Artificial intelligence (AI) is increasingly deployed in higher education institutions (HEIs), yet its role in addressing basic human needs remain poorly understood. This study examines how AI enables and constrains progress toward Sustainable Development Goals 1 (No Poverty), 2 (Zero Hunger), and 3 (Good Health and Well-Being) by analysing 27 international case studies based on secondary institutional data sources through a sociotechnical lens. The findings show that AI contributes to basic-needs sustainability through three mechanisms (data visibility, institutional mediation, and capability amplification) that translate technological capabilities into social outcomes. A central insight is the role of redistributive governance, whereby HEIs deliberately redirect AI-generated operational efficiencies toward student support initiatives such as scholarships, food programs, and well-being services. These dynamics generate cross-SDG spillovers and give rise to three institutional roles for HEIs: (i) microcosms of sustainability systems, (ii) redistributive infrastructures, and (iii) capability-building ecosystems. The study contributes to the literature on AI and sustainable development by demonstrating that AI-driven sustainability outcomes emerge from sociotechnical interactions between digital technologies and institutional governance structures. The findings provide guidance for policymakers and university leaders seeking to align AI adoption with equity-oriented development strategies. • HEIs act as microcosms where AI solutions model broader sustainability transitions • Redistributive governance converts AI efficiencies into basic-needs support • Cross-SDG spillovers show AI generates cascading benefits across HEI ecosystems • AI drives SDG gains only when integrated into coordinated institutional systems • AI strengthens key competencies for food, health, and poverty-related capabilities
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Walter Leal Filho
Tiago F. A. C. Sigahi
Antonis Skouloudis
Technological Forecasting and Social Change
Universität Hamburg
Universidade de São Paulo
Universidade Estadual de Campinas (UNICAMP)
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Filho et al. (Mon,) studied this question.
www.synapsesocial.com/papers/69df2c01e4eeef8a2a6b105a — DOI: https://doi.org/10.1016/j.techfore.2026.124681