explainable ai

A multidimensional analysis of generative AI adoption in academia: synthesizing TAM3 and UTAUT2 frameworks to predict students’ attitudes and learning effectiveness

The rapid integration of generative artificial intelligence (GenAI) tools into higher education raises important questions regarding their impact on student learning. Based on established technology acceptance frameworks, this study develops and empirically tests an integrative model that explains students’ attitudes t...

Artificial Intelligence, Machine Learning, and Deep Learning Approaches for Attention-Deficit/Hyperactivity Disorder Diagnosis: Protocol for a Scoping Review

Abstract Background Attention-deficit/hyperactivity disorder (ADHD) affects over 366 million adults and 139 million children worldwide, yet diagnosis remains fundamentally subjective, relying on clinical interviews, behavioral observations, and rating scales that yield inconsistent results across practitioners and sett...

Shadow AI, Human Oversight, and Institutional Trust: A Socio-technical Model of Responsible AI Governance in the Public Sector

When AI translations fall flat: embodied simulation and effort in cross-language reading

Common machine translation evaluation metrics, such as the Bilingual Evaluation Understudy (BLEU) and Crosslingual Optimized Metric for Evaluation of Translation (COMET) are reliable measures of propositional fidelity and surface fluency but systematically ignore the embodied texture of reading, leaving unexplained why...

Effect of Artificial Intelligence Tools on Teaching Effectiveness of Geometry in Tertiary Institutions in Benue State

This study examined the effect of Artificial Intelligence (AI) tools on teaching effectiveness of geometry in tertiary institutions in Benue State, Nigeria. A cross-sectional survey design was adopted. The study involved 235 respondents comprising 60 mathematics lecturers and 175 undergraduate students selected from st...

Multidimensional analysis of e-scooter crash severity using cluster correspondence and explainable AI

The rapid expansion of electric scooters (e-scooters) in urban transportation networks has transformed personal mobility across U.S. cities, yet rising crash rates have raised critical safety concerns. This study investigates the factors contributing to e-scooter crash severity through a novel integration of Cluster Co...

A tripartite framework for nursing artificial intelligence ethics: A scoping review.

BACKGROUND As artificial intelligence (AI) rapidly integrates into nursing practice, education, and research, it presents a profound ethical paradox: significant opportunities for operational efficiency alongside deep-seated challenges to the profession's core values. PURPOSE This scoping review synthesizes existing...

AI and promote carbon reduction: exploring an optimal deep learning model to predict CO2 emissions under technological innovations and green finance

Accurate prediction of carbon dioxide (CO₂) emissions is essential for effective carbon management and evidence-based climate policy. However, emission dynamics are inherently nonlinear and arise from complex interactions among technological, financial, energy, economic and demographic factors. This study develops an i...

Confessions of an AI Brain

Hypothesis-level reference argumentation network for case-based clinical reasoning in medical AI

Deep learning systems have achieved remarkable success in medical image analysis; however, most explainable artificial intelligence (XAI) methods remain descriptive, providing post-hoc explanations without actively validating diagnostic decisions. While concept-based and retrieval-based approaches improve interpretabil...

CampusHire: An AI-Powered Training and Placement Management System with Explainable Skill Matching

Campus training and placement (T&P) activities in many engineering institutions are still coordinated through spreadsheets, e-mail and notice boards, which makes eligibility checks, shortlisting and placement reporting slow and error-prone and gives students little guidance on which opportunities suit them. This paper...

A Systematic Review of Explainable Multimodal Artificial Intelligence for Early Identification of Developmental Potential in Children

Children who are developmentally lagging require quick and tailored supports- this is why focusing on potential development is important. AI has advanced by integrating multiple forms of speech, behaviour, physiology, and vision, and emerging AI systems for speech triage and multi-modal systems are demonstrating promis...