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Explainable AI for Unsupervised Machine Learning: A Proposed Scheme Applied to a Case Study with Science Teachers

Topics: Adaptive Educational Systems; AI Literacy; Architectures for AI-based Educational Systems; Learning Analytics and Educational Data Mining; Next Generation Teaching and Learning Environments; Pedagogical Agents; Pre-K/K-12 Education; Synchronous and Asynchronous Learning; Theoretical Bases of E-Learning Environments; Universal Design for Learning

Authors: Yael Feldman-Maggor 1 ; Tanya Nazaretsky 2 and Giora Alexandron 1

Affiliations: 1 Department of Science Teaching, Weizmann Institute of Science, Rehovot, Israel ; 2 School of Computer and Communication Sciences, EPFL, Lausanne, Switzerland

Keyword(s): Explainable Artificial Intelligence, Clustering, Personalized Learning.

Abstract: Explainable Artificial Intelligence (XAI) seeks to render Artificial Intelligence (AI) models transparent and comprehensible, potentially increasing trust and confidence in AI recommendations. This research explores the realm of XAI within unsupervised educational machine learning, a relatively under-explored topic within Learning Analytics (LA). It introduces an XAI framework designed to elucidate clustering-based personalized recommendations for educators. Our approach involves a two-step validation: computational verification followed by domain-specific evaluation concerning its impact on teachers’ AI acceptance. Through interviews with K-12 educators, we identified key themes in teachers’ attitudes toward the explanations. The main contribution of this paper is a new XAI scheme for unsupervised educational machine-learning decision-support systems. The second is shedding light on the subjective nature of educators’ interpretation of XAI schemes and visualizations.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Feldman-Maggor, Y.; Nazaretsky, T. and Alexandron, G. (2024). Explainable AI for Unsupervised Machine Learning: A Proposed Scheme Applied to a Case Study with Science Teachers. In Proceedings of the 16th International Conference on Computer Supported Education - Volume 1: CSEDU; ISBN 978-989-758-697-2; ISSN 2184-5026, SciTePress, pages 436-444. DOI: 10.5220/0012687000003693

@conference{csedu24,
author={Yael Feldman{-}Maggor. and Tanya Nazaretsky. and Giora Alexandron.},
title={Explainable AI for Unsupervised Machine Learning: A Proposed Scheme Applied to a Case Study with Science Teachers},
booktitle={Proceedings of the 16th International Conference on Computer Supported Education - Volume 1: CSEDU},
year={2024},
pages={436-444},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012687000003693},
isbn={978-989-758-697-2},
issn={2184-5026},
}

TY - CONF

JO - Proceedings of the 16th International Conference on Computer Supported Education - Volume 1: CSEDU
TI - Explainable AI for Unsupervised Machine Learning: A Proposed Scheme Applied to a Case Study with Science Teachers
SN - 978-989-758-697-2
IS - 2184-5026
AU - Feldman-Maggor, Y.
AU - Nazaretsky, T.
AU - Alexandron, G.
PY - 2024
SP - 436
EP - 444
DO - 10.5220/0012687000003693
PB - SciTePress

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