Redesigning Teaching, Learning, and Assessment in the Age of Generative AI: A Constructive Alignment Perspective for Higher Education

Abstract

Generative artificial intelligence (GenAI) has changed the conditions under which students in higher education read, write, solve problems, and complete assessed work. The central pedagogical question is therefore no longer whether students use AI, but how AI use reshapes the relationship between intended learning outcomes, teaching–learning activities, and the evidence on which judgments of competence are based. This article presents a critical narrative review that integrates constructive alignment theory with research on cognitive offloading, scaffolding, self-regulated learning, formative feedback, and assessment validity under AI-rich conditions. The review argues that GenAI can function either as a pedagogically valuable scaffold that stimulates elaboration and reflection or as a shortcut that inflates task performance without building durable competence; which pathway dominates depends on instructional design, guardrails, and the assessment formats through which learning is inferred. The review further argues that GenAI does not invalidate constructive alignment but requires its extension: intended learning outcomes must specify the conditions of performance—with or without AI—as part of the construct, and the tool environment becomes an explicit component of alignment. Building on this analysis, the article develops a design framework organized around four decisions—whether AI should be prohibited, permitted, permitted with documentation, or required for a given task—and derives implications for course design, formative feedback, and credible certification. The central conclusion is that higher education should neither ban nor normalize GenAI indiscriminately but redesign aligned teaching and assessment systems that protect independent judgment while preparing students for responsible human–AI collaboration.