Exploring Healthcare Virtual Simulation Modality Preferences an

Interprofessional Learning Experience

Lorene Cobb

, Leslie Rippon

, Marcia Downer, Lauren Snowdon, Alicia McGregor,

Natalie Neubauer, Lisa Sheikovits, Angela Lis and Genevieve Pinto Zipp

School of Health and Medical Sciences, Seton Hall University, 400 South Orange Avenue, South Orange, U.S.A.

Keywords: Virtual Reality, AI Avatar, Virtual Simulated Patients, Healthcare Simulation, Interprofessional Education.

Abstract: Background: Various simulation platforms are available to employ a trial-and-error teaching method via

artificial experiences to engage healthcare professional students in activities reflecting real-life experiences

without risks. Objective: To understand faculty preferences regarding two different types of virtual simulation

platforms. Methods: Upon completing two 30-minute avatar-based simulation experiences employing a

patient case under two different simulation platforms (A=AI generated avatar, B=Live actor avatar)

participants completed online surveys including the Interprofessional Collaborative Competency Attainment

Scale (ICAAS), demographic information, sliding scale to assess perception of verbal and nonverbal

communication, and open question items assessing perception of communication, perceptions in avatar type

preference, and if the experience promoted confidence, ability and knowledge. Sample: This pilot study

consisted of 9 faculty members from various allied health professions. Results: Participants preferred the Live

actor avatar over the AI avatar experiences for verbal communication and authenticity of emotions. Following

the Live avatar simulation, participants reported improved perception of confidence, ability, and knowledge

necessary for interprofessional teamwork. Conclusion: The small sample size may affect the generalizability

of the results, participants perceived value to both types of avatar patient experiences with the perceptions

more favourable for simulating patient centred interactions with the live actor avatars.

1 INTRODUCTION

As health professions educators it is imperative that

we prepare students to effectively meet the needs of

today’s ever-changing healthcare landscape.

Academic institutions must prepare health

professional students to be ready to address these

needs upon entering the workforce. To meet this

challenge, health professional educators continually

infuse innovative and effective teaching methods and

evidence based instructional materials into the

academic environment to ensure that students are

ready for person-centered entry-level practice. Not

surprising academic institutions are turning to

simulation-based platforms as a teaching modality

employing a trial-and-error method of teaching and

learning via artificial experiences that engage learners

https://orcid.org/

0009-0005-3581-157X

https://orcid.org/0009-0000-1387-4215

https://orcid.org/0000-0003-0683-0673

in activities that reflect real-life conditions without

risk taking consequences (Zhai, X. et al. 2021).

Traditionally, health profession programs have

infused individualized simulation experiences to

promote and assess students' understanding of

complex clinical scenarios via a hands-on active

learning approach. Simulated scenarios which

incorporate students from across professional

disciplines have emerged over the past several years

to provide a unique opportunity to merge theory with

practice by highlighting the interdependency that

exists amongst and between health professionals

when providing person centered team-based care

(Clapper, 2010). The nature of such diverse, in-

person interprofessional simulation experiences have

been found to spontaneously enhance student

communication skills and promote development of

respect for different value systems through effective

890

Cobb, L., Rippon, L., Downer, M., Snowdon, L., McGregor, A., Neubauer, N., Sheikovits, L., Lis, A. and Zipp, G. P.

Exploring Healthcare Virtual Simulation Modality Preferences an Interprofessional Learning Experience.

DOI: 10.5220/0013500400003932

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 17th International Conference on Computer Supported Education (CSEDU 2025) - Volume 1, pages 890-898

ISBN: 978-989-758-746-7; ISSN: 2184-5026

personal interactions (Rider, Kurtz, Slade, et al,

2014). While there are numerous benefits of in-

person simulation experiences, including the

development of leadership and teamwork (Endacott

et al., 2014), improved decision making and critical

thinking (Rhodes and Curran, 2005), clinical skills

and clinical performance benefits (Alinier et al.,

2006), enhanced patient deterioration management

(Cooper et al., 2012), and situation awareness

improvement (Bogossian et al., 2014) have been

noted in the literature. Additoinally, notable barriers

including lack of time, resources, financial cost, and

workload issues (Al-Ghareeb, Cooper, 2016) have

also been reported.

