Medical students’ perceptions of AI-based feedback and feedforward on communication skills in doctor–patient consultation - an acceptance study in a video-based simulation
Bibliographic Data
| ID | 15328158 |
|---|---|
| Authors | Moritz Bauermann (0009-0000-5786-0149, University of Augsburg, corresponding author), Thomas Rotthoff (0000-0002-5171-5941, University of Augsburg), Tobias Hallmen (0009-0005-6450-5694, University of Augsburg), Miriam Kunz (0000-0002-0740-6738, University of Augsburg), Emma André (0000-0002-2367-162X, University of Augsburg), Ann-Kathrin Schindler (0000-0002-2293-2357, University of Augsburg) |
| Year | 2025 |
| Volume | 30 |
| Issue | 1 |
| Pages | 2592414-2592414 |
| Publication date | 2025-12-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Medical Education Online (JOURNAL) |
| Journal identifiers | ISSN: 1087-2981 • E-ISSN: 1087-2981 |
| Publisher | Taylor & Francis (PUBLISHER • GB) |
| DOI | 10.1080/10872981.2025.2592414 |
| PMID | 41324408 |
| OpenAlex | W4416864358 |
| Language | EN |
| References cited | 59 |
Feedback and feedforward are highly relevant in promoting students' learning. With advances in artificial intelligence (AI), new opportunities to support feedback and feedforward are emerging. However, few studies have explored how medical students perceive and accept AI-based feedback and feedforward in medical communication training. In this study, we explored medical students' perceptions of AI-based and avatar-mediated feedback and feedforward in a simulation applying a doctor-patient consultation video. The participants comprised 82 medical students (56.1% female), 66 of whom were in their second semester and 16 in their fourth semester. Before participants saw a video of a medical student in a standardized pre-recorded consultation, they were asked to put themselves in the position of the peer shown. A human-like avatar subsequently provided AI-based feedback and feedforward for the medical student in the video. The participants-still taking on the shown medical student's role-were then asked to rate the perceived trustworthiness and their potential learning acceptance of the AI-based feedback and feedforward. The participants' ratings of trustworthiness and potential learning acceptance were higher for the AI-based, avatar-mediated feedforward than the feedback. Additionally, they reported a generally positive attitude toward AI. This attitude was positively correlated with a higher potential learning acceptance of feedback. The tendency to favor feedforward over feedback in interpersonal contexts-as described in the literature-was evident for the perception of the AI-based, avatar-mediated evaluations of a simulated doctor-patient consultation video. Future research could apply these insights to enhance AI-based learning in medical education, e.g. by providing students with AI-based feedforward on their own consultation videos and assessing their perceptions of the same
Control (management · Feed forward · Interpersonal communication · Peer feedback · Perception · Trustworthiness · Artificial Intelligence in Healthcare and Education · Clinical Reasoning and Diagnostic Skills · Simulation-Based Education in Healthcare
Starting at the Beginning
The Power of Feedback Revisited
Human Trust in Artificial Intelligence
A review of feedback models and typologies
Empathy, Sympathy, Care
Consequences of individual feedback on behavior in organizations.
Attitudes towards AI
Thematic analysis.
Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology
Statistical Power Analysis
Student Perceptions of AI-Generated Avatars in Teaching Business Ethics
AI-based avatars are changing the way we learn and teach
Information and Media Literacy in the Age of AI
In AI We Trust
Avatar customization orientation and undergraduate-course outcomes
A review of automated feedback systems for learners
Effective virtual patient simulators for medical communication training
Trust does not need to be human
Feedforward strategies in the first-year experience of online and distributed learning environments
What do teachers think and feel when analyzing videos of themselves and other teachers teaching
Between authenticity and cognitive demand
| Citation velocity | historical |
|---|---|
| Highly cited | No |