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Artificial Intelligence in Medical Education

Artificial Intelligence in Medical Education - AIMED

Status
Recruiting
Phases
Unknown
Study type
Interventional
Source
DRKS
Registry ID
DRKS00040063
Enrollment
180
Registered
2026-05-18
Start date
2026-06-09
Completion date
Unknown
Last updated
2026-08-03

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Medical students solve case studies on the diagnosis and communication of lung cancer, melanoma, Huntington's disease, and sexually transmitted diseases

Interventions

Group 1: Conversation with an AI chatbot about delivering bad news, with AI-generated feedback Group 2: A conversation with an AI chatbot about delivering bad news, without AI-generated feedback, but

Sponsors

Charité - Universitätsmedizin Berlin
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: - Medical students at one of the two universities - Semester 9 or 10 of medical studies - Provided informed consent

Exclusion criteria

Exclusion criteria: - Participants who do not fulfill the inclusion criteria - Participants who do not provide answers to the primary outcome (pre- and post-intervention) - Participants who indicate repeated participation - Participants who fail plausibility checks, for example by completing the questionnaire in an unrealistically short time or after a very short interaction with the Chatbot - Participants who change the conversation topic away from the intended patient-doctor interaction or instances where the AI chatbot malfunctions and deviates from the conversation (e.g., adopts the physician role instead of the patient role) - Participants who exclude themselves from participation at the end of the questionnaire

Design outcomes

Primary

MeasureTime frame
Perceived communication competence (PCC) regarding breaking bad news measured before the interaction with the AI chatbot and after the feedback measured on an 11-point Likert scale from 0 (very low) to 10 (very high).

Secondary

MeasureTime frame
PCC for sexual history-taking, socio-demographic characteristics such as gender and year of study, Feedback Perceptions Questionnaire (FPQ), grade of the conversation in the feedback, structural characteristics of the conversation (duration, number of tokens, content)

Countries

Germany

Contacts

Public ContactJan Zöllick

Charité - Universitätsmedizin Berlin

jan.zoellick@charite.de+4930450529055

Outcome results

None listed

Source: DRKS (via WHO ICTRP) · Data processed: Aug 10, 2026