CI

At a glance

ClinicalIndex Comparison Record
N/ACompleted· 115 enrolled
Drug / intervention
AI post consultation feedbackdevice
Likely dose
Not stated in record
Structured eligibility isn't available for this trial yet — see the full criteria in the Eligibility tab below.

Standardized by ClinicalIndex from the ClinicalTrials.gov record · verify against the source.

Search/NCT07277829
NCT07277829N/ACompleted

AI-generated Feedback in Social Robotic Virtual Patients

Ioannis Parodis·interventional·Posted Dec 11, 2025·Updated Dec 11, 2025

In Brief

A clinical study evaluating AI post consultation feedback for Virtual Patient and 5 related conditions. Completed, enrolled 115 participants across 1 site.

Detailed Summary

The goal of this quasi-experimental educational study is to learn whether AI-generated post-consultation feedback in social robotic virtual patient interactions improves medical students' clinical performance in medical history-taking. The main question it aims to answer is: Can AI-generated feedback integrated in an AI-enhanced social robotic virtual patient platform improve medical students' clinical performance in medical history taking? Researchers will compare results from standardised examinations following the structure of an objective structured clinical examination (OSCE), of medical students performing virtual patient interactions with AI-generated post consultation feedback compared to medical students who have not received AI-generated feedback. Participants will perform five virtual patient cases in rheumatology using an established virtual patient platform: the Social AI-enhanced Robotic Interface (SARI). After completion of each case, students participate in follow-up seminars with consultant rheumatologists to discuss the cases. After completion of all nine cases, students take part in a OSCE based examination to evaluate medical-history taking.

Study Details

Study Typeinterventional
Allocation--
Masking--
Primary Purpose--
CountriesSweden

Timeline

N/ACompletedFinished
20252026
First PostedDec 11, 2025
Enrollment StartJan 27, 2025
Primary CompletionJun 5, 2025
TodayJul 2, 2026
Enrollment to primary: 4 monthsPosted 7 months ago

Interventions

AI post consultation feedbackdevice

A feedback algorithm which follows a two-stage design was implemented to generate post consultation feedback using large language models (LLMs) from OpenAI. The first stage of the feedback algorithm is an assessment model that evaluates student-VP dialogues using a predefined rubric developed in collaboration with consultant rheumatologists. This assessment model was iteratievly refined and validated prior to the study. The second stage of the feedback algorithm involves generating a feedback output based on the stageone assessment Students received approximately one page of structured written feedback immediately after completing each VP encounter with SARI. This feedback focused on medical history-taking within the context of rheumatology and included constructive comments with examples covering general history-taking, specific symptom enquiries, and systematic assessment of the VPs.