The paper introduces six design principles for embedding incidental learning into AI-assisted software development and presents SHIELD, a multi-agent system that surfaces contextual learning moments from AI coding agents' reasoning traces.
Critical Inker: Scaffolding Critical Thinking in AI-Assisted Writing Through Socratic Questioning
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abstract
As Large Language Models (LLMs) increasingly automate writing tasks, there is a growing risk of cognitive deskilling where users offload critical thinking to the system. To address this, we introduce Critical Inker, a writing tool designed to scaffold critical reflection during writing through logical analysis and socratic feedback. We present two methods: (1) A Socratic chatbot using questions to help them realize and fix logical errors in their writing and (2) Visual Feedback, which highlights logical errors in the text without dialog. We detail the technical implementation of the system and evaluate its argument extraction and logical validity accuracy. Our evaluation shows a 91.2% argument overlap with ground truth argument annotations and 87% validity accuracy. Finally, we conducted a small-scale pilot and discuss early qualitative results.
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2026 1verdicts
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Agents That Teach: Towards Designing Incidental Learning Back into AI-Assisted Software Development
The paper introduces six design principles for embedding incidental learning into AI-assisted software development and presents SHIELD, a multi-agent system that surfaces contextual learning moments from AI coding agents' reasoning traces.