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 📅 2026 Vol 3, No 3 🔗 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.
Keywords
Adaptive Learning; Artificial Intelligence; Higher Education; Learning Analytics; Llama-ARCS; Motivational Theory; Real-Time Feedback; Virtual Reality
How to Cite

Evwiekpaefe, et al. (2026). The Llama–ARCS Adaptive Learning framework: AI–VR Integration System for Real-Time Motivational Feedback in Higher Education. Journal of Computing Theories and Applications, 3(3). https://doi.org/10.62411/jcta.15031

Evwiekpaefe, Abraham Eseoghene; Chinyio, Darius Tienhus; Tohomdet, Loreta Katok, "The Llama–ARCS Adaptive Learning framework: AI–VR Integration System for Real-Time Motivational Feedback in Higher Education," Journal of Computing Theories and Applications, vol. 3, no. 3, 2026.

Evwiekpaefe, Abraham Eseoghene; Chinyio, Darius Tienhus; Tohomdet, Loreta Katok. "The Llama–ARCS Adaptive Learning framework: AI–VR Integration System for Real-Time Motivational Feedback in Higher Education." Journal of Computing Theories and Applications, vol. 3, no. 3, 2026.

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

Evwiekpaefe, et al. (2026) 'The Llama–ARCS Adaptive Learning framework: AI–VR Integration System for Real-Time Motivational Feedback in Higher Education', Journal of Computing Theories and Applications, 3(3). doi: 10.62411/jcta.15031.

Evwiekpaefe, Abraham Eseoghene; Chinyio, Darius Tienhus; Tohomdet, Loreta Katok. The Llama–ARCS Adaptive Learning framework: AI–VR Integration System for Real-Time Motivational Feedback in Higher Education. Journal of Computing Theories and Applications. 2026;3(3).

Artikel Terkait

A Composite Centrality Framework for Evacuation Planning in Meso-Scale Spatial Networks with Semi-Structured Connectivity

Santoso, Jaya; Muliyana, Ana; Saragih, Asido; Pakpahan, Ridho; Chrisinta, Debora

Sentence-Level Sentiment Analysis of Indonesian App Reviews Using IndoBERTweet

Aqiilah, Inge Najwa; Saptono, Ristu; Syaifuddin, Akhmad

A Systematic Review of Agentic AI in Healthcare: An Evidence-Informed Seven-Principle Framework

Prakash, Chandra; Sisodia, Avneesh ; Lind, Mary

A Systematic Literature Review of Robustness-Aware Batik Motif Classification: Acquisition Variability, Feature Representation, and Learning Models

Priyambodo, Aji; Isnanto, R. Rizal; Sanjaya, Ridwan

YOLOv9s with Region-Dispersion Channel Spatial Attention for Robust Chili Leaf Disease Detection

Hidayat, Miwan Kurniawan; Na'am, Jufriadif; Ernawan, Ferda

Understanding Statistical and Temporal Representations for Large-Scale IoT DDoS Detection Through Ablation-Driven Analysis

Wicaksono, Daniel Nomolas; Setiadi, De Rosal Ignatius Moses; Susanto, Ajib; Harkespan, Imanuel; Mohamed, Mohamad Afendee; Sambas, Aceng
Tren Sitasi Jurnal