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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.05856 |
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| _version_ | 1866914447328542720 |
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| author | Gao, Zhiqi Zhu, Guo Luo, Huarui Pan, Dongyijie Primo Tang, Haoming Zhang, Bingquan Pei, Jiahuan Li, Jie Wang, Benyou |
| author_facet | Gao, Zhiqi Zhu, Guo Luo, Huarui Pan, Dongyijie Primo Tang, Haoming Zhang, Bingquan Pei, Jiahuan Li, Jie Wang, Benyou |
| contents | Standardized patients (SPs) play a central role in clinical communication training but are costly, difficult to scale, and inconsistent. Large language model (LLM) based AI standardized patients (AI-SPs) promise flexible, on-demand practice, yet learners often report that they talk like a patient but feel different. We interviewed 12 clinical-year medical students and conducted three co-design workshops to examine how learners experience constraints of SP encounters and what they expect from AI-SPs. We identified six learner-centered needs, translated them into AI-SP design requirements, and synthesized a conceptual workflow. Our findings position AI-SPs as tools for deliberate practice and show that instructional usability, rather than conversational realism alone, drives learner trust, engagement, and educational value. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_05856 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | "It Talks Like a Patient, But Feels Different": Co-Designing AI Standardized Patients with Medical Learners Gao, Zhiqi Zhu, Guo Luo, Huarui Pan, Dongyijie Primo Tang, Haoming Zhang, Bingquan Pei, Jiahuan Li, Jie Wang, Benyou Human-Computer Interaction Standardized patients (SPs) play a central role in clinical communication training but are costly, difficult to scale, and inconsistent. Large language model (LLM) based AI standardized patients (AI-SPs) promise flexible, on-demand practice, yet learners often report that they talk like a patient but feel different. We interviewed 12 clinical-year medical students and conducted three co-design workshops to examine how learners experience constraints of SP encounters and what they expect from AI-SPs. We identified six learner-centered needs, translated them into AI-SP design requirements, and synthesized a conceptual workflow. Our findings position AI-SPs as tools for deliberate practice and show that instructional usability, rather than conversational realism alone, drives learner trust, engagement, and educational value. |
| title | "It Talks Like a Patient, But Feels Different": Co-Designing AI Standardized Patients with Medical Learners |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2602.05856 |