explainable ai

AI-Based Learning Analytics and Real-Time Monitoring for IoT-Enabled Smart Laboratory of Engineering Education

It is demonstrated that multimodal sensing with explainable AI improves operational visibility and educational analytics and the smart lab framework is proposed, supporting safer, more efficient, and data-driven learning across engineering disciplines.

Comparative Analysis of Artificial Intelligence Techniques for Cardiovascular Disease Diagnosis and Risk Prediction

Evidence is provided that ensemble-based frameworks currently offer the most effective balance between predictive accuracy, robustness, and clinical feasibility, and future research should emphasize multi-center external validation and explainable AI frameworks.

Generative Artificial Intelligence in South American newsrooms: mapping adoption and uses (2023–2025)

The study presents one of the first regional mappings of the adoption of generative AI in South American journalism and proposes an analytical model that explains how the technology is being institutionalized in news organizations within the South American context.

Data-driven dissection of heterogeneity: a computational framework for identifying depression subtypes through multi-omics and explainable AI

Influence of Artificial Intelligence-Driven Administrative Practices on Principals’ Effectiveness in Public Senior Secondary Schools in Bauchi State, Nigeria

This study investigates the influence of Artificial Intelligence-driven administrative practices on principals’ effectiveness in public senior secondary schools in Bauchi State, Nigeria and provides empirical evidence on the role of emerging AI technologies in improving principals’ administrative effectiveness.

From Predictive Accuracy to Human-Centric Decision Support: An Operational HCT-ML Framework for Intelligent Transportation Systems

The Human-Centric and Trustworthy Machine Learning (HCT-ML) framework is developed as an operational decision-support profile for ITS and provides an evidence-informed and reproducible protocol that can be tested through stakeholder studies, simulations, and field deployments.

EXPLAINABLE ARTIFICIAL INTELLIGENCE FOR RELIABLE DECISION-MAKING IN AUTOMATED SYSTEMS

The study suggests that explainable AI is a crucial factor in building intelligent, reliable, accountable, transparent, and effective AI-powered systems that can be deployed in realistic environments for decision making.

Explaining the AI–journalist credibility gap: generalized and source-specific judgments of news credibility

The Psychology of AI Dependency: A Conceptual Analysis of Cognitive Offloading and Human Decision-Making in the Generative AI Era

The widespread adoption of generative artificial intelligence (AI) has significantly transformed the way people acquire knowledge, solve problems, and make decisions in both academic and professional settings. While these technologies improve efficiency and accessibility, their growing use has raised important psycholo...

From Explainability to Actionability: a Tiered Adaptable Multi-Agent Framework with Agent Reasoning Tools for Collaborative Failure Recovery

This research introduces a tiered multi-agent architecture grounded in Human-Centered eXplainable AI principles that contributes an adaptable and generalizable framework and foundational artifacts for trustworthy AI teammates.

An Integrated AI-Based Crop Prediction and Advisory Platform for Precision Agriculture

SmartCrop AI is proposed as a practical digital platform that brings machine-learning based crop and yield prediction, soil-suitability assessment, an AI advisory component, simulated IoT data, mandi-price information and farmer-oriented reporting.

Exploring generalizability and explainability of LLMs in classifying clinically rated suicidal ideation using heterogeneous data

Results showed that topic-specific classifiers reduced subgroup disparity compared to the topic-general classifier, and a factorization approach to decompose complex data into simpler components by reducing topic dimensions of clinical transcripts was proposed.