One pilot instructor reported the adaptive proctor differentiated students at multiple proficiency boundaries and decoupled conceptual mastery from computational speed¶

Claim · All claims
Evidence · 1 study · qualitative r1 · q1
Currency · newest study 2026 · checked against its sources 2026-10-17 (automated, 1 of 1 entry) · not reviewed by a person · recheck by 2029: tool and model accuracy changes quickly

Subclaims¶

q1 i? A pilot engineering instructor reported the AI scaled cognitive burden dynamically, enabling differentiation at the D/F and A/B boundaries and presentation of stretch questions to the top quartile. → Ilya Mikhelson 2026

Evidence¶

Ilya Mikhelson 2026¶

Ilya Mikhelson. (2026). The Theoretical Foundation of Socratic Tests: Dynamic, Multimodal, Conversational Examinations. https://arxiv.org/abs/2607.29624 doi:10.48550/arxiv.2607.29624

q1 · i? · qualitative · r1

Qualitative instructor report from the Spring 2026 pilot (Section 6.1). The instructor described the AI as able to "titrates its way to the limit of each student's understanding." Case-level instructor testimony; no quantitative differentiation data are printed.

"Because the proctor scales the cognitive burden dynamically, the instructor was able to“directly compare transcripts to differentiate between students at the D/F boundary as well as the A/B boundary,”while simultaneously presenting“multiple ’stretch’ lines of questioning”to the top quartile of the cohort."

Discussion¶

Learner Variables¶

• Processing Speed — moderator: an instructional effect differs with it

Related Claims¶

• Intelligent tutoring systems improve achievement over non-individualised instruction — a broader claim this one bears on
• Immersive learning tools should surface misconceptions and track learner difficulties so instructors can adapt teaching and assessment — related
• Clustering yields a small number of behaviorally distinct team clusters that instructors can use for cluster-differentiated feedback — related
• Integrating cognitive scores with non-cognitive learner attributes enables the system to dynamically adapt AI-generated explanations in tone, depth and instructional strategy to individual learner performance — related
• Expert EAP reviewers rated GenAI-generated differentiated materials favorably for academic fidelity, proficiency appropriateness, and teachability — related
• Immediate, informative test results enable teachers to differentiate instruction to individual needs — related
• Over 80% of pilot students agreed the AI proctor accurately understood their responses and scaffolded them appropriately — related