When Help Hurts: Verification Load and Fatigue with AI Coding Assistants

AI coding assistants help, but developers still spend effort verifying model output. We isolate interface effects by holding a single LLM fixed while N = 60 participants solve three Python tasks with Inline, Chat, or Structured prompting, plus a no‑AI control. AI reduced workload by − 18.2 TLX points and time by 22% (25.0 vs. 32.1 min) and improved correctness (OR = 1.71). Within AI, Inline is fastest and lowest‑load on simple work; Chat yields higher correctness beyond a per‑observation complexity threshold (z ≈ + 0.41) without a time cost; Structured benefits novices at mid complexity. We introduce a mode‑agnostic verification‑load index (failures, time‑to‑first‑compile, churn, pauses, switches) that partially mediates rising stress/fatigue across tasks. We translate these findings into design guidance: adaptive mode orchestration, transparency on demand, and verification aware packaging, and propose reporting verification load alongside outcomes to evaluate interfaces as models evolve.