First, Do No Harm: AI Supervisor Scaffolds Novice Growth in Counselor Education

The most dangerous mistakes a novice counselor makes are not the obvious ones: they are utterances that sound caring while quietly violating professional ethics and leaving vulnerable clients less protected. We build an AI supervisor that does not replace novice counselors, but grows them—teaching them to internalize ethical violations they would otherwise never notice. What makes this supervisor non-trivial is not detection but teaching: it must locate the ethical-violating utterance, diagnose the ethical violation against APA principles, and deliver feedback that explains not just what went wrong, but why it is risky and how to respond differently. The core obstacle is that (1) ethical violations are by nature unlabeled in real clinical data, and (2) existing AI counselors trained only to match correct answers will never learn to teach. We resolve both at once: a controllable AI novice that intentionally enacts predefined mistake categories makes supervision labels a natural byproduct of generation, yielding ETHICSCAFF, a human-in-the-loop dataset; and GRPO under a Novice Growth Reward (NGR) optimizes the supervisor not for answer correctness but for whether a weaker novice model actually improves after reading its explanation. Across patient-disjoint tests, our best models reach 94.37% F1 for violated-principle classification and 74.24% F1 for violation localization; GRPO further improves localization precision, enabling the supervisor to surface context-dependent ethical risks that keyword or retrieval baselines can miss. Expert ratings favor our teaching-oriented explanations for professional depth and actionability, while supervised novices improve across all six counseling metrics. In a four-week pilot, our feedback produces the highest self-efficacy trajectory among all conditions, suggesting guidance that helps learners turn detected problems into safer responses.