Human-in-the-Loop Control of Objective Drift in LLM-Assisted Computer Science Education
Large language models (LLMs) are increasingly embedded in computer science education through AI-assisted programming tools, yet such workflows often exhibit objective drift, in which locally plausible outputs diverge from stated task specifications. Existing instructional responses frequently emphasize tool-specific prompting practices, limiting durability as AI platforms evolve. This paper adopts a human-centered stance, treating human-in-the-loop (HITL) control as a stable educational problem rather than a transitional step toward AI autonomy. Drawing on systems engineering and control-theoretic concepts, we frame objectives and world models as operational artifacts that students config...
| Authors | Mark Dranias, Adam Whitley |
|---|---|
| Primary category | cs.AI |
| Published | 31 March 2026 |
| Last updated at source | 31 March 2026 |
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