JMIR Publications

ABSTRACT

Background:

Speech-language pathology (SLP) students need repeated opportunities to practice clinical communication with patients who have communication disorders, including aphasia. Existing simulations support case-based learning, but many provide limited open-ended dialogue and limited affective feedback during communication breakdowns.

Objective:

This study aimed to describe and pilot-evaluate Virtual Observation and Interactive Clinical Education (V.O.I.C.E.), an emotionally responsive artificial intelligence (AI) virtual patient system for aphasia-focused communication training in graduate SLP education.

Methods:

We conducted a single-group, mixed methods pilot study with 11 graduate SLP students. V.O.I.C.E. combines a large language model patient persona with poststroke Broca aphasia, a multimodal affect pipeline that renders patient emotion through speech prosody, facial expression, and gesture, and an automated rubric-guided debriefing report. Participants completed three approximately 10-minute simulations with progressively more severe aphasia presentations. Quantitative measures included pre-post self-efficacy for clinical communication and poststudy ratings of dialogue realism, voice realism, facial/gesture realism, usability, and educational value, which were analyzed descriptively. Qualitative measures including open-ended questions examining participants’ experience with and attitudes towards the AI patient, which were analyzed using hybrid deductive-inductive content analysis.

Results:

Overall self-efficacy improved significantly after the simulations, with a mean within-person gain of 1.09 points on a 6-point scale (95% CI 0.33-1.84; p=.03). Significant gains were observed for confidence in interviewing clients with aphasia (mean 2.33 to 3.89; change +1.56; p=.04) and responding to unexpected patient behavior (mean 3.73 to 5.09; change +1.36; p=.03). Confidence gains were positively associated with perceived dialogue realism (Spearman rho=0.79; p=.01), facial/gesture realism (rho=0.81; p=.01), and voice realism (rho=0.75; p=.02). Qualitatively, students described V.O.I.C.E. as a safe first step toward clinical practice that supported iterative practice, feedback-driven strategy refinement, metacognitive awareness, and rehearsal of emotionally challenging interactions; they also identified limitations related to speech recognition, emotional subtlety, feedback specificity, and the need for direct patient contact.

Conclusions:

This pilot study suggests that emotionally responsive AI virtual patients may provide a useful opportunity for SLP students in their early stages of learning and practicing aphasia-focused clinical communication. The findings highlight the importance of multimodal realism, emotionally contingent patient behavior, and actionable debriefing. Larger controlled studies are needed to further evaluate the efficacy of the V.O.I.C.E. system.