Redesign assessments and curricula so AI availability supports rather than impedes expertise growth¶

Strategy · All strategies
Evidence · no claims cited

Description¶

For educational institutions, the paper recommends resolving the tension between short-term assignment completion and long-term capability building through objective redesign. It states: "Educational institutions should either redesign assessments to remain meaningful with AI availability (e.g., reorganize the objectives so as to let adopting AI bring individual growth) or build curricula where encouraging the tool usage does not impede the growth of expertise." Institutional and classroom policies should foster fair use rather than total reliance on AI.

Design Implications¶

Context¶

Requirements¶

• Assessment redesign discussed before practitioners set strategies individually
• Policies that foster fair use of AI

Constraints¶

• The paper frames this as addressing the flexibility-of-objectives factor, which manifests differently in education than in industry where effective productivity dominates

Target Learners¶

• educational institutions and teachers
• students as recipients of AI in learning

Target Learning Goals¶

• long-term capability building alongside AI tool usage
• academic integrity and fair use

Affordances¶

• Five Factor Ai Productivity Moderation Framework

Related Strategies¶

• Build educational AI with pedagogical guardrails such as withholding direct solutions and embedded reflection steps
• Address generative AI in higher education through assessment redesign and proportional reflective practices rather than surveillance or punitive controls
• Redesign assessment toward progressive, authentic, and reflective methods rather than intensive paperwork

Examples¶

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Key Sources¶

• Won Ik Cho, Seong-hun Kim, Geunhye Kim. (2026). Position: Adopting AI in Practice Does Not Guarantee the Productivity Boost. Proceedings of the 43rd International Conference on Machine Learning, PMLR 306. https://arxiv.org/abs/2605.24688