Predicting Student Success in Communication Skills Learning Scenarios with Virtual Humans

Carnell, Stephanie; Lok, Benjamin; James, Melva T.; Su, Jonathan K.

doi:10.1145/3303772.3303828

Cited by 14 publications

(15 citation statements)

References 24 publications

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“…An exception to this is constituted by the study by Jani et al [ 32 ], which discusses these aspects and implications for humanity in medicine in the discussion section. Carnell et al [ 34 ] acted on these considerations by explicitly using interpretable AI models.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence Supporting the Training of Communication Skills in the Education of Health Care Professions: Scoping Review

Stamer¹,

Steinhäuser²,

Flägel³

2023

J Med Internet Res

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Background Communication is a crucial element of every health care profession, rendering communication skills training in all health care professions as being of great importance. Technological advances such as artificial intelligence (AI) and particularly machine learning (ML) may support this cause: it may provide students with an opportunity for easily accessible and readily available communication training. Objective This scoping review aimed to summarize the status quo regarding the use of AI or ML in the acquisition of communication skills in academic health care professions. Methods We conducted a comprehensive literature search across the PubMed, Scopus, Cochrane Library, Web of Science Core Collection, and CINAHL databases to identify articles that covered the use of AI or ML in communication skills training of undergraduate students pursuing health care profession education. Using an inductive approach, the included studies were organized into distinct categories. The specific characteristics of the studies, methods and techniques used by AI or ML applications, and main outcomes of the studies were evaluated. Furthermore, supporting and hindering factors in the use of AI and ML for communication skills training of health care professionals were outlined. Results The titles and abstracts of 385 studies were identified, of which 29 (7.5%) underwent full-text review. Of the 29 studies, based on the inclusion and exclusion criteria, 12 (3.1%) were included. The studies were organized into 3 distinct categories: studies using AI and ML for text analysis and information extraction, studies using AI and ML and virtual reality, and studies using AI and ML and the simulation of virtual patients, each within the academic training of the communication skills of health care professionals. Within these thematic domains, AI was also used for the provision of feedback. The motivation of the involved agents played a major role in the implementation process. Reported barriers to the use of AI and ML in communication skills training revolved around the lack of authenticity and limited natural flow of language exhibited by the AI- and ML-based virtual patient systems. Furthermore, the use of educational AI- and ML-based systems in communication skills training for health care professionals is currently limited to only a few cases, topics, and clinical domains. Conclusions The use of AI and ML in communication skills training for health care professionals is clearly a growing and promising field with a potential to render training more cost-effective and less time-consuming. Furthermore, it may serve learners as an individualized and readily available exercise method. However, in most cases, the outlined applications and technical solutions are limited in terms of access, possible scenarios, the natural flow of a conversation, and authenticity. These issues still stand in the way of any widespread implementation ambitions.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence Supporting the Training of Communication Skills in the Education of Health Care Professions: Scoping Review

Stamer¹,

Steinhäuser²,

Flägel³

2023

J Med Internet Res

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“…Currently, the prevalence of VH in health applications appears to lag behind that in other areas, such as education and training [ 118 , 119 ] or serious games [ 120 ], which is not surprising given the concerns about patient safety [ 121 ], ethical and legal issues [ 122 ], and perception of professional counselors [ 123 ]. Technological advancements will continue to augment VH capabilities; equally important is identifying the design tradeoffs associated with those capabilities in patient-facing systems.…”

Section: Discussionmentioning

confidence: 99%

Computer-Controlled Virtual Humans in Patient-Facing Systems: Systematic Review and Meta-Analysis

Chattopadhyay¹,

Ma²,

Sharifi³

et al. 2020

J Med Internet Res

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Background Virtual humans (VH) are computer-generated characters that appear humanlike and simulate face-to-face conversations using verbal and nonverbal cues. Unlike formless conversational agents, like smart speakers or chatbots, VH bring together the capabilities of both a conversational agent and an interactive avatar (computer-represented digital characters). Although their use in patient-facing systems has garnered substantial interest, it is unknown to what extent VH are effective in health applications. Objective The purpose of this review was to examine the effectiveness of VH in patient-facing systems. The design and implementation characteristics of these systems were also examined. Methods Electronic bibliographic databases were searched for peer-reviewed articles with relevant key terms. Studies were included in the systematic review if they designed or evaluated VH in patient-facing systems. Of the included studies, studies that used a randomized controlled trial to evaluate VH were included in the meta-analysis; they were then summarized using the PICOTS framework (population, intervention, comparison group, outcomes, time frame, setting). Summary effect sizes, using random-effects models, were calculated, and the risk of bias was assessed. Results Among the 8,125 unique records identified, 53 articles describing 33 unique systems, were qualitatively, systematically reviewed. Two distinct design categories emerged — simple VH and VH augmented with health sensors and trackers. Of the 53 articles, 16 (26 studies) with 44 primary and 22 secondary outcomes were included in the meta-analysis. Meta-analysis of the 44 primary outcome measures revealed a significant difference between intervention and control conditions, favoring the VH intervention (SMD = .166, 95% CI .039-.292, P=.012), but with evidence of some heterogeneity, I2=49.3%. There were more cross-sectional (k=15) than longitudinal studies (k=11). The intervention was delivered using a personal computer in most studies (k=18), followed by a tablet (k=4), mobile kiosk (k=2), head-mounted display (k=1), and a desktop computer in a community center (k=1). Conclusions We offer evidence for the efficacy of VH in patient-facing systems. Considering that studies included different population and outcome types, more focused analysis is needed in the future. Future studies also need to identify what features of virtual human interventions contribute toward their effectiveness.

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“…In teaching communication skills in specific to prospective providers, virtual humans are frequently used during training, as they can provide a standardized experience for all students [55]. In the study done by Wiehagen, he proposed and focused on four categories of medical simulation devices for improving provider training.…”

Section: Utilizing Technology For Training Medical and Dental Studentsmentioning

confidence: 99%

Patient-Provider Communication Training Models for Interactive Speech Devices

Ngantcha

Amith

Tao

et al. 2021

Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Human Body, Motion and Behavior

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Patient-provider communication plays a major role in healthcare with its main goal being to improve the patient's health and build a trustworthy relationship between the patient and the doctor. Provider's efficiency and effectiveness in communication can be improved through training in order to meet the essential elements of communication that are relevant during medical encounters. We surmised that speech-enabled conversational agents could be used as a training tool. In this study, we propose designing an ontology-based interaction model that can direct software agents to train dental and medical students. We transformed sample scenario scripts into a formalized ontology training model that links utterances of the user and the machine that expresses patient-provider communication. We created two instance-based models from the ontology to test the operational execution of the model using a prototype software engine. The assessment revealed that the dialogue engine was able to handle about 62% of the dialogue links. Future direction of this work will focus on further enhancing and capturing the features of patient-provider communication, and eventual deployment for pilot testing.

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Predicting Student Success in Communication Skills Learning Scenarios with Virtual Humans

Cited by 14 publications

References 24 publications

Artificial Intelligence Supporting the Training of Communication Skills in the Education of Health Care Professions: Scoping Review

Artificial Intelligence Supporting the Training of Communication Skills in the Education of Health Care Professions: Scoping Review

Computer-Controlled Virtual Humans in Patient-Facing Systems: Systematic Review and Meta-Analysis

Patient-Provider Communication Training Models for Interactive Speech Devices

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