We evaluate whether open-weight LLMs can assess Computational Thinking in middle-school students’ finite-state game designs against human labels. Across a rich and a sparse design, models detect some behaviors at near-human agreement but over-credit absent ones and fail at counting, loop detection, and standards mapping. Failures trace to fixable setup.