# RAG-PRISM: A Personalized, Rapid, and Immersive Skill Mastery Framework with Adaptive Retrieval-Augmented Tutoring

arXiv: https://arxiv.org/abs/2509.00646 · HTML: https://mlsanity.com/paper/2509.00646/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Agentic RAG, agents and memory, one of 15 directions in Retrieval-augmented generation.
- Builds on 15 of its 36 references that are in the map.
- First posted on arXiv 31 Aug 2025.

## Placed in

- Retrieval-augmented generation / [Agentic RAG, agents and memory](https://mlsanity.com/rag/agentic-rag-agents-and-memory/)

## Builds on (most cited first, 15 of 15)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://mlsanity.com/paper/2004.12832/) (2020)
- [Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?](https://mlsanity.com/paper/2004.03685/) (2020)
- [A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2405.06211/) (2024)
- [The Faiss library](https://mlsanity.com/paper/2401.08281/) (2024)
- [Benchmarking Large Language Models in Retrieval-Augmented Generation](https://mlsanity.com/paper/2309.01431/) (2023)
- [HW-V2W-Map: Hardware Vulnerability to Weakness Mapping Framework for Root Cause Analysis with GPT-assisted Mitigation Suggestion](https://mlsanity.com/paper/2312.13530/) (2023)
- [Synchronous Faithfulness Monitoring for Trustworthy Retrieval-Augmented Generation](https://mlsanity.com/paper/2406.13692/) (2024)
- [Large Language Models and Attention-Based AI for Hardware Design and Security: Progress, Challenges, and Opportunities](https://mlsanity.com/paper/2504.08854/) (2025)
- [Personalized Education with Generative AI and Digital Twins: VR, RAG, and Zero-Shot Sentiment Analysis for Industry 4.0 Workforce Development](https://mlsanity.com/paper/2502.14080/) (2025)
- [Smart Environment for Adaptive Learning of Cybersecurity Skills](https://mlsanity.com/paper/2307.05281/) (2023)
- [Photogrammetry for Digital Twinning Industry 4.0 (I4) Systems](https://mlsanity.com/paper/2407.18951/) (2024)
- [PRISM: A Personalized, Rapid, and Immersive Skill Mastery framework for personalizing experiential learning through Generative AI](https://mlsanity.com/paper/2411.14433/) (2024)

## Built on by (most cited first, 1 of 1)

- [AI-accelerated End-to-End Framework for Rapid Professional Upskilling](https://mlsanity.com/paper/2607.14044/) (2026)

## Abstract (arXiv)

The rapid digital transformation of Fourth Industrial Revolution (4IR) systems is reshaping workforce needs, widening skill gaps, especially for older workers. With growing emphasis on STEM skills such as robotics, automation, artificial intelligence (AI), and security, large-scale re-skilling and up-skilling are required. Training programs must address diverse backgrounds, learning styles, and motivations to improve persistence and success, while ensuring rapid, cost-effective workforce development through experiential learning. To meet these challenges, we present an adaptive tutoring framework that combines generative AI with Retrieval-Augmented Generation (RAG) to deliver personalized training. The framework leverages document hit rate and Mean Reciprocal Rank (MRR) to optimize content for each learner, and is benchmarked against human-generated training for alignment and relevance. We demonstrate the framework in 4IR cybersecurity learning by creating a synthetic QA dataset emulating trainee behavior, while RAG is tuned on curated cybersecurity materials. Evaluation compares its generated training with manually curated queries representing realistic student interactions. Responses are produced using large language models (LLMs) including GPT-3.5 and GPT-4, assessed for faithfulness and content alignment. GPT-4 achieves the best performance with 87% relevancy and 100% alignment. Results show this dual-mode approach enables the adaptive tutor to act as both a personalized topic recommender and content generator, offering a scalable solution for rapid, tailored learning in 4IR education and workforce development.