Relational AI in Education: Reciprocity, Participatory Design, and Indigenous Worldviews

TL;DR

This paper advocates for designing AI in education that emphasizes relational, participatory, and Indigenous-inspired principles to foster social learning and community sustainability, rather than automation or individualization.

Contribution

It introduces a relational design framework for AI in education grounded in reciprocity and Indigenous worldviews, addressing key tensions and proposing responsible design directions.

Findings

AI should support learning with others, not replace human interaction.

Identifies tensions caused by GenAI in educational contexts.

Proposes design strategies to sustain communities and natural environments.

Abstract

Education is not merely the transmission of information or the optimisation of individual performance; it is a fundamentally social, constructive, and relational practice. However, recent advances in generative artificial intelligence (GenAI) increasingly emphasise efficiency, automation, and individualised assistance, risking the weakening of relational learning processes. Despite growing adoption, AI in education (AIED) research has yet to fully articulate how AI can be designed in ways that sustain the social and ecological relationships through which learning occurs. In this paper, we re-centre education as relational and frame learner-AI interactions as context-specific relationships with clearly defined purposes and boundaries, rather than positioning them as substitutes for, or replacements of, human interaction. Grounded in participatory design practices and inspired by Indigenous worldviews (including Aboriginal Australian, Native American, and Mesoamerican traditions) that foreground reciprocity and relational accountability, we argue that meaningful educational AI should support learning with others rather than replace them. We advance this perspective by: i) conceptualising AIED as a relational design problem grounded in reciprocity; ii) articulating key tensions introduced by GenAI in education; and iii) outlining design directions that expand the AIED design space toward reciprocity, including when not to use AI, how to define pedagogical boundaries, and how to support responsible uses of AIED innovations that sustain communities and natural environments.