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.

翻译：人工神经网络（ANNs）已成为教育应用中最重要的人工智能（AI）技术之一，能够提供自适应教育服务。然而，其教育潜力在实践中受到三个主要挑战的限制：i）难以在开发过程中融入符号化教育知识（如因果关系和实践者知识），ii）学习与反映偏差，以及iii）缺乏可解释性。鉴于教育领域的高风险特性，将教育知识整合到ANN中对于开发符合基本教育限制且能提供预测可解释性的AI应用至关重要。本研究论证神经符号AI家族具有解决上述挑战的潜力。为此，我们调整了神经符号AI框架，并相应开发了名为NSAI的方法，该方法能够将教育知识注入深度神经网络并从中提取知识，用于建模学习者的计算思维。实验结果表明，与仅基于训练数据训练的深度神经网络以及通过SMOTE和自动编码器方法增强训练数据的模型相比，NSAI方法具有更好的泛化能力。更重要的是，与其他模型不同，NSAI方法优先考虑能够捕捉输入特征与输出标签之间因果关系的鲁棒表示，从而确保学习过程中的安全性，避免虚假相关性并控制训练数据中的偏差。此外，NSAI方法还能从学习到的网络中提取规则，促进对预测路径的解释与推理，并完善初始教育知识。这些发现表明，神经符号AI能够克服ANNs在教育领域的局限性，实现可信赖且可解释的应用。