The Controller Area Network (CAN) facilitates efficient communication among vehicle components. While it ensures fast and reliable data transmission, its lightweight design makes it susceptible to data manipulation in the absence of security layers. To address these vulnerabilities, machine learning (ML)-based intrusion detection systems (IDS) have been developed and shown to be effective in identifying anomalous CAN traffic. However, these models often function as black boxes, offering limited transparency into their decision-making processes, which hinders trust in safety-critical environments. To overcome these limitations, this paper proposes a novel method that combines Local Interpretable Model-agnostic Explanations (LIME) with a vision-language model (VLM) to generate detailed textual interpretations of an ML-based CAN IDS. This integration mitigates the challenges of visual-only explanations in traditional XAI and enhances the intuitiveness of IDS outputs. By leveraging the multimodal reasoning capabilities of VLMs, the proposed method bridges the gap between visual and textual interpretability. The method supports both global and local explanations by analyzing feature importance with LIME and translating results into human-readable narratives via VLM. Experiments using a publicly available CAN intrusion detection dataset demonstrate that the proposed method provides coherent, text-based explanations, thereby improving interpretability and end-user trust.

A learning management system (LMS) plays a crucial role in supporting students’ educational activities by centralized platforms for course delivery, communication, and student support. Recently, many universities have integrated chatbots into their LMS to assist students with various inquiries and tasks. However, existing chatbots often necessitate human interventions to manually respond to complex queries, resulting in limited scalability and efficiency. In this paper, we present a memory-augmented large language model (LLM) framework that enhances the reasoning and contextual continuity of LMS-based chatbots. The proposed framework first embeds user queries and retrieves semantically relevant entries from various LMS resources, including instructional documents and academic frequently asked questions. Retrieved entries are then filtered through a two-stage confidence filtering process that combines similarity thresholds and LLM-based semantic validation. Validated information, along with user queries, is processed by LLM for response generation. To maintain coherence in multi-turn interactions, the chatbot incorporates short-term, long-term, and temporal event memories, which track conversational flow and personalize responses based on user-specific information, such as recent activity history and individual preferences. To evaluate response quality, we employed a multi-layered evaluation strategy combining BERTScore-based quantitative measurement, an LLM-as-a-Judge approach for automated semantic assessment, and a user study under multi-turn scenarios. The evaluation results consistently confirm that the proposed framework improves the consistency, clarity, and usefulness of the responses. These findings highlight the potential of memory-augmented LLMs for scalable and intelligent learning support within university environments.

Accurate prediction of the state of health (SOH) of lithium-ion batteries is essential for ensuring the safety and reliability of electric vehicles and energy storage systems. While machine learning (ML)-based models have demonstrated strong predictive performance, their limited interpretability remains a major challenge for deployment in safety-critical applications. Although explainable boosting machines (EBMs) provide an interpretable alternative through their additive structure, existing studies still rely on manual analysis of model outputs, which restricts scalability and reproducibility. To address this limitation, this study proposes a structured interpretation framework that integrates EBMs with multimodal large language models (MLLMs). The proposed framework employs EBMs to generate SOH predictions along with global feature importance and variable-level score-density visualizations. These outputs are subsequently processed by an MLLM to perform automated interpretation at both global and variable levels, followed by aggregation, cross-validation, and generation of a unified interpretation report. Experiments were conducted on a lithium-ion battery degradation dataset and the EBM achieved competitive predictive performance compared to baseline ML models. In addition, the quality of the generated interpretations was evaluated using both an MLLM-as-a-Judge and a user study. The evaluation results show that the generated interpretations consistently achieved high scores, with average ratings exceeding 4.5 out of 5 across key criteria such as interpretation accuracy and faithfulness, as assessed by both independent MLLMs and domain experts. These results demonstrate that the proposed framework enables reliable and scalable interpretation of battery SOH prediction models, providing a practical solution for explainable artificial intelligence in battery health management.

Accurate state-of-charge (SOC) prediction is critical for estimating driving range and ensuring the reliability of electric vehicle (EV) battery management systems. Although machine learning-based SOC prediction models achieve high accuracy, their complex nonlinear structures limit interpretability and hinder practical deployment. This study proposes an automated interpretation framework that integrates a multimodal large language model (MLLM) with Shapley interaction quantification (SHAP-IQ) to explain SOC prediction results. An XGBoost-based SOC prediction model is developed, and SHAP-IQ is employed to analyze both main effects of individual input variables (order 1) and pairwise feature interactions (order 2). SHAP-IQ visualizations and attribution values are provided as inputs to MLLM, which generates instance-level natural language explanations, while cross-validation and aggregation procedures ensure consistency. Experiments using real-world driving data collected from a BMW i3 show that XGBoost outperforms benchmark models in SOC prediction accuracy. The results indicate that, for the analyzed instances, SOC predictions are primarily governed by electrical variables such as battery voltage and current, whereas driving and environmental variables mainly affect the prediction through interaction effects. The proposed framework demonstrates the potential to improve the interpretability of SOC prediction models and can be extended to other energy systems in EVs employing complex machine learning models.

Accurate prediction of the state-of-charge (SOC) of batteries is essential for reliable energy management and safety in electric vehicles (EVs). Although machine learning (ML) models have shown strong predictive performance for SOC estimation, their practical development is hindered by limited interpretability, particularly in understanding how individual input variables affect both global model behavior and instance-level responses. Partial dependence plots (PDPs) and individual conditional expectation (ICE) plots are widely adopted visual explanation techniques. However, their interpretation typically relies on manual and fragmented analysis, requiring substantial expert involvement. This study proposes a hierarchical multimodal large language model (MLLM)-based framework that automatically interprets and aggregates PDP and ICE explanations for battery SOC prediction in EVs. Multiple specialized MLLM agents generate structured textual interpretations of PDP and ICE plots for each input variable, capturing global trends and individual-level heterogeneity. These interpretations are subsequently synthesized by a feature-level aggregation agent, which resolves inconsistencies and identifies dominant behavior patterns. A global aggregation agent consolidates the feature-wise explanations into a coherent system-level report summarizing key factors, interaction characteristics, and uncertainty considerations relevant to SOC prediction. The proposed framework provides a scalable and reproducible solution for transforming visual explainable artificial intelligence outputs into structured and decision-oriented knowledge, thereby enhancing the transparency and trustworthiness of ML-based battery management systems.