As an alternative to traditional, in-person

simulations, active learning using virtual reality

platforms or gaming platforms, have been employed

as an environment for meaningful educational

scenarios. Virtual reality (VR) has demonstrated

usability as a teaching modality to enhance

interprofessional collaboration and communication

(Qiau et al., 2021). In general, diverse, virtual

interprofessional simulation learning experiences

have been found to spontaneously enhance student

communication skills and promote the development

of respect for different value systems through

effective personal interactions (Rider, Kurtz, Slade, et

al, 2014).

The Perception –Action Theory which provides a

framework for understanding how individual

preferences influence learning processes within the

cognitive system was used as a lens to guide the

study. This theory posits that perception and action

are interdependent, forming a continuous feedback

loop that shapes how we interact with and learn from

our environment (Biwer et al. 2020). The continuous

feedback loop between perception and action allows

learners to adapt their strategies based on the

outcomes of their actions. This adaptability is crucial

for personalized learning, where students adjust their

approaches based on what works best for them (Fujii

2024). Students who are given the autonomy to

choose their learning methods and environments have

been noted to perform better academically; thus,

accommodating students learning preferences can

lead to more effective, meaningful learning

experiences (Biwer et al. 2020, Fujii, 2024).

To date, limited evidence is available

differentiating amongst the various simulation-based

platforms' impact on learning. This article presents

pilot data exploring faculty preferences regarding two

different types of simulation platforms used to

promote student learning, one using AI generated

avatar patients and the other using Live actor avatar

patients. Recognizing this interdependency between

perception and action, in this study we sought to

explore different simulation-based learning

experiences and individual perceptions towards them.

We believe findings from this pilot project will

further inform the development of meaningful

simulation-based learning experiences. Additionally,

given the limited evidence supporting avatar

simulation-based learning experiences’ ability to

mimic real-life scenarios, the accuracy of the case

portrayal and verbal and nonverbal communication

authenticity will be explored in both the AI generated

avatar patient and the Live Actor avatar patient

scenarios.

2 INTERVENTIONS

In this pilot study two distinct VR platforms, with

different ways of animating avatars, were utilized to

explore preference. In both cases the same patient

information was used in the development of the

virtual patients. The patient was, CJ Williams, a

middle-aged man, who has been seeing a team of

interprofessional healthcare providers for balance,

coordination, memory issues, and has recently

received an unexpected diagnosis of multiple

sclerosis. The learning objectives for the scenario are

for the team of healthcare professionals to work as a

team and counsel CJ regarding the unexpected

diagnosis and obtain necessary information from CJ

to create a comprehensive treatment plan to meet his

needs.

2.1 Live Actor Avatar

The first VR Platform, ENGAGE

(Willmington, DE,

USA), utilizes live actor VR avatars to support human

actors who wear VR headsets to bring avatars to life

(Figure 1). The actor performed in a virtual

environment, and their basic head, eye, and arm

movements were captured in real-time to animate the

avatars using the Meta Quest Pro VR headset. The

same actor portrayed the avatar in all sessions. The

participants met in a conference room on campus and

interacted with the avatar in the ENGAGE

platform

using one computer to perform the telehealth

conference call.

