The integration of AI systems in education faces significant challenges in terms of transparency and accountability. Here, we propose a two-step methodology that distinguishes between epistemic and pragmatic applications, involving human experts in the process. We conduct a case study focused on predicting low student achievement, using a large Italian dataset and employing advanced machine learning techniques. Our experimental design incorporates data-driven and theory-driven approaches within the framework of Informed Machine Learning, aiming to improve both predictive performance and explainability.
A 2-step methodology for XAI in education / Francesco Balzan, Andrea Zanellati, Stefano Pio Zingaro, Maurizio Gabbrielli. - ELETTRONICO. - (2023), pp. 1-7. (Intervento presentato al convegno European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases tenutosi a Torino, Italy nel 18/09/2023).
A 2-step methodology for XAI in education
Francesco Balzan
Primo
Writing – Original Draft Preparation
;Andrea ZanellatiSecondo
Writing – Original Draft Preparation
;Stefano Pio ZingaroPenultimo
Writing – Original Draft Preparation
;Maurizio GabbrielliUltimo
Writing – Review & Editing
2023
Abstract
The integration of AI systems in education faces significant challenges in terms of transparency and accountability. Here, we propose a two-step methodology that distinguishes between epistemic and pragmatic applications, involving human experts in the process. We conduct a case study focused on predicting low student achievement, using a large Italian dataset and employing advanced machine learning techniques. Our experimental design incorporates data-driven and theory-driven approaches within the framework of Informed Machine Learning, aiming to improve both predictive performance and explainability.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
