Designing Human-Centered Assessment in an AI World: A Framework for Making Student Thinking Visible

Abstract

The rapid adoption of generative artificial intelligence has challenged long-standing assumptions about assessment in higher education. While much of the current conversation has focused on academic integrity and AI detection, these approaches do not address a more fundamental question: What evidence is needed to make valid judgments about student learning? This paper argues that the emergence of AI has not changed the purpose of assessment but has highlighted the need to reconsider how learning is demonstrated. Building upon authentic assessment and evidence-centered assessment, the paper introduces Human-Centered Assessment as a conceptual approach that emphasizes making student thinking visible through authentic voice, reflection, judgment, context, process transparency, and ethical AI integration. It then presents three complementary frameworks that guide faculty from understanding the principles of human-centered assessment to evaluating existing assignments and redesigning assessments for AI-mediated learning environments. Together, these frameworks shift the conversation from detecting AI to designing assessments that generate richer evidence of student learning. The paper concludes by discussing implications for assessment practice, faculty development, and future research.