“I Didn’t Make the Micro Decisions”: Measuring, Inducing, and Exposing Goal-Level AI Contributions in Collaboration

As Large Language Models (LLMs) increasingly shape how users form, refine, and extend their goals, attributing contributions in human--AI collaboration becomes critical---both for users calibrating their own reliance and for evaluators assessing AI-assisted work. Yet existing methods focus on final artifacts, missing the process through which goals themselves are jointly shaped. We introduce a goal-level attribution framework, COTRACE, that decomposes explicit goals into verifiable requirements and traces both direct contributions and indirect influences across dialogue turns. Applying COTRACE to 638 real-world collaboration logs, we find that while models account for only ~10% of goal-shaping at the macro level, they contribute substantially more on introducing lower-level concrete requirements, and make various kinds of indirect contributions. Through controlled simulations, we show that interaction design choices significantly affect model goal-shaping behavior, with downstream effects on output diversity. In a user study, exposing participants to goal-level analyses shifts their perceived contributions by nearly 2 points on a 5-point scale, revealing systematic miscalibration in how users understand their own AI-assisted work.