Exploring Healthcare Virtual Simulation Modality Preferences an Interprofessional Learning Experience

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Figure 1: Live Actor Avatar Interface on EngageXR

2.2 AI Avatar

The second VR platform, VICTORY XR

(Davenport, IA, USA)

, which was used to develop the

AI avatar (CJ) interface and utilizes artificial

intelligence (AI) to animate avatars using the

ChatGPT 4.0 text to speech model. Internet download

speeds of over 50mbs and upload speeds over 15mbs

are required for operation of the AI avatar. CJ (Figure

2) was programmed to be pleasant and agreeable,

have knowledge of the signs and symptoms of

multiple sclerosis, and was given the same case

specific information as the Live Actor avatar. Avatar

graphics include simple head, face, eye and hand

movements that accompany the speech. While the AI

is processing a response CJ adopts a thinking posture

with his head down and hand on their chin, the avatar

takes anywhere from 6-10 seconds to respond. The

participants met with the avatar on campus in the

same conference room used for the Live Avatar.

Participants gathered and utilized one computer using

a direct web link to the telehealth session on device.

Figure 2: AI Avatar Interface on VictoryXR

3 METHODS

This study employed an embedded (QUAL/Quan)

counterbalanced mixed method approach (Creswell

et al., 2011) and University IRB approval was

obtained. Simulation best practices, as reported by

Violata et al., 2023, relating to outcomes, objectives,

simulation design and facilitation of a participant pre-

briefing and debriefing were included in this study.

Potential subjects were identified through

association with the university’s professional

healthcare and allied healthcare programs. Potential

subjects were sent the approved letter of solicitation

via email. Individuals that indicated interest in

participating in the study were sent an approved

informed consent. Once the informed consent was

received participants were sent information on

learning experience date for the 60 minutes on

campus commitment, a 20-minute online survey, and

15-minute case specific information designed to

prepare them for the simulated interaction. Subjects

were required to complete the online survey and the

case specific information prior to the on-campus

session.

All potential participants who agreed to complete

15-minute prep work, participate in the 60- minute in

person learning experience and complete the 20-

minute online post survey were invited to self-select

participating in one of two interprofessional group

sessions based upon their availability. Groups 1 (day

1 participants) and 2 (day 2 participants) were then

randomly assigned into either Case A (AI Avatar) and

Case B (Live actor Avatar) first. The simulated patient

experience order was counterbalanced and groups

completed both experiences on the same day in

alternate order, a rest period was provided between

sessions to minimize the potential for cognitive

fatigue.

On the scheduled testing day subjects reported to a

specific room on campus at a specified time. Upon

arrival, participants were assigned a subject number

by a member of the research team. The participants

were given 10 minutes to re-orientate the case

information and discuss the case with their IPE team

members. The participants had 30 minutes to complete

the simulated patient interaction. After the first

session, subjects completed a post simulation survey

(15-20 minutes) and took a scheduled 15-minute rest

break. Participants then engaged in the second 30-

minute patient simulation using the same case but

alternate simulation modality and completed the same

15- 20-minute post experience survey. The survey was

distributed via an online Qualtrics link containing

demographic questions, the revised Interprofessional

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Collaborative Competency Attainment Scale

(ICASS), six questions assessing their perception of

communication and three open-ended questions

exploring perceptions regarding their preferences with

avatar learning and if the experience (AI vs Live)

promoted their confidence, ability, and knowledge.

Descriptive statistics and frequencies were run on

all quantitative data collected. Differences between

subjects' responses for the ICAAS between simulation

rounds (AI avatar verse live actor avatar) were

assessed with non-parametric Wilcoxen signed ranks

test. Differences between the perception of

communication scale between simulation rounds (AI

avatar verse live actor avatar) were assessed using a

paired t-test and effect sizes were estimated with a

Cohen’s d. For the qualitative analysis, the PI

manually decoded and encoded the responses from the

survey's open-ended questions. The process of coding

employed first cycle coding practices described by

Saldana (2016). Specifically, the PI used in-vivo

coding, or direct quotes from the participants and

descriptive coding of brief phrases or words. Codes

were then arranged into categories. The PI created a

data codebook that was provided to the co-PIs for their

independent review of proposed codes, and categories.

Once a 100% intercoder agreement was established

for the codes and categories, the PI and co-PIs

generated consensus driven thematic analysis

statements addressing each of the research questions.

