Sustainable Intelligent Tutoring Systems for Social Well-Being: A Human-Centered Framework for Inclusive, Ethical, and Resilient Learning
Year: 2026
Type: article
Abstract: 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.
Source: Sustainability
DOI: 10.3390/su18178946
Author: Serafeim A. Triantafyllou
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7202037372