AI-Powered Analysis
Assessing Student Acceptance of an LLM-Integrated VR Public Speaking Simulation via Extended UTAUT
This study investigates the acceptance of an LLM-integrated VR public speaking simulation by students using the Extended UTAUT model. Key findings indicate that effort expectancy, facilitating conditions, and hedonic motivation significantly predict behavioral intention. Performance expectancy was not a significant predictor. Academic major and GPA level were identified as significant antecedent variables. The research provides theoretical and practical implications for deploying novel technologies in educational settings.
Executive Impact: Key Metrics & Insights
Quantifiable insights into the core findings and their potential impact on your enterprise operations.
Participants
Public Speaking Sections
LLM-Integrated VR System
Speech Scenarios
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Technology Acceptance
VR Simulation Methodology
Antecedent Variables
Variance in Behavioral Intention Explained by UTAUT Model
77.5% Variance in Behavioral Intention Explained by UTAUT Model| Determinant | Significance (p-value) | Result |
|---|---|---|
| Performance Expectancy (PE) | 0.052 | Rejected (H1) |
| Effort Expectancy (EE) | 0.015 | Accepted (H2) |
| Facilitating Conditions (FC) | <0.001 | Accepted (H3) |
| Hedonic Motivation (HM) | <0.001 | Accepted (H4) |
Enterprise Process Flow
Record Speech (Unity with Meta Quest 3)
→
Azure Speech to Text (STT)
→
ChatGPT-4 Evaluation
→
Azure Text to Speech (TTS)
Impact of Streamlined Feedback Cycle
The near-instant feedback loop (speech capture → transcription → ChatGPT critique → text-to-speech delivery) compresses practice and reflection from minutes to seconds, signaling tangible time savings.
This streamlining collectively lowered the perceived effort required to gain meaningful practice benefits, thereby boosting student motivation to continue using the system. The feedback was found to be 'straightforward and easy to understand' and 'smooth operation'.
Our findings confirm the critical role of Effort Expectancy in technology adoption and highlight the value of streamlining interaction and feedback cycles when designing AI-VR learning simulations.
| Antecedent Variable | UTAUT Construct | Significance (p-value) | Result |
|---|---|---|---|
| Academic Major | Performance Expectancy (PE) | 0.020 | Accepted (H7) |
| Academic Major | Effort Expectancy (EE) | 0.046 | Accepted (H8) |
| Academic Major | Facilitating Conditions (FC) | 0.124 | Rejected (H9) |
| Academic Major | Hedonic Motivation (HM) | 0.051 | Rejected (H10) |
| GPA Level | Performance Expectancy (PE) | 0.063 | Rejected (H11) |
| GPA Level | Effort Expectancy (EE) | 0.417 | Rejected (H12) |
| GPA Level | Facilitating Conditions (FC) | 0.494 | Rejected (H13) |
| GPA Level | Hedonic Motivation (HM) | 0.027 | Accepted (H14) |
Academic Major's Influence on Perceived Usefulness and Ease of Use
Students majoring in communication reported significantly higher PE and EE scores than students from other majors. This aligns with earlier work showing that domain expertise amplifies perceptions of performance expectancy and usability when individuals encounter novel technologies.
Communication majors treated the simulation principally as an additional practice tool that is both useful and familiar.
In contrast, non-communication majors, with less exposure to public speaking, regarded it as less valuable and more difficult to perform speeches.
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Your AI Implementation Roadmap
A phased approach to integrate and scale AI solutions within your organization, inspired by the research.
Phase 1: Initial Deployment & User Feedback
Deploy the LLM-integrated VR simulation to a pilot group, collecting initial user feedback and performance data to inform early iterations.
Phase 2: Feature Expansion & Integration
Integrate advanced features like eye-tracking and hand-tracking, and expand to cover more diverse speech topics and scenarios based on initial feedback.
Phase 3: Scaled Rollout & Long-term Impact Assessment
Roll out the system across multiple academic sections and institutions, incorporating AI-generated scores into formal assessments to measure sustained pedagogical impact.
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