Intellectual Effort Inflection Point and Generative Artificial Intelligence-driven Epistemic Delegation
DOI:
https://doi.org/10.33423/yh3xsx30Keywords:
higher education, AI-mediated learning environments, assessment design in higher education, cognitive offloading, epistemic labor distribution, learning process visibility, metacognitive disclosure practicesAbstract
Generative Artificial Intelligence (GenAI) enables students to produce polished assignments without demonstrating the intellectual effort that higher education is meant to cultivate. This paper reframes GenAI not as a detection problem but as an instructional design challenge. Drawing on experiential learning theory and behavioral objective design, we develop the Delegation Spectrum Model and the Intellectual Effort Inflection Point (IEIP) to describe how epistemic labor is distributed between students and GenAI across stages of mediation. Using Refined Disclosure of Support Statements from a senior communication course and AI prompt stress testing of a high-stakes memo assignment, we illustrate how task architecture can permit cognitive displacement while preserving surface quality. We then propose design principles for epistemically visible objectives that require source engagement, evidence appraisal, and claim warranting. The framework offers a transferable diagnostic tool for AI-resilient curriculum reform and for clarifying when epistemic delegation supports learning and when it undermines it.
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