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Large language model virtual patients offer an alternative, but evidence on automated feedback and perceptions across diverse health professions remains limited. This study examined healthcare students’ perceptions of an enhanced GPT-4-powered virtual patient with integrated automated feedback on professional and communication competence. Methods included CUQ usability ratings, structured scales, and qualitative content analysis.",{"@graph":69,"@context":127},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/training-anamnesis-interviews-with-a-chatbot-virtual-patient-and-integrated-feedback-healthcare-students-perspectives/392218/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/training-anamnesis-interviews-with-a-chatbot-virtual-patient-and-integrated-feedback-healthcare-students-perspectives/392218.png","ImageObject",300,407,{"name":92,"@type":93},"Sophia Brooks","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-25",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",17,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119,123],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does the study address about anamnesis training?","Question",{"text":113,"@type":114},"Real or standardized patient training is resource-intensive, hard to scale, and limited in online learning. The study focuses on whether an LLM-powered virtual patient can support anamnesis training with integrated automated feedback.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How was the GPT-4-powered virtual patient used in the study?",{"text":118,"@type":114},"Healthcare students conducted an anamnesis interview with a chatbot-based virtual patient simulating brain hemorrhage and multimorbidity, then received automated feedback before completing an online survey.",{"name":120,"@type":111,"acceptedAnswer":121},"What were the main findings from usability and response quality measures?",{"text":122,"@type":114},"Usability was rated high, with top ratings for response quality and feedback quality. Realism scored lower, and qualitative results valued interaction realism and the integrated feedback mechanism.",{"name":124,"@type":111,"acceptedAnswer":125},"What improvements and future research directions does the study suggest?",{"text":126,"@type":114},"Students reported limited perceived naturalness, and future systems should improve realism and emotional expressiveness. Future research should examine transfer of acquired skills to clinical practice and long-term learning outcomes.","https://schema.org",{"og:url":83,"og:type":129,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":131,"canonical":83},"index,follow",{"doc_id":133,"site_id":62},392218,1790414476,{"code":4,"msg":5,"data":136},{"doc_id":133,"user_id":137,"nickname":92,"user_avatar":138,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":139,"file_id":140,"file_url":141,"file_type":142,"file_size":143,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":144,"language":145,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":146,"faqs":147,"seo_title":148,"seo_description":67,"update_tm":149,"read_time":150,"preview_page_count":4},962084925636,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d","Training anamnesis interviews with a chatbot virtual patient and integrated feedback: healthcare students’ perspectives  \nCornelia Schlick  \nIU International University of Applied Sciences  \nKatharina Rädel-Ablass  \nIU International University of Applied Sciences  \nKlaus Schliz  \nIU International University of Applied Sciences  \nClaudia Miersch  \nIU International University of Applied Sciences  \nResearch Article  \nKeywords: virtual patient, large language model, anamnesis training, health professions education, automated feedback  \nPosted Date: August 7th, 2026  \nDOI: [https://doi.org/10.21203/rs.3.rs-10473687/v1](https://doi.org/10.21203/rs.3.rs-10473687/v1)  \nLicense: 􀁱 􀅏 This work is licensed under a Creative Commons Attribution 4.0 International License.  \nRead Full License  \nAdditional Declarations: No competing interests reported.  \nAbstract  \nBackground  \nEffective anamnesis is a core clinical competency, yet training opportunities with real or standardized patients are resource-intensive, difficult to scale, and particularly limited in online learning environments. Large language model (LLM)-powered virtual patients (VPs) offer a promising alternative, but most existing evidence focuses on medical students, and the integration of automated feedback and its perception across other health professions remains underexplored. This study examined healthcare students' perceptions of an enhanced GPT-4-powered VP with integrated automated feedback on professional and communication competence.  \nMethods  \nA cross-sectional study was conducted at IU International University of Applied Sciences (January– March 2025) among undergraduate students from eight health-related Bachelor's programs, including nursing, physiotherapy, occupational therapy, speech therapy, and nutritional science. Participants conducted an anamnesis interview with a chatbot-based VP simulating a patient with brain hemorrhage and multimorbidity, then received automated feedback before completing an online survey. Usability was assessed with the Chatbot Usability Questionnaire (CUQ); response quality, realism, behavioral engagement, feedback quality, and training benefit were assessed using purpose-developed, pilot-tested scales (5-point Likert), analyzed via medians, Cronbach's alpha, and Positive Response Proportions. Open-ended responses underwent qualitative content analysis following Mayring.  \nResults  \nSixty students participated (83 .3% female; mean age 37.0 years) . Usability was rated high (CUQ mean 76.93, SD 12 . 51) . Response quality and feedback quality received the highest ratings (Cronbach's α = 0.90 and 0 .86, respectively), while realism scored lower (α = 0 .87) . Despite limited perceived naturalness, most students reported behaving professionally, respectfully, and empathically toward the VP. Educational value was rated highly, especially regarding professional competence and communication skills. Qualitative findings highlighted interaction realism and the integrated feedback mechanism as the most valued aspects, alongside requests for more unpredictable patient behavior and multimodal interaction formats.  \nConclusions  \nAI-based virtual patients with integrated automated feedback are perceived as a valuable, scalable tool for anamnesis training across diverse health professions, extending prior evidence beyond medical  \neducation. While realism and emotional expressiveness remain areas for improvement, findings support the use of such systems as an effective formative learning tool. Future research should investigate the transfer of acquired skills to clinical practice and long-term learning outcomes.  \nINTRODUCTION  \nEffective anamnesis, commonly referred to as history taking, is a cornerstone of clinical practice across all health professions, forming the basis for accurate diagnosis, appropriate treatment planning, and the establishment of therapeutic patient–provider relationships.1,2 The systematic acquisition of anamnesis skills remains ","cbCaid5BdS4SNMZe","https://ap.wps.com/l/cbCaid5BdS4SNMZe","pdf",1549732,21,"English","# Background\n# Methods\n# Results\n# Conclusions\n# Introduction","[{\"question\":\"What problem does the study address about anamnesis training?\",\"answer\":\"Real or standardized patient training is resource-intensive, hard to scale, and limited in online learning. The study focuses on whether an LLM-powered virtual patient can support anamnesis training with integrated automated feedback.\"},{\"question\":\"How was the GPT-4-powered virtual patient used in the study?\",\"answer\":\"Healthcare students conducted an anamnesis interview with a chatbot-based virtual patient simulating brain hemorrhage and multimorbidity, then received automated feedback before completing an online survey.\"},{\"question\":\"What were the main findings from usability and response quality measures?\",\"answer\":\"Usability was rated high, with top ratings for response quality and feedback quality. Realism scored lower, and qualitative results valued interaction realism and the integrated feedback mechanism.\"},{\"question\":\"What improvements and future research directions does the study suggest?\",\"answer\":\"Students reported limited perceived naturalness, and future systems should improve realism and emotional expressiveness. Future research should examine transfer of acquired skills to clinical practice and long-term learning outcomes.\"}]","Training Anamnesis Interviews with a Chatbot Virtual Patient and Integrated Feedback - Healthcare Students’ Perspectives | PDF",1790364185,53]