Once the PI and co-PIs analysed both data sets, they

converged both the quantitative and qualitative data to

create a better understanding of the participants'

responses.

3.1 Instruments

The learning experiences generated by the AI and live

Avatars were evaluated post interaction using the

validated revised ICASS, which is 21-item

questionnaire intended to measure interprofessional

communication and collaboration on a 5-point Likert

scale (1=poor, 2=fair, 3=good, 4= very good, and 5=

excellent), which has been used in similar avatar

simulations (Rippon et al., 2023). The subject

perception of the authenticity of the avatar

communication was evaluated using a 6-item survey,

created to assess perception of authenticity of verbal

and non-verbal communication, and was rated on a

sliding scale (0=not authentic to 100= completely

authentic). The perception of avatar communication

underwent face validity. Speech Language Pathology

(SLP) faculty were included in the face validation and

deemed all items pertaining to verbal and non-verbal

communication were important to include and easy to

assess. The 6-items demonstrated strong internal

consistency a priori (α = .951) and moderate inter-

rater reliability with intra-class correlation (ICC)

average measures (ICC= .745, p<.001). Items

pertaining to verbal communication (n=3) had a

strong inter-item correlation (r= .837-.877) and items

pertaining to non-verbal communication (n=3) had a

moderately strong inter-item correlation (r=.712-

.774).

3.2 Sample

There were 9 participants enrolled in this pilot study

(N=9). All were faculty in the health and allied health

professions (Athletic Training= 2, Physician

Assistant=1, Physical Therapy=3, Occupational

Therapy=1, Speech Language Pathology=2), with

over 2 years of teaching experience, and all (n=9) had

previous experience with patient care. Of the 9

participants 2 identified as male and 7 identified as

female, were between the ages of 35-55, and with no

formal training in VR simulation. Subjects self-

selected into group 1 (n=3) and group 2 (n=6).

4 RESULTS

All 9 participants completed both the AI avatar and

Live actor avatar interactions on the same day. There

were technical issues with the Wi-Fi during day one

of testing, which required the Live avatar encounter

to be delayed. Additionally, there were technical

issues with the AI avatar sessions, the system stopped

responding early in one encounter and the encounter

needed to be stopped and restarted. After restarting

the participants were able to complete the session in

its entirety.

4.1 Quantitative

For the AI avatar interaction subjects had a mean

perception of the avatar's verbal communication, with

100 indicating the highest level of authenticity, of

47.2 +33.9, a mean perception of the avatar

responding appropriately to questions of 56.4 +33.8,

and a mean perception of displaying appropriate

vocal characteristics (such as tone, rate of speech and

loudness) of 44.9 +27.9.

For the AI avatar interaction participants had a

mean perception of the avatar's non-verbal

communication of 47.2 +33.9, a mean perception of

displaying authentic facial responses of 31.8 +24.8,

and a mean perception of displaying authentic

emotions of 35.5 + 27.0.

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For the Live avatar interaction participants had a

mean perception of the avatar's verbal

communication, of 97.8 + 4.8, a mean perception of

the avatar responding appropriately to questions of

98.1 +2.6, and a mean perception of displaying

appropriate vocal characteristics (such as tone, rate of

speech and loudness) of 91.2 + 15.4. For the Live

avatar interaction participants had a mean perception

of the avatar's non-verbal communication of 63.6

+20.2, a mean perception of displaying authentic

facial responses of 59.1 +15.9, and a mean perception

of displaying authentic emotions of 84.9 +16.8.

For analysis of the difference in perception of the

avatar communication, due to the small sample size

both non-parametric and parametric t-tests were run.

Since the data was normally distributed and there was

a large effect size the parametric results are being

reported. Participants reported significant differences

in the authenticity of the AI and the Live avatar

communication with a paired t-test analysis.

Participants had a 41.7 +34.7 higher perception of the

Live Avatar responding appropriately to questions

(t=3.792, p=.004, 95% CI 16.8-66.5) and avatar

modality had a strong effect size (d=1.199).

