How Do We Prepare Learners for AI-Shaped Futures? Introducing the Assessment-as-Capability Model

The rapid expansion of AI-enabled systems for assessment and feedback has intensified the need for theoretical frameworks that explain how learners interpret the information they receive and how developmental pathways are sustained across technological and institutional contexts. This chapter demonstrates a principled approach to evaluating theoretical frameworks by reviewing and comparing four candidate perspectives and then introducing an integrated framework, Assessment-as-Capability (AaC), that synthesizes experiential and sustainable views of assessment. AaC conceptualizes assessment as a developmental system in which capability grows through interactions among reflection, judgment, agency, and coherent institutional supports. A central contribution is the idea of capability trajectories, which explain how judgment and agency deepen over time and how equity operates as a developmental condition rather than a remedial goal. The framework also highlights AaC’s relevance for both doctoral inquiry and the wider field of assessment research by offering concepts that clarify how contemporary evaluative contexts, including those shaped by AI, prepare learners for future demands. The chapter outlines AaC’s core principles, conceptual contribution, boundary conditions, and guiding questions, illustrating how the framework supports capability-building approaches to assessment and feedback across varied educational settings.