arXiv subjects

Chun-Hua Tsai

Publications and source records attributed to Chun-Hua Tsai.

3 recordsLinked to original sources

arXiv2026

Towards SocratiCode: Designing a Generative AI-Based Programming Tutor for K-12 Students through a 4-Week Participatory Design Study

Generative AI creates new opportunities for programming education, but many existing systems remain overly directive, producing lengthy explanations and premature solutions that can overwhelm K-12 novices. In this paper, we present a participatory design study of how an adaptive tutorial system, SocratiCode, evolved toward a Socratic tutoring model for beginner programming instruction. Drawing on weekly learner feedback, we iteratively refined the system over a four-week study with two K-12 students learning Python. Across iterations, the system shifted from flexible tutorial generation toward a more dialogic form of support characterized by guided questioning, reflection prompts, misconception checks, incremental hints, and mandatory pauses for learner input. Our preliminary observations suggest that this Socratic shift improved explanation clarity, supported problem-solving engagement, and better aligned instruction with novice learners' needs, especially when combined with human guidance. We argue that generative AI in K-12 programming education may be most effective not as an answer engine, but as a Socratic, adaptive learning companion embedded within a human-guided instructional framework.

cs.HC↗

arXiv2024

Deep Learning-based Text-in-Image Watermarking

In this work, we introduce a novel deep learning-based approach to text-in-image watermarking, a method that embeds and extracts textual information within images to enhance data security and integrity. Leveraging the capabilities of deep learning, specifically through the use of Transformer-based architectures for text processing and Vision Transformers for image feature extraction, our method sets new benchmarks in the domain. The proposed method represents the first application of deep learning in text-in-image watermarking that improves adaptivity, allowing the model to intelligently adjust to specific image characteristics and emerging threats. Through testing and evaluation, our method has demonstrated superior robustness compared to traditional watermarking techniques, achieving enhanced imperceptibility that ensures the watermark remains undetectable across various image contents.

cs.MM↗

arXiv2022

Not Another Day Zero: Design Hackathons for Community-Based Water Quality Monitoring

This study looks at water quality monitoring and management as a new form of community engagement. Through a series of a unique research method called `design hackathons', we engaged with a hyperlocal community of citizens who are actively involved in monitoring and management of their local watershed. These design hackathons sought to understand the motivation, practices, collaboration and experiences of these citizens. Qualitative analysis of data revealed the nature of the complex stakeholder network, workflow practices, initiatives to engage with a larger community, current state of technological infrastructure being used, and innovative design scenarios proposed by the hackathon participants. Based on this comprehensive analysis, we conceptualize water quality monitoring and management as community-based monitoring and management, and water data as community data. Such a conceptualization sheds light on how these practices can help in preempting water crisis by empowering citizens through increased awareness, active participation and informal learning of water data and resources.

cs.HC↗