Framing, Judging, Steering: An Assessable Competency Model for Teach-ing Students to Reason With Generative AI
arXiv:2606.05983v1 Announce Type: cross Abstract: Generative AI makes answers easy and understanding hard, and uncritical use invites cognitive offloading. Schools still measure unaided performance, yet the real task is to produce good work with AI: framing an ill-defined task, judging the output, and steering the model toward a better result. This ability is rarely assessed in its own right; where measured, it collapses into one "prompting" score that cannot diagnose why AI use succeeds or fails. We propose CoRe-3 (Co-Reasoning), a competency model factoring productive AI use into three asses
The rapid proliferation of generative AI tools necessitates an urgent re-evaluation of educational methodologies as these technologies become commonplace.
This paper highlights a critical gap in assessing how humans effectively interact with AI, moving beyond simple prompting to more complex co-reasoning skills essential for future workforces.
The proposed CoRe-3 model shifts the focus of AI education from evaluating raw output to assessing the nuanced process of framing, judging, and steering AI, fundamentally changing how AI proficiency might be measured and taught.
- · Educational institutions adopting new AI curricula
- · Students proficient in AI co-reasoning
- · AI ethicists and pedagogical researchers
- · Traditional assessment models
- · Institutions slow to adapt AI education
- · Students relying solely on unaided cognitive performance
Educational systems will begin integrating structured frameworks for AI interaction competency into their curricula.
A new industry of AI-assisted learning tools and assessment platforms will emerge, designed around co-reasoning models.
The development of a global standard for AI co-reasoning proficiency could influence hiring practices and job markets.
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