An Automated Virtual Reality Training System for Teacher-Student Interaction: A Randomized Controlled Trial

Abstract

Background Shortages in qualified supervision and other resources prevent education personnel from rehearsing effective practices. Interactive simulations, although increasingly used in education, frequently require instructor management. Automated simulations rarely engage trainees in skills related to practice (eg, speech). Objective We evaluated the capability of delivering behavioral skills training through an automated virtual reality (VR) simulation using artificial intelligence to improve the implementation of a nondirective mathematical questioning strategy. Methods We recruited and randomly assigned 30 college-aged participants to equivalent treatment (ie, lecture, modeling, and VR; 15/30, 50%) and control groups (ie, lecture and modeling only; 15/30, 50%). The participants were blind to treatment conditions. Sessions and assessments were conducted face to face and involved the use of VR for assessment regardless of the condition. Lessons concerned the use of a nondirective mathematical questioning strategy in instances where a simulated student provided correct or incorrect answers to word problems. The measures included observed and automated assessments of participant performance and subjective assessments of participant confidence. The participants completed the pretest, posttest, and maintenance probes each week over the course of 3 weeks. Results A mixed ANOVA revealed significant main effects of time (F2,27=124.154; P<.001; ηp2=0.816) and treatment (F1,28=19.281; P<.001; ηp2=0.408) as well as an interaction effect (F2,28=8.429; P<.001; ηp2=0.231) for the average percentage of steps in the questioning procedure. Posttest scores for the intervention group (mean 88%, SD 22.62%) exceeded those of the control group (mean 63.33%, SD 22.64%), with t28=3.653, P<.001, and Cohen d=1.334. Maintenance scores indicated a positive effect of the intervention (mean 83.33%, SD 24.40%) relative to the control (mean 54.67%, SD 15.98%), t28=3.807, P<.001, Cohen d=1.39. A Mann-Whitney U test indicated that the treatment groups’ self-ratings of confidence (mean 2.41, SD 0.51) were higher than those of the control group (mean 2.04, SD 0.52), U=64, P=.04, r=0.137. Conclusions The results demonstrate the potential of artificial intelligence-augmented VR to deliver effective, evidence-based training with limited instructor management. Additional work is needed to demonstrate the cascading effect of training on authentic practice and to encompass a wider range of skills.

The Scope of Virtual Reality Simulators in Radiology Education: Systematic Literature Review

“…The VR modalities used in some of the studies allowed remote assistance from an external agent (teacher), as the VR training is conducted in front of a screen while being part of a team, with the teacher making constant corrections and indications [ 22 24 27 ]. However, researchers are looking into VR systems with artificial intelligence–supported tutoring, which includes the assessment of learners, generation of learning content, and automated feedback [ 39 ].…”

Investigating influencing factors of learning satisfaction in AI ChatGPT for research: University students perspective

“… Constructs No. of Items Piloting Results Source of adoption Perceived Ease of Use (PEU) 5 0.85 [ 62 , 63 , 77 ] ChatGPT Use (GPTU) 5 0.80 [ 62 , 63 , 77 ] Perceived Usefulness (PU) 5 0.88 [ 49 , 63 , 77 , 93 ] interaction learning (INL) 5 0.75 [ [94] , [95] , [96] ] Information Quality (INQ) 5 0.82 [ 53 , 68 ] Interaction Quality (IQ) 4 0.79 [ 56 , 97 ] Collaborative learning (CL) 3 0.81 [ 47 , 62 , 73 ] Learning Motivation (LM) 5 0.90 [ 77 ] Learning Satisfaction (LS) 5 0.87 [ 63 , ...…”

Features of adaptive training algorithms for improved complex skill acquisition

“…Mixed ANOVA was used for analysis as the method has been widely employed in studies that assess skill transfer from virtual to physical environments, as well as to assess attributes of the Flow Short Scale surveys (Wilbert et al, 2010;Francis et al, 2020;Cooper et al, 2021;King et al, 2022). All analyses were performed using R version 4.3.0 (R Core Team, 2023) and Python 3.10.6 Jones et al, 2001;McKinney, 2010;Vallat, 2018;Wickham et al, 2019. 3 Results…”