Teaching opportunities for anamnesis interviews through AI based teaching role plays. A survey with online learning students from health study programs.

Autor/inn/en	Rädel-Ablass, Katharina; Schliz, Klaus; Schlick, Cornelia; Meindl, Benjamin; Pahr-Hosbach, Sandra; Schwendemann, Hanna; Rupp, Stephanie; Roddewig, Marion; Miersch, Claudia
Titel	Teaching opportunities for anamnesis interviews through AI based teaching role plays. A survey with online learning students from health study programs.
Quelle	In: BMC medical education, 25 (2025) 1, Art. 259, 11 S. PDF als Volltext kostenfreie Datei (1); PDF als Volltext kostenfreie Datei (2); PDF als Volltext kostenfreie Datei (3) Link als defekt meldenVerfügbarkeit
Sprache	englisch
Dokumenttyp	online; Zeitschriftenaufsatz
ISSN	1472-6920
DOI	10.25656/01:35778 10.1186/s12909-025-06756-0
URN	urn:nbn:de:0111-pedocs-357787
Schlagwörter	Anamnese; Künstliche Intelligenz; Gesprächsverhalten; Selbstgesteuertes Lernen; Digitales System; Training; Gesundheitsberuf; Hochschuldidaktik; Student
Abstract	Background: This study presents a novel approach to educational role-playing through an AI-based bot, leveraging GPT-4 to simulate anamnesis interviews in various learning scenarios. Developed collaboratively by an interdisciplinary team of university lecturers and AI experts, the bot provides a platform for students of different health study programs to engage in complex patient-health professional conversations, offering an alternative to traditional role plays with actors or real patients. Methods: This study utilized a GPT-4 based digital teaching assistant, implemented through a proprietary chatbot design platform, to train anamnesis interviews in virtual settings with students from different online health care study programs. Students' satisfaction, virtual patient's accuracy, its realism, and quality were evaluated with a quantitative survey. Results: The evaluation of the bot focused on student feedback, highlighting a preference for the AI-driven method due to its immersive and interactive nature. Preliminary results show that students consistently rate the language ability of the AI model positively. More than 80% of students rated the professional and content-related precision of the virtual patient as good to excellent. Even as a text-based chatbot, the vast majority of students see a fairly close to very close relationship to a real anamnesis interview. The results further indicate that students even prefer this training approach to traditional in-person role-plays. Conclusions: The study underscores the bot's potential as a versatile tool for enriching learning experiences across multiple health disciplines, signaling a meaningful shift in educational practices towards the integration of AI technologies. (DIPF/Orig.).
Erfasst von	DIPF \| Leibniz-Institut für Bildungsforschung und Bildungsinformation, Frankfurt am Main
Update	2026/4