explainable ai

A lightweight and explainable conversational AI framework for natural language SQL learning without large language models

Maya Node A Consent-and-Control Design Approach for AI Systems

AI systems are often evaluated for what they can produce, but not with equal discipline for what they are allowed to do, whose authority they are acting under, when that authority expires, or how a person can stop and correct them. Maya Node is a consent-and-control design approach that treats those questions as first-...

Impact of AI on Students' Performance in Economics in Kaduna Metropolis Secondary Schools

This study investigated the effect of Artificial Intelligence (AI) tools on the academic performance of Senior Secondary School students in Economics in selected schools within Kaduna Metropolis, Kaduna State, Nigeria. Adopting a descriptive correlational survey design, data were collected from five hundred (500) Senio...

Effectiveness of AI-Generated Study Materials versus Traditional Teaching Methods in Higher Education

The increasing integration of artificial intelligence (AI) in higher education has significantly trans-formed the creation and dissemination of study materials. While AI-generated content offers advantages such as accessibility, scalability, and instant feedback, concerns remain regarding its depth, contex-tual relevan...

Dataset for: Explainable AI in │ │ Title │ Medicine — A Systematic Literature Mapping Study

This repository contains the extraction dataset, the explanation-evaluation reclassification, and the association analysis supporting the systematic mapping study of explainable artificial intelligence (XAI) in medicine (383 studies from Q1/Q2-indexed journals, 2020–2025).

Accessibility-Aware Adaptive AI Interfaces for Intelligent Web Applications: A Human-in-the-Loop Framework

Intelligent web applications increasingly personalize content, navigation and task support through artificial intelligence. However, many adaptive interfaces are optimized for engagement or efficiency rather than accessibility, which can create new barriers for users with visual, motor, cognitive or situational limitat...

The Missing "A" in ELSI: Why Artificial Intelligence Requires an Aesthetic Dimension

Artificial intelligence has intensified ethical, legal, and social inquiry, but generative systems expose a domain-specific limit in the conventional ELSI framework. This article argues that when AI operates as a cultural producer or perceptual mediator, aesthetics constitutes an analytically distinct dimension. AI-gen...

Preserving Professional Skepticism in the Age of Algorithmic Auditing: A Conceptual Framework for AI Adoption in Accounting Practice

Generative artificial intelligence (AI) has moved beyond the transactional automation that characterized earlier accounting technologies such as robotic process automation and rule-based expert systems. Large language models and machine-learning-based analytics can now draft audit memoranda, flag anomalies in real time...

Accessibility-Aware Adaptive AI Interfaces for Intelligent Web Applications: A Human-in-the-Loop Framework

Intelligent web applications increasingly personalize content, navigation and task support through artificial intelligence. However, many adaptive interfaces are optimized for engagement or efficiency rather than accessibility, which can create new barriers for users with visual, motor, cognitive or situational limitat...

Machine learning integrated explainable artificial intelligence in predicting toxicities of enfortumab vedotin in urothelial carcinoma: an exploratory study.

Preliminary, hypothesis-generating findings suggest ML may predict EV-related toxicities, but single train-test split, small event counts, and lack of external validation preclude clinical use.

Artificial Intelligence in Public Health: Optimizing Clinical Triage and Preventing Infectious Disease Outbreaks

Artificial intelligence (AI) is increasingly transforming public health from a predominantlyreactive model of treating established disease towards proactive detection, prediction, andprevention. Within hospitals, natural language processing and machine learning can analyseelectronic health records, clinical observation...

Segmentation Guided Explainable Deep Learning for Multi-Class Skin Disease Detection

This research centers on the creation of a "Segmentation-Guided" Explainable Deep Learning system, built specifically to sharpen the accuracy of skin disease detection and fix the "trust gap" currently plaguing digital dermatology. To move past the limitations of typical "black-box" models—where the inner logic is hidd...