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 conceptual article offers a narrative synthesis of recent work in learning analytics, educational data science, human–AI interaction, and AI governance to propose stewardship as a necessary fourth paradigm of educational data science. Stewardship represents the professional, epistemic, and institutional work of governing judgment in an environment where analytic systems are increasingly generative and persuasive. Rather than treating stewardship as a general ethics checklist, the article positions it as the governance of epistemic and pedagogical authority: who determines what counts as evidence, interpretation, and educational action when AI systems help produce those judgments. The synthesis suggests that while GenAI can support bounded analytic tasks, evidence for systemic educational transformation remains limited and uneven. The field’s primary challenge is therefore not technical performance alone, but the governance of interpretation, validation, delegation, and action. By centering provenance, uncertainty, accountable oversight, learner agency, and institutional learning, stewardship provides an actionable framework for anchoring analytic innovation in responsible educational improvement.