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All Journal Journal of Information Systems and Informatics Journal of Computing Theories and Applications
Evwiekpaefe, Abraham Eseoghene
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Computer Science & IT Decision Sciences, Operations Research & Management

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Journal : Journal of Computing Theories and Applications

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The Llama–ARCS Adaptive Learning framework: AI–VR Integration System for Real-Time Motivational Feedback in Higher Education Evwiekpaefe, Abraham Eseoghene; Chinyio, Darius Tienhus; Tohomdet, Loreta Katok
Journal of Computing Theories and Applications Vol. 3 No. 3 (2026): JCTA 3(3) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.15031

Abstract

This study developed and evaluated an AI-integrated Virtual Reality (VR) system designed to enhance personalized learning in higher education. While VR improves engagement, existing systems often lack adaptivity or experience high latency during AI interactions. To address these limitations, this research introduces a novel integration of a cache-optimized Llama 2 Large Language Model (LLM) that delivers real-time, motivationally grounded feedback. The system was implemented using Unity 3D and validated with 50 undergraduate students. Technical validation showed that the cache layer reduced interaction latency from 17.7 ms to 14.2 ms and maintained zero system crashes throughout the pilot. Learner motivation was assessed using Keller’s ARCS model, yielding mean scores ranging from 4.08 to 4.69 across all dimensions. Independent t-tests (p > 0.05) and negligible effect sizes (Cohen’s d < 0.2) revealed no significant difference between technical (ICT) and non-technical (Physics) students. These findings confirm that the proposed system effectively bridges technological and motivational gaps, providing a robust model for adaptive, immersive education.
Co-Authors Ajakaiye, Fiyinfoluwa Aleke, Paschal Obioma Chinyio, Darius Tienhua Chinyio, Darius Tienhus Lawi, Valerie Plangnan Tohomdet, Loreta Katok