Critical review reveals critical evaluation gaps in AI tutoring systems across equity, well-being, and environmental impacts, highlighting the need for a comprehensive human-centered framework.
Intelligent Tutoring Systems (ITS) can provide adaptive explanations, practice, feedback, and learner modelling at a scale that conventional one-to-one tutoring cannot readily achieve. Yet learning gains alone do not establish that an ITS is sustainable or beneficial to social well-being. This critical review integrates evidence on ITS effectiveness with research and policy guidance concerning learner agency, psychological well-being, inclusion, accessibility, data governance, algorithmic fairness, teacher roles, institutional capacity, and environmental responsibility. The review identifies a persistent evaluation gap: many studies privilege short-term achievement while under-measuring distributional effects, autonomy, emotional safety, teacher workload, lifecycle cost, and computational impact. To address this gap, the paper proposes the Human-Centered Sustainable Intelligent Tutoring Systems (H-SITS) framework as a conceptual and integrative framework for organizing candidate dimensions of sustainable tutoring and guiding future empirical validation. The framework organizes seven interdependent dimensions: pedagogical effectiveness; learner agency and well-being; inclusion and accessibility; trust, privacy, and fairness; teacher augmentation; institutional and economic viability; and environmental responsibility. It also presents a staged implementation roadmap and a research agenda emphasizing longitudinal, participatory, and equity-sensitive evaluation. The central conclusion is that sustainable tutoring is not a property of an algorithm alone. It is an outcome of socio-technical design, governance, and continuous evidence-based adaptation that keeps human flourishing, educational justice, and public value as primary objectives.
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Serafeim A. Triantafyllou (2026) studied this question.
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