Framing, Judging, Steering: An Assessable Competency Model for Teach-ing Students to Reason With Generative AI

2026-06-04

AI Summary

Current educational systems lack effective assessments of students’ ability to co-reason with generative AI, particularly in diagnosing performance across critical subprocesses such as task framing, output evaluation, and model steering. To address this gap, this work proposes the CoRe-3 (Co-Reasoning) competency framework, which decomposes human–AI collaborative reasoning into three independently assessable dimensions: Framing, Judging, and Steering. The authors implement an open platform, CoReasoningLab, that empirically measures these competencies by simulating learner interactions using multiple AI models and leveraging cross-vendor large language models as scoring backends for fine-grained diagnostic feedback. Empirical validation demonstrates that the three competencies exhibit strong discriminant and convergent validity, confirming both the theoretical coherence and practical utility of the proposed model.

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 assessable skills we abbreviate FJS: Framing (specifying an ill-defined task before invoking AI), Judging (evaluating output for errors and unstated assumptions), and Steering (iteratively redirecting the model). Its distinguishing claim is the separation of pre-generation Framing from post-generation Steering, with Judging as the gate between. We ground the skills in theory, state five testable propositions, and instantiate them in CoReasoningLab, an open platform that presents flawed AI output and scores them independently. Over simulated learners (generated and graded by different models), the skills dissociate: each tracks its own manipulated competence while staying flat in the others, and grades become correlated when one competence is shared across all three (convergent and discriminant validity), across grader backends from two providers. Human-rater agreement and outcomes are next; we release the instrument, data, and protocol.

Problem

• Generative AI
• Competency Assessment
• Cognitive Offloading
• Prompting
• AI Literacy

Innovation

• Co-Reasoning
• Generative AI
• Competency Model
• Assessment Framework
• Human-AI Collaboration