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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.09042 |
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| _version_ | 1866917478564626432 |
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| author | Xu, Chen Lyu, Zhenyu Lan, Tian Yang, Yi Ji, Yu Ji, Luyao Shen, Jian Wang, Zhihua Cui, Leyang Zhang, Jieshuo Dong, Qunxi Yang, Minqiang Wang, Juan Liu, Xiuling Hu, Bin |
| author_facet | Xu, Chen Lyu, Zhenyu Lan, Tian Yang, Yi Ji, Yu Ji, Luyao Shen, Jian Wang, Zhihua Cui, Leyang Zhang, Jieshuo Dong, Qunxi Yang, Minqiang Wang, Juan Liu, Xiuling Hu, Bin |
| contents | 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 9,915-instance 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. Experiments show that a novice guided by our supervisor outperforms an unguided peer on clinical metrics, and that teaching-oriented optimization via NGR further sharpens the supervisor's own ethical detection. In a user study with novice counseling-psychology students, participants show significant self-efficacy gains across all eight assessed competencies after receiving AI supervisory feedback, demonstrating that the scaffold transfers from simulation to real-world practice. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_09042 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | First, Do No Harm: AI Supervisor Scaffolds Novice Growth in Counselor Education Xu, Chen Lyu, Zhenyu Lan, Tian Yang, Yi Ji, Yu Ji, Luyao Shen, Jian Wang, Zhihua Cui, Leyang Zhang, Jieshuo Dong, Qunxi Yang, Minqiang Wang, Juan Liu, Xiuling Hu, Bin Computation and Language 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 9,915-instance 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. Experiments show that a novice guided by our supervisor outperforms an unguided peer on clinical metrics, and that teaching-oriented optimization via NGR further sharpens the supervisor's own ethical detection. In a user study with novice counseling-psychology students, participants show significant self-efficacy gains across all eight assessed competencies after receiving AI supervisory feedback, demonstrating that the scaffold transfers from simulation to real-world practice. |
| title | First, Do No Harm: AI Supervisor Scaffolds Novice Growth in Counselor Education |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2508.09042 |