ORCID
- Keith Nolan: 0000-0001-7974-4253
Abstract
An alarming decline in Gaeilge proficiency has renewed education efforts, fueled by the rise of AI in recent years. This research presents the development of a deep learning framework designed to evaluate spoken Gaeilge (henceforth Irish) proficiency of learners by quantifying student voice sample similarity to everyday Irish speaker truth samples, combined with explainable AI (XAI) techniques used to generate proof-of-concept student-friendly feedback. While automatic speech recognition (ASR) systems for high-resource languages like English have access to large, detailed datasets, the Irish language relies on a limited availability of speech resources, that challenges traditional model development. We address these gaps through a human-centered approach that employs a triplet-loss based Convolutional Neural Network (CNN) using spectrograms for similarity scoring, and XAI methods like using Mel-frequency cepstral coefficients (MFCCs) and Gradient-weighted Class Activation Mapping (Grad-CAM) to compare and contrast student attempts. Our evaluation combines technical metrics with visualisations, to make the learning process intuitive and simple for students. Results demonstrate that the model can successfully identify the similarity between student samples and truth samples, supplemented by power-transformed similarity scoring to help interpretability. This work contributes a scalable foundation for low-resource language education, demonstrating the potential for AI-assisted tools to aid in preserving languages like Irish while positioning XAI as a bridge between deep learning techniques and personalised, accessible learning for all students.
Keywords
Convolutional Neural Network, Educational AI, Explainable AI, Irish Language, Speech Recognition, Triplet Loss
DOI Link
Publication Date
2026-02-16
Event
3rd International Conference on Human-Centred AI - Education and Practice, HCAI-ep 2026
Publication Title
HCAI-ep 2026 - Proceedings of the 2026 Conference on Human Centered Artificial Intelligence - Education and Practice
Publisher
Association for Computing Machinery (ACM)
ISBN
9798400721533
First Page
8
Last Page
14
Deposit Date
2026-05-14
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Additional Links
Recommended Citation
Johnson, Ben and Nolan, Keith, "Translating Low-Level Features into Student Friendly Explanations using XAI" (2026). Research Outputs: 2025-Present. 3.
https://arrow.tudublin.ie/faccomentro/3