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 📅 2025 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. (2025). 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, 2025.

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, 2025.

Evwiekpaefe, et al. (2025) '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. 2025;3(3).

An Attention-Enhanced CNN–RBF Framework for Network Intrusion Detection in Imbalanced Traffic

Kabura, Fabrice; Nsabimana, Thierry

A Lightweight Maize Leaf Disease Recognition Using PCA-Compressed MobileNetV2 Features and RBF-SVM

Abubakar, Mustapha; Ibrahim, Yusuf; Ajayi, Ore-Ofe; Saminu, Sani Saleh

Multimodal Deep Learning for Pneumonia Detection Using Wearable Sensors: Toward an Edge-Cloud Framework

Ibam, Emmanuel Onwako; Oluwagbemi, Johnson Bisi

Android Malware Detection Using Machine Learning with SMOTE-Tomek Data Balancing

Masari, Maryam Sufiyanu; Danladi, Maiauduga Abdullahi; Onyinye, Ilori Loretta; Tohomdet, Loreta Katok

Hybrid Real-time Framework for Detecting Adaptive Prompt Injection Attacks in Large Language Models

Prakash, Chandra; Lind, Mary; De La Cruz, Elyson

Integrating Fully Homomorphic Encryption and Zero-Knowledge Proofs for Efficient Verifiable Computation

Qureshi, UmmeAmmara; Doshi, Bhumika; More, Aditya; Joshi, Kashyap; Kumar, Kapil

Tren Sitasi Jurnal