From Prediction to Stewardship: Framing Educational Data Science in the Age of Generative AI

As generative AI expands the technical frontiers of prediction, measurement, and design, a growing tension has emerged between algorithmic fluency and institutional trust. This paper proposes stewardship as a necessary fourth paradigm of educational data science. Stewardship represents the professional and epistemic work of governing judgment in an environment where analytic systems are increasingly generative and persuasive. Current research suggests that while AI excels at bounded analytic tasks, its capacity for systemic educational transformation remains unproven. Therefore, the field’s primary challenge is no longer technical performance, but the governance of interpretation, validation, and action. By centering on provenance, accountable oversight, and learner agency, stewardship provides the framework needed to anchor analytic innovation in responsible institutional improvement and human-centric purposes.