This paper is an experience report on a 13-week Test-Driven, AI-Assisted (TDAA) redesign of DSAA 3071, Theory of Computation, an upper-level course at the Hong Kong University of Science and Technology (Guangzhou). The design is simple: the course replaces lectures with self-directed, AI-assisted learning, and frequent, independently completed tests create a high-frequency quality gate. AI agents help the instructor prepare the learning path, course website, tests, grading workflow, and repairs. Two conditions made this strict gate workable. Students needed a visible preparation path of learning sheets and aligned validation practice, so the closed-book tests felt fair rather than arbitrary. The instructor needed an AI-assisted materials harness, a version-controlled agent workspace, so that weekly drafting, review, test production, and grading could scale with human oversight. Evidence from a student survey ($N=18$), weekly scores, and the project's git history suggests that students treated the tests as useful accountability and that the harness made frequent closed-book testing operational. The evidence is limited to one small, proof-heavy course without a control group. The contribution is therefore a reusable design pattern: high-frequency tests preserve individual accountability, while AI agents make material production and marking scalable. We release the harness as a public starter template so that other instructors can reproduce it.

翻译：暂无翻译