Trusting the Explainers: Teacher Validation of Explainable Artificial
Intelligence for Course Design

Trusting the Explainers: Teacher Validation of Explainable Artificial Intelligence for Course Design

17 December 2022

Mirko Marras

Papers citing "Trusting the Explainers: Teacher Validation of Explainable Artificial Intelligence for Course Design"

6 / 6 papers shown

Title
A constraints-based approach to fully interpretable neural networks for detecting learner behaviors Juan D. Pinto Luc Paquette 43 0 0 10 Apr 2025
Towards a Unified Framework for Evaluating Explanations Juan D. Pinto Luc Paquette 22 1 0 22 May 2024
ClickTree: A Tree-based Method for Predicting Math Students' Performance Based on Clickstream Data Narjes Rohani Behnam Rohani A. Manataki 16 1 0 01 Mar 2024
InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts Vinitra Swamy Syrielle Montariol Julian Blackwell Jibril Frej Martin Jaggi Tanja Kaser 31 3 0 05 Feb 2024
MultiModN- Multimodal, Multi-Task, Interpretable Modular Networks Vinitra Swamy Malika Satayeva Jibril Frej Thierry Bossy Thijs Vogels Martin Jaggi Tanja Kaser Mary-Anne Hartley 18 11 0 25 Sep 2023
The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations Vinitra Swamy Jibril Frej Tanja Kaser 23 14 0 01 Jul 2023