Jin-Guo Liu, Shang-Qi Lu, Xin-Ran Shi, Long-Li Zheng, Wei Wang (2026) — arXiv:2606.23315 (cs.CY) 📄 Full text (arXiv)
Liu et al. (2026) report on a 13-week Test-Driven, AI-Assisted (TDAA) redesign of a Theory of Computation course at HKUST (Guangzhou). The course replaced all lectures with self-directed, AI-assisted learning and weekly closed-book tests serving as high-frequency quality gates. AI agents helped the instructor prepare learning paths, course websites, test drafting, grading workflows, and content repairs — all managed through a version-controlled agent workspace. Student survey data (N=18), weekly scores, and git history suggest the model preserved individual accountability while making material production and marking scalable with human oversight. The work contributes a reusable design pattern for ai-changing-teaching-workflows that integrates active-learning principles with llm-powered scaffolding, and offers practical implications for assessment design in higher-ed contexts. The approach connects to broader conversations about automated-grading and llm-student-modeling-memory as enablers of scalable personalized education.
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