Abstract: This study presents the Retrieval-Augmented Pedagogical Assistant (RAPA) methodology, an integrated framework designed to overcome the core limitations of general Large Language Models (LLMs)—specifically factual instability (hallucination) and static knowledge bases—by deploying a specialized, institutional Retrieval-Augmented Generation (RAG) architecture. The methodology addresses three critical challenges to the responsible integration of AI in higher education. Firstly, the framework ensures data sovereignty and sustainable deployment by mandating a comprehensive Total Cost of Ownership (TCO) analysis. This analysis validates the strategic necessity of local RAG hosting and of leveraging computational efficiencies, such as Parameter-Efficient Fine-Tuning (PEFT) and PROXIMITY caching, to ensure a cost-effective solution that strictly complies with FERPA and GDPR data protection mandates and mitigates security risks associated with data leakage. Secondly, the framework ensures the equitable integration of AI literacy across disciplines with varying technological resources, particularly in the Humanities and Vocational Education and Training (VET). This is achieved by minimizing technical prerequisites and institutionalizing continuous Professional Development (PD) through the Dialogic Video Cycle (DVC), which trains faculty in Prompt Engineering to embed individualized pedagogical rules and ethical constraints into the RAPA’s architecture. Finally, specific measures are implemented to evaluate the development of Critical Thinking (CT). RAPA outputs are architecturally constrained to include transparent Chain-of-Thought (CoT) reasoning and verifiable source citations. Student Critical AI Analysis Assignments require students to critique the AI's synthesis, identifying inaccuracies, biases, or limitations. The effectiveness of this assessment is quantified using a quasi-experimental design and technical RAGAS metrics, such as Faithfulness and Context Precision, ensuring a verifiable shift from passive knowledge consumption to active, informed critique. Key findings from the preliminary architectural validation indicate that integrating Proximity-LSH caching reduced database retrieval calls by 77.2% and retrieval latency by approximately 72.5%, while maintaining high retrieval recall, addressing the scalability bottleneck inherent in high-volume educational deployments. Furthermore, the application of Robust Fine-Tuning (RbFT) demonstrated a marked improvement in the system's resilience to noisy educational data, preventing performance degradation where standard RAG models typically fail when exposed to irrelevant or counterfactual document chunks. These technical optimizations directly support the pedagogical objective by ensuring that the AI assistant remains responsive and factually grounded.

Shohel Pramanik and Mohd Heikal Bin Husin. “The Retrieval-Augmented Pedagogical Assistant (RAPA): A Methodology for Enhancing Critical Thinking and Equity in AI-Augmented Education”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.12 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0161226

@article{Pramanik2025,
title = {The Retrieval-Augmented Pedagogical Assistant (RAPA): A Methodology for Enhancing Critical Thinking and Equity in AI-Augmented Education},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0161226},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0161226},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {12},
author = {Shohel Pramanik and Mohd Heikal Bin Husin}
}