Adaptive Scaffolding Needs Contingency: An AI Tutor That Escalates and Fades on What the Learner Does

2026-09-19

AI Summary

研究解决编码助手降低学习者规划与监控能力的问题，通过CoMeT系统根据学习者表现动态调整辅助程度来保持元认知需求不变。

Abstract

Coding assistants raise task performance, but learners plan and monitor less. Giving less away, the usual fix, conflates two things: how much work a system carries (cognitive load) and what the learner must decide before help arrives (metacognitive demand). Our principle, preserved metacognitive demand, holds the second constant and lets the first vary. CoMeT implements it: support rises when a learner fails at a decision point and fades on take-up. Within subjects, 131 adult learners used CoMeT, an unrestricted assistant and a question-only tutor on three Python tasks. CoMeT matched the question-only tutor's demand, delivered artifacts twice as often as the assistant, and frustrated learners less than the question-only tutor, with delegation and load unchanged. Learners often did not answer. Fading held when their turn addressed the decision under support, and CoMeT surrendered the full answer in one session in sixteen, against one in six for the question-only tutor.

Problem

• coding assistants
• task performance
• metacognitive demand
• cognitive load

Innovation

• Adaptive Scaffolding
• Metacognitive Demand
• Cognitive Load
• Dynamic Support