# Explainable AI in Education: Techniques and Qualitative Assessment
###### Abstract  
Many of the articles on AI in education compare the performance and fairness of different models, but few specifically focus on quantitatively analyzing their explainability. To bridge this gap, we analyzed key evaluation metrics for two machine learning models--ANN and DT--with a focus on their performance and explainability in predicting student outcomes using the OULAD. The methodology involved evaluating the DT, an intrinsically explainable model, against the more complex ANN, which requires post hoc explainability techniques. The results show that, although the feature-based and structured decision-making process of the DT facilitates natural interpretability, it struggles to model complex data relationships, often leading to misclassification. In contrast, the ANN demonstrated higher accuracy and stability but lacked transparency. Crucially, the ANN showed great fidelity in result predictions when it used the LIME and SHAP methods. The results of the experiments verify that the ANN consistently outperformed the DT in prediction accuracy and stability, especially with the LIME method. However, improving the interpretability of ANN models remains a challenge for future research. ANN; decision tree; fidelity; stability; LIME; SHAP; feature importance +
Footnote †: 2020: _Applied_  
and make sure that forecasts are transparent, comprehensible, and reliable, the majority of the work in the field of ML explanation research is concentrated on developing new methods and approaches [3]. XAI techniques are important in every field since they aim to define understanding and results of AI systems [4]. Initially, explainable approaches in the education domain were maonly focused on model-specific methods like Decision Tree (DT) models [5; 6; 7]. It was simple for analysts and educators to comprehend the logic behind the forecasts since these models are naturally interpretable. Despite this, the necessity for increasingly advanced models became apparent as the quantity and complexity of educational data expanded. To handle complex datasets and increase prediction accuracy, nonlinear black box models have arisen, such as ensemble approaches and deep learning. For instance, [PERSON] et al. [8] noted that neural network models had the best prediction accuracy, whereas DTs had the second highest. Although these models performed better, their underlying workings were significantly less transparent than those from previous models, which presented interpretability issues. Because of their high performance, black box models such as deep learning offer great promise for the educational field [9; 10]. The widely used post hoc methods, SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), improve the interpretability of such black box ML models. By exposing the inner workings of black box models, these techniques help both educators and learners gain a deeper understanding of how certain predictions are formed [11; 12]. As deep learning models become increasingly popular in education, there is a clear need for evaluations that measure the effectiveness of explanatory methods and determine what explanations are best suited for specific instances. Nevertheless, there is not yet a widely accepted set of metrics to assess the quality of explanation methods in ML, complicating the comparison of various approaches [3; 11; 13]. This challenge arises because explainability is inherently subjective; definitions of a \"good\" explanation vary based on context, analytical objectives, and target audiences. Such variability makes it difficult to objectively compare explanation techniques across diverse applications. Consequently, assessing model explainability in various contexts could benefit from a more scalable and consistent approach. Despite the presence of numerous articles comparing the performance of AI models in education, there are only a few that focus on the quantitative assessment of explainability [13; 14; 15; 16].