Participants had a 27.3 +25.9 higher perception of the

Live Avatar displaying authentic facial responses

(t=3.331, p=.009), with avatar modality having a

strong effect size (d=1.053). Participants had a 49.4

+35.9 higher perception of the Live Avatar displaying

authentic emotions (t=4.341, p<,.001, 95% CI 23.6-

75.1), with avatar modality having a strong effect size

(d=1.373). Subjects had a 46.3 +29.6 higher

perception of the Live Avatar displaying appropriate

vocal characteristics (t=4.934, p<.001, 95%CI=25.1-

67.5), with avatar modality having a strong effect size

(d=1.560).Participants had a 50.6 +37.3 higher

perception of the Live Avatar displaying authentic

verbal communication (t=4.290, p<.001, 95%CI=

23.9-77.3) with avatar modality having a strong effect

size (d=1.357). Subjects had a 34.9 +32.0 higher

perception of the Live Avatar displaying authentic

non-verbal communication (t=3.445, p=.004,

95%CI= 11.9-57.8) with avatar modality having a

strong effect size (d=1.090).

Participants reported some differences between

the learning experiences, AI and Live Avatar, based

on the ICAAS. A Wilcoxen signed ranks test

indicated, compared to before the learning activities,

subjects had no significant difference between

perception of their ability to collaborate

interprofessional (p=.121). However, participants did

report, after the Live Avatar simulation, a

significantly higher perception of their ability to;

actively listen to IP team members’ ideas and

concerns (z=2.041, p=.041), provide constructive

feedback to IP team members during (z=1.994,

p=,046), learn with, from and about IP team members

to enhance care (z=2.410, p=.016), identify and

describe their abilities and contributions to the IP

team (z=2.428, p=.015), be accountable for their

contributions to the IP team (z=2.32, p=.026),

recognize how others’ skills and knowledge

complement and overlap with their own (z=2.157,

p=.031), use an IP team approach with the patient to

assess the health situation (z=2.379, p=.017), use an

IP team approach with the patient to provide whole

person care (z=2.716, p=.007), include the

patient/family in decision making (z=2.200, p=.028),

address conflict in a respectful manner (2.165,

p=.030), and develop an effective care plan with IP

team members (z=2.041, p=.041). There was no

significant difference between perception of their

ability following the AI and Live Avatar simulation

to; promote effective communication among

members of the IP team (p=.053), express their ideas

and concerns in a clear concise manner (p=.579), seek

out IP team members to address issues (p=.149), work

effectively with IP team members to enhance care

(p=.057), understand abilities and contributions of IP

team members (p=.055), actively listen to the

perspectives of IP team members (p=.141), take into

account the ideas of IP team members (p=.071), and

negotiate responsibilities with overlapping scopes of

practice (p=.123).

4.2 Qualitative

Table 1 provides the participants in vivo codes and

associated categories that emerged from responses to

the question asking them to share, their thoughts

regarding the AI Avatar Based Virtual Learning

Experience just completed and its impact on their

skills as a healthcare professional. Upon reviewing

the categories that emerged following participation in

the AI specific case scenario the thematic analysis

statement is proposed, faculty perceived that AI

Avatar Based Virtual Learning Experience promoted

collaboration amongst professionals, provided and

opportunity for practice, and assisted in supporting

person centred care practice. However, technical

issues were present that negatively impacted the

learning experiences. Ultimately, the AI avatar

scenario appeared less realistic.

Table 2 provides participants in vivo codes and

associated categories emerging from responses to the

question asking them to share, their thoughts

regarding the LIVE Avatar Based Virtual Learning

Experience

and its impact on their skills as a

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Table 1: Participants’ perceptions regarding AI Avatar Based Virtual Learning Experience.

