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Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/231829
Analyzing instructor facilitation patterns in debriefings using AI-based speech metrics
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Background
Facilitation of debriefings is critical for fostering reflection and behavioral change. However, little is known about how novice instructors distribute their speaking time and how this impacts perceived debriefing quality. Artificial intelligence (AI)-driven conversational analysis offers an objective method to characterize facilitation patterns.
Methods
Participants were fifteen novice healthcare instructors with less than one year of experience who recently completed an online simulation course. A cross-sectional study was conducted using GailBot API for automated transcription and speech analysis of remote interprofessional debriefings. Standardized scenarios and consistent student participants controlled external variables. Instructor facilitation patterns were compared to DASH evaluations from students, instructors, and an expert observer.
Results
Instructors spoke for an average of 56.09% of debriefing time. Higher instructor talk time correlated negatively with DASH self-ratings (Spearman’s Rho = −0.627, p = .012).
Conclusion
AI-based analysis provides valuable insights into facilitation patterns, supporting multidisciplinary faculty development.
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ARMIJO RIVERA, Soledad, et al. Analyzing instructor facilitation patterns in debriefings using AI-based speech metrics. Clinical Simulation In Nursing. 2026. Vol. 112, num. 101897. ISSN 1876-1399. [consulted: 2 of October of 2026]. Available at: https://hdl.handle.net/2445/231829