Artificial Intelligence, Human Capital and Social Sector Transformation: Evidence and Policy Implications for Health and Education

Artificial intelligence (AI) has emerged as one of the most consequential technological developments of the twenty-first century, with profound implications for healthcare and education. The increasing availability of large datasets, machine learning algorithms, generative AI, natural language processing and intelligent decision-support systems is transforming the way healthcare services are delivered and how knowledge is produced, transmitted and assessed. This paper reviews the emerging evidence on the impact of AI on the health and education sectors, with particular emphasis on service efficiency, personalization, decision-making, accessibility, learning outcomes, workforce transformation and ethical governance. The review indicates that AI can contribute to earlier disease detection, clinical decision support, personalized treatment, administrative efficiency, drug discovery and public-health surveillance. In education, AI facilitates adaptive learning, intelligent tutoring, automated assessment, personalized feedback, curriculum development and research assistance. However, the benefits are accompanied by substantial challenges, including algorithmic bias, privacy and data-security concerns, lack of transparency, misinformation, academic integrity problems, digital inequality and potential displacement or restructuring of professional roles. Recent systematic reviews indicate that evidence for educational improvements is promising but heterogeneous, while evidence concerning long-term organizational and patient-level outcomes remains limited. The paper argues that AI should not be conceptualized as a substitute for healthcare professionals or educators but as an augmentative technology operating within human-centred institutional frameworks. Effective adoption requires investments in digital infrastructure, AI literacy, regulatory capacity, data governance and continuous professional development. The paper concludes that the socioeconomic value of AI will depend less on technological capability alone than on the institutional capacity of countries to deploy AI safely, equitably and responsibly.