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Article overview
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Augmenting deep neural networks with symbolic knowledge: Towards trustworthy and interpretable AI for education | | Danial Hooshyar
; Roger Azevedo
; Yeongwook Yang
; | | Date: |
1 Nov 2023 | | Abstract: | Artificial neural networks (ANNs) have shown to be amongst the most important
artificial intelligence (AI) techniques in educational applications, providing
adaptive educational services. However, their educational potential is limited
in practice due to three major challenges: i) difficulty in incorporating
symbolic educational knowledge (e.g., causal relationships, and practitioners’
knowledge) in their development, ii) learning and reflecting biases, and iii)
lack of interpretability. Given the high-risk nature of education, the
integration of educational knowledge into ANNs becomes crucial for developing
AI applications that adhere to essential educational restrictions, and provide
interpretability over the predictions. This research argues that the
neural-symbolic family of AI has the potential to address the named challenges.
To this end, it adapts a neural-symbolic AI framework and accordingly develops
an approach called NSAI, that injects and extracts educational knowledge into
and from deep neural networks, for modelling learners computational thinking.
Our findings reveal that the NSAI approach has better generalizability compared
to deep neural networks trained merely on training data, as well as training
data augmented by SMOTE and autoencoder methods. More importantly, unlike the
other models, the NSAI approach prioritises robust representations that capture
causal relationships between input features and output labels, ensuring safety
in learning to avoid spurious correlations and control biases in training data.
Furthermore, the NSAI approach enables the extraction of rules from the learned
network, facilitating interpretation and reasoning about the path to
predictions, as well as refining the initial educational knowledge. These
findings imply that neural-symbolic AI can overcome the limitations of ANNs in
education, enabling trustworthy and interpretable applications. | | Source: | arXiv, 2311.00393 | | Services: | Forum | Review | PDF | Favorites | |
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