Participant Please share with us your overall thoughts regarding the Avatar Based Virtual Learning Experience you

jus

completed and its impact on you

skills as a healthcare professional. In vivo codes

Category

F5 I felt that the use of VR enabled a greater humanistic aspect that made it easier for us to interact

both as a team and as 1:1 clinician with the patient

Realistic

F1 I liked this better as the patient was very realistic. Changes in voicetoneareimportant to

understanding the patient

Realistic

F2 The sim allowed me to confront a more genuine patient experience where not all my ideas would be

well received by the patient and forced me to offer a variety of solutions. It also encouraged patient

therapist collaboration.

Realistic

Collaboration

F4 This is a great way for students to practice interview and the clinical decision making Promotes

practice

F10 It was interesting, and the conversation flowed very easily. I appreciated hearing from my colleagues

and how they approach CJ differently and similarly to me. It was a great learning opportunity.

Communication

F6 I thought it went really well. I liked the overall mannerisms and discussion Communication

Realism

F9 Excellent, the software was very interactive and facilitated a logical flow of ideas. Communication

F11 This was my first interaction with CJ and felt that it was a great way to allow for interdisciplinary

work. Engaging in IP work enhances clinical skills and pushes your knowledge and communication

abilities. The Avatar Based experience allowed for a real-life simulation of a team meeting.

Communication

Realistic

F7 The client was extremely realistic. It felt like talking to a real patient. The responses were very real.

The questions were real. The client's tone of voice and the way they sounded unsure felt personal

rather than robotic.

Realistic

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895

healthcare professional. Upon reviewing the

categories that emerged following participation in the

LIVE specific case scenario the thematic analysis

statement is proposed, faculty perceived that LIVE

Avatar Based Virtual Learning Experience promoted

collaboration and communication among

professionals, provided an opportunity for practice,

and was realistic to person centred care.

5 DISCUSSION

This mixed method study examined participants’

perceived preferences of interactions with AI avatar

patients and Live Avatar patients, learning

experiences with the AI avatar patients and live

Avatar patients, and perceived differences of the

participants’ perceptions on overall communication

with both the AI and live Avatar patients. Interactions

were assessed according to verbal communication

when interacting with the AI and live avatars

respectively. Participants’ verbal communication

with the live avatar was preferred over verbal

communication with the AI avatar. Study participants

reported that the live avatar responded in a “socially

appropriate” manner to questions and displayed the

capability to vary vocal characteristics (tone, rate of

speech and volume of speech) that assisted to create

a realistic, patient conversation when compared to the

verbal interactions with the AI avatar. Additionally,

during the live avatar interactions, participants

perceived the avatar as displaying authentic emotions

and correspondingly reported the live avatar to

display authentic verbal and non-verbal

communication as well.

The avatar portrayed by the live actor offer a level

of realism because they mirror the movements and

utilize the voices supplied by real people. This

approach captures some nuances of human behaviour,

making interactions feel more genuine and

emotionally resonant (Hadhazy, 2022). Additionally,

live actor avatars provide a layer of emotional depth

and convey a wide range of emotions, which is crucial

for applications that require deep emotional

engagement, such as virtual therapy or immersive

storytelling (Hadhazy, 2022).

While AI avatars can perform a wide range of

actions without human intervention, (Kyrlitsias, et al.,

2022) and through the ability to react to

conversational inputs are thought to converse in a

natural and humanistic manner (Javaid et al., 2023)

The use of text to speech technology and the difficulty

of integrating Social Emotional Regulation (SER)

systems limits the ability of the avatar to perceive and

integrate non-verbal information in speech (Wani et

al., 2021) and respond appropriately.

Additionally, participants reported that after the

live avatar experience, they displayed significantly

improved perceptions of skills necessary for effective

interprofessional (IP) teamwork. Specifically,

participants reported perceived improved skills and

abilities when working with their IP colleagues

including: active listening, providing constructive

feedback , the ability to learn with, to, from and about

other professionals on the team so to enhance patient

care, describe their individual contributions to IP

care, be accountable for their individual contributions

to the IP team and use a team approach to develop a

comprehensive, person and family centred plan of

care.

