readiness (D1 = 73.6) but very low e-government uptake (D2 = 19.9), indicating a shift of the divide from access to meaningful use, usability, and trust. GenAI capabilities are emergent and uneven (D3 sub-dimensions: access 47.8; task use 39.4; competence/verification 43.6). Cluster analysis identifies four user profiles—from “Digitally Excluded” to “GenAI-Augmented Citizens”— that support differentiated interventions. The initial hypothesis—that GenAI can widen disparities in the short run—receives partial confirmation: GenAI may lower interaction costs but raises verification and ethics thresholds for vulnerable groups. We outline a policy roadmap prioritizing human-centered service redesign, transparency, and GenAI literacy before automation, and provide reporting templates to support comparable monitoring and cross-country learning.

In capital-intensive industries, operational knowledge is often trapped in unstructured technical manuals, creating a barrier to efficient and reliable maintenance planning. This work addresses the need for an integrated system that can automate knowledge extraction and generate optimized, resilient, operational plans. A synergistic multi-agent framework is introduced that transforms unstructured documents into a structured knowledge base using a self-validating pipeline. This validated knowledge feeds a scheduling engine that combines multi-objective optimization with discrete-event simulation to generate robust, capacity-aware plans. The framework was validated on a complex maritime case study. The system successfully constructed a high-fidelity knowledge base from unstructured manuals and the scheduling engine produced a viable, capacity-aware operational plan for 118 interventions. The optimized plan respected all daily (6) and weekly (28) task limits, executing 64 tasks on their nominal date, bringing 8 forward, and deferring 46 by an average of only 2.0 days (95th percentile 4.8 days) to smooth the workload and avoid bottlenecks. An interactive user interface with a chatbot and planning calendar provides verifiable “plan-to-page” traceability, demonstrating a novel, end-to-end synthesis of document intelligence, agentic AI, and simulation to unlock strategic value from legacy documentation in high-stakes environments.

Background: The accelerating global population aging underscores the urgency of addressing public health challenges. Sarcopenia and depression are prevalent, interrelated conditions in older adults, yet prevailing research often treats depression as a static state, neglecting its longitudinal progression and limiting predictive capability for sarcopenia. Methods: Using data from four waves (2011–2018) of the China Health and Retirement Longitudinal Study (CHARLS), we identified distinct depressive symptom trajectories via Group-Based Trajectory Modeling. Seven machine learning algorithms were employed to develop predictive models for sarcopenia risk, incorporating these trajectory patterns and baseline characteristics. Results: Three depressive symptom trajectories were identified: ‘Persistently Low’, ‘Persistently Moderate’, and ‘Persistently High’. Tree-based ensemble methods, particularly Random Forest and XGBoost, demonstrated superior and robust performance (mean accuracy: 0.8265 and 0.8178; mean weighted F1-score: 0.8075 and 0.8084, respectively). Feature importance analysis confirmed depressive symptoms as a core, independent predictor, ranking third (5.7% importance) in the optimal Random Forest model, only after BMI and cognitive function, and surpassing traditional risk factors like age and waist circumference. Conclusions: This study validates that longitudinal depressive symptom trajectories provide superior predictive power for sarcopenia risk compared to single-time-point assessments, effectively mapping mental health trajectories to physical risk. The robust ML framework not only enables early identification of high-risk individuals but also reveals a multidimensional risk profile, highlighting the intricate mind–body connection in aging. These findings advocate for integrating dynamic mental health monitoring into routine geriatric assessments, demonstrating the potential of AI to facilitate a paradigm shift towards proactive, personalized, and scalable prevention strategies in public health and clinical practice.