TY - GEN
T1 - Explainable AI for Unsupervised Machine Learning
T2 - 16th International Conference on Computer Supported Education, CSEDU 2024
AU - Feldman-Maggor, Yael
AU - Nazaretsky, Tanya
AU - Alexandron, Giora
N1 - Publisher Copyright: Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85193954698&partnerID=8YFLogxK
U2 - https://doi.org/10.5220/0012687000003693
DO - https://doi.org/10.5220/0012687000003693
M3 - منشور من مؤتمر
T3 - International Conference on Computer Supported Education, CSEDU - Proceedings
SP - 436
EP - 444
BT - Proceedings of the 16th International Conference on Computer Supported Education, CSEDU 2024
A2 - Poquet, Oleksandra
A2 - Ortega-Arranz, Alejandro
A2 - Viberg, Olga
A2 - Chounta, Irene-Angelica
A2 - McLaren, Bruce
A2 - Jovanovic, Jelena
Y2 - 2 May 2024 through 4 May 2024
ER -