Deaf and hard-of-hearing (DHH) students often experience delays in print literacy due to limited access to spoken language. This study developed a prototype of an AI-integrated digital textbook (AIDT) for Korean language learning, which incorporated visual learning materials and simplified versions of complex texts to enhance accessibility and support reading comprehension. Personalized instruction tailored to individual needs was also embedded, based on insights gathered through prior participatory design phases. A usability test was conducted with six DHH students and four teachers using five learning tasks. The results revealed both the strengths and limitations of the AIDT prototype. Students appreciated the interactive and visually rich features by reporting increased engagement and a greater sense of independence in their learning. However, they encountered challenges related to insufficient sign language support and a non-intuitive interface. Teachers recognized the potential of AIDT to reduce instructional workload and to promote self-directed and personalized learning. At the same time, they emphasized the need for clearer navigation features to better track student learning progress. These findings suggest that AIDTs can be a valuable tool for supporting Korean language learning among DHH students when designed with accessibility and usability in mind. We propose future research directions that include refining the interface design, enhancing sign language scaffolding, improving navigation features, and exploring classroom integration to better support the diverse learning needs of DHH students.

This study explores the student perceptions of Generative AI (GenAI) tools, such as ChatGPT, on academic writing across different educational levels in higher education. Using topic modeling, this study analyzed open-ended responses from 189 university students in Korea to identify the dominant themes related to these tools. The findings reveal that students across all education levels generally recognize the potential of ChatGPT to enhance writing efficiency, idea generation, and organizational structure in their academic work. However, concerns vary by educational level, with graduate students expressing a more cautious and critical attitude towards the use of GenAI tools. These insights provide a deeper understanding of the impact of