Selected work

Representative Papers

AI Misuse in Education Is a Measurement Problem: Toward a Learning Visibility Framework

Mar 08, 2026

This work addresses the risks posed by the misuse of artificial intelligence in education, which obscures the visibility of the learning process and threatens academic integrity, equity, and cognitive development. The authors propose a “learning visibility” framework that reconceptualizes AI misuse as a measurement challenge rather than a detection problem. Integrating cognitive offloading theory, learning analytics, and multimodal timeline reconstruction techniques, the framework establishes an assessment system centered on process transparency, normative AI use, and shared evidentiary practices. Moving beyond the limitations of conventional AI-detection tools, this approach offers educators a principled pathway for AI integration that upholds educational values, fosters trust, and enhances transparency, thereby effectively mitigating the “black box” effect induced by AI-mediated learning environments.

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Detection of Anomalous Behavior in Robot Systems Based on Machine Learning

Sep 12, 2025

This study addresses anomaly detection in robotic systems to enhance operational safety and reliability. We propose a context-aware machine learning approach, leveraging system logs collected from quadcopter and Pioneer robots under diverse operational conditions in the CoppeliaSim simulation environment. We systematically evaluate the anomaly detection performance of logistic regression (LR), support vector machines (SVM), and autoencoders. Our key contribution is a “context-dependent model selection strategy,” which empirically demonstrates that anomaly complexity varies significantly across robotic platforms: LR achieves optimal performance in highly dynamic quadcopter scenarios, whereas autoencoders substantially outperform other models on the less structured, behaviorally complex Pioneer platform. Experimental results validate the adaptability and effectiveness of this strategy across heterogeneous robotic systems, providing a principled methodology for tailoring anomaly detection solutions to specific robot architectures and operational contexts.

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Recent publications

Latest Papers

AI Misuse in Education Is a Measurement Problem: Toward a Learning Visibility Framework

Mar 08, 2026

This work addresses the risks posed by the misuse of artificial intelligence in education, which obscures the visibility of the learning process and threatens academic integrity, equity, and cognitive development. The authors propose a “learning visibility” framework that reconceptualizes AI misuse as a measurement challenge rather than a detection problem. Integrating cognitive offloading theory, learning analytics, and multimodal timeline reconstruction techniques, the framework establishes an assessment system centered on process transparency, normative AI use, and shared evidentiary practices. Moving beyond the limitations of conventional AI-detection tools, this approach offers educators a principled pathway for AI integration that upholds educational values, fosters trust, and enhances transparency, thereby effectively mitigating the “black box” effect induced by AI-mediated learning environments.

0 citationsRead paper

Detection of Anomalous Behavior in Robot Systems Based on Machine Learning

Sep 12, 2025

This study addresses anomaly detection in robotic systems to enhance operational safety and reliability. We propose a context-aware machine learning approach, leveraging system logs collected from quadcopter and Pioneer robots under diverse operational conditions in the CoppeliaSim simulation environment. We systematically evaluate the anomaly detection performance of logistic regression (LR), support vector machines (SVM), and autoencoders. Our key contribution is a “context-dependent model selection strategy,” which empirically demonstrates that anomaly complexity varies significantly across robotic platforms: LR achieves optimal performance in highly dynamic quadcopter scenarios, whereas autoencoders substantially outperform other models on the less structured, behaviorally complex Pioneer platform. Experimental results validate the adaptability and effectiveness of this strategy across heterogeneous robotic systems, providing a principled methodology for tailoring anomaly detection solutions to specific robot architectures and operational contexts.

0 citationsRead paper