SAGE-RAI: Design Patterns for Transparent RAG Systems

Retrieval-Augmented Generation (RAG) systems are increasingly deployed in web-based educational environments, yet transparency can be seen as a primarily ethical, and, too often, optional, concern, rather than foundational. This paper presents design patterns for building transparent RAG systems, derived from developing and deploying SAGE-RAI, an advanced multi-purpose RAG system, in an educational context. Through systematic evaluation combining quantitative rating data (n=26, mean rating=4.62/5) and qualitative interviews (n=4), we demonstrate that transparency serves dual pedagogical and ethical functions. Our empirical findings reveal high user satisfaction (92.3% rating 4-5 stars) while identifying critical tensions between AI assistance and learning independence. Our findings suggest that as RAG systems increasingly mediate access to web-based knowledge, transparency must evolve from an optional feature to an architectural requirement.

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Publication Details

Journal
Open Research Online (The Open University)
Published
2026-04-12
Primary Topic
Intelligent Tutoring Systems and Adaptive Learning
Type
article
Field-Weighted Citation Impact
0.00

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article

SAGE-RAI: Design Patterns for Transparent RAG Systems

Alexander Mikroyannidis, Allan Third, John Domingue, Joseph Kwarteng et al.
Open Research Online (The Open University)
Intelligent Tutoring Systems and Adaptive Learning
article

SAGE-RAI: Design Patterns for Transparent RAG Systems

Alexander Mikroyannidis, Allan Third, John Domingue, Joseph Kwarteng, David Tarrant
article en

Abstract

Retrieval-Augmented Generation (RAG) systems are increasingly deployed in web-based educational environments, yet transparency can be seen as a primarily ethical, and, too often, optional, concern, rather than foundational. This paper presents design patterns for building transparent RAG systems, derived from developing and deploying SAGE-RAI, an advanced multi-purpose RAG system, in an educational context. Through systematic evaluation combining quantitative rating data (n=26, mean rating=4.62/5) and qualitative interviews (n=4), we demonstrate that transparency serves dual pedagogical and ethical functions. Our empirical findings reveal high user satisfaction (92.3% rating 4-5 stars) while identifying critical tensions between AI assistance and learning independence. Our findings suggest that as RAG systems increasingly mediate access to web-based knowledge, transparency must evolve from an optional feature to an architectural requirement.

Open Research Online (The Open University)
UK Research and Innovation
Quality Education
Openalex Percentile: Top 83%
Intelligent Tutoring Systems and Adaptive Learning
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