The study surveyed 761 Nigerian healthcare professionals to assess their readiness for AI adoption in clinical settings. While 92.6% were aware of AI, only 63.0% felt prepared and 40.9% reported low knowledge, highlighting a gap between awareness and readiness. Major barriers identified were lack of training, poor infrastructure, high costs, job displacement fears, ethical and data privacy concerns, with significant regional and professional differences in preparedness and attitudes.
arXiv:2607. 18884v1 Announce Type: cross Abstract: Artificial intelligence (AI) is increasingly integrated into healthcare to support diagnostics, decision-making, and administrative processes.
The scoping review examines how artificial intelligence (AI) is applied across all stages of medication management in rural healthcare settings, from prescribing to post-administration monitoring. It identifies four main themes: the types of AI used, the medication phases impacted, the effectiveness in reducing errors, and rural-specific challenges such as infrastructure and alert fatigue. Studies show machine‑learning surveillance can cut prescribing and transcription errors by 34% to 80%, yet barriers like governance gaps, funding limits, and clinician resistance remain.
The article applies an Education-Centered AI Policy Framework to Ghana’s National Artificial Intelligence Strategy 2025‑2035, examining six key components such as teacher agency, curriculum, and responsible AI. It finds the strategy ambitious in promoting AI literacy, youth skills, and rural outreach, yet it falls short on school‑level implementation, teacher preparation, and culturally responsive pedagogy. The authors highlight concerns about transparency, coherence, and the need for a sector‑specific policy that links workforce readiness with classroom practice and learner protection.
arXiv:2606. 00038v1 Announce Type: cross Abstract: Artificial intelligence (AI) literacy is increasingly recognized as a foundational competency for all university graduates.
arXiv:2608. 12360v1 Announce Type: cross Abstract: Background: AI/ML-enabled medical devices are increasingly deployed in healthcare under evolving regulatory frameworks.
arXiv:2606. 28960v1 Announce Type: new Abstract: Physicians now pose millions of clinical questions to AI tools each week, yet these tools are evaluated largely on hypothetical or exam-style questions, not those actually asked in practice.
FLARE is a systematic, uncertainty‑aware framework that evaluates the financial and operational implications of adopting AI in healthcare. It integrates fuzzy logic, time‑driven activity‑based costing, and return‑on‑investment analysis to estimate costs of clinical service delivery, AI development and operation, and the economic impact of workflow integration. A case study on AI‑assisted large vessel occlusion detection in the CT stroke pathway demonstrated that FLARE can quantify conventional pathway costs, AI‑related costs, and AI‑enabled savings, identifying a break‑even threshold of about 3,992 patients per year and a positive first‑year ROI at typical stroke volumes of 5,000 patients.
arXiv:2607. 14782v1 Announce Type: new Abstract: Grounded in human rights-based frameworks such as the UNESCO Recommendation on the Ethics of AI, the Global Index on Responsible AI (GIRAI) examines how countries translate responsible AI commitments into enforceable protections, institutional capacity, and redress mechanisms.
arXiv:2609.15624v1 Announce Type: cross Abstract: Researchers assessing competent generative-AI use at work must choose among self-reports, objective tests, and measures of oversight and reliance. We...
The study investigates how generative AI (GenAI) affects student learning in AI-related courses, using survey data from 118 students across 12 courses. Four distinct user clusters were identified—high-use, light-use, and two moderate-use groups—each showing varying benefits and reliance patterns. The research highlights that early reliance, evaluation literacy, and instructor policies significantly influence perceived academic benefits and negative impacts, underscoring the need for institutional policies to address inequities in AI use.
arXiv:2608. 11540v1 Announce Type: cross Abstract: The convergence of artificial intelligence (AI), Industrial Internet of Things, cyber-physical systems, and advanced robotics is reshaping manufacturing faster than engineering curricula can adapt, widening the gap between the competencies required on the shop floor and those delivered by traditional engineering and technology education.
arXiv:2606. 11830v1 Announce Type: new Abstract: Background.