This study findings further support and extend the

recent research examining simulation modalities in

healthcare education. Live avatars achieved

substantially higher authenticity ratings in verbal

communication and emotional expression. However,

this should not discount the utility of AI-based

simulation. Research examining AI Virtual Simulated

Patients (AI-VSP) found high acceptance rates

among diverse healthcare students (84-93%

recommendation rates), suggesting AI can serve as a

valuable supplementary training tool (De Mattei et

al., 2024; Lanza-Postigo et al., 2024). Therefore, the

use of AI avatars may be advantageous when more

traditional forms of simulation are not possible, or the

focus of the learning activity is skill development

requiring the capability to repeatedly practice the

same scenario.

Building on these findings, a 2024 study by

Vogelsang et al. on simulated learning experiences

involving immersive virtual reality has revealed

significant student improvements in self-efficacy,

particularly for specific clinical scenarios like

managing aggressive behaviours in dementia care.

Specifically in this study, the VR intervention group

showed statistically significant improvements

compared to controls, both immediate post-

intervention and following clinical rotations.

A critical theme emerging across recent studies,

and implied in this study, is the role of simulation in

developing socioemotional competencies. A recent

systematic review identified communication (34.4%)

and self-efficacy (30.5%) as the most frequently

trained skills during simulation experiences (Lanza-

Postigo et al., 2024). This aligns with findings from

both the AI avatar and VR studies, where

improvements in interprofessional communication

and self-confidence were consistently observed (De

Mattei et al., 2024; Vogelsang et al., 2024). While

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standardized patients (28.4%) and high-fidelity

simulation (26.1%) remain the most prevalent

modalities for socioemotional skills training (Lanza-

Postigo et al., 2024), our work and those of others in

VR, suggests emerging technologies like AI and VR

should complement, not replace, traditional

approaches. Specifically, AI and VR can reduce some

of the notable barriers including lack of time,

resources, financial cost, and workload issues (Al-

Ghareeb et al., 2016, Cooper etal., 2016) to

simulation in healthcare. The use of VR allows for

creation of virtual environments that may be costly to

reproduce in the physical world, varied physical

appearance, and for institutions without dedicated

simulation spaces to facilitate these interactions.

Learning objectives and resources need to be

considered when selecting VR simulation

technology. Based upon the results of this study, Live

actor avatar simulations were perceived to foster a

more collaborative and patient-centred care approach.

However, AI avatar experience may be preferable

when the learning experience is focused on skill

introduction, development, repeated practice and

scalability.

5.1 Limitations and Future Research

Limitations of this study included its small sample

size, which may affect the generalizability of the

results, and technical issues surrounding the AI avatar

experience which may have impacted participant’s

ability to achieve the learning outcomes. Although

the study employed randomization and

counterbalancing, there is always a potential for

sequencing effects and participant fatigue.

Additionally, given that the primary focus was to

assess individual perceptions, behavioural tracking

was not employed, thus may lead to social desirability

bias. Future research should focus on assessment of

engagement levels, speech analysis and avatar

response time. Future research should also employ a

longitudinal study to examine the perceptions of

interprofessional team skills post-simulation and

assess the long-term learning effects in healthcare

students.

6 CONCLUSIONS

This study demonstrated participants in virtual

simulation experiences preferring the bidirectional,

authentic communication offered by the live actor AI

avatar patients when compared to the AI driven avatar

patients. While the sample size was small, the large

effect size demonstrated a perceived value to both

types of avatar patient experiences with the

perceptions more favourable for simulating patient

centred interactions with the live actor avatars.

This synthesis suggests a future where

traditional and emerging simulation modalities work

in concert, each addressing specific learning

objectives while collectively providing

comprehensive preparation for clinical practice. The

challenge for educators lies in thoughtfully

integrating these approaches to maximize learning

outcomes while managing faculty abilities, student

learning preferences, educational and environmental

resource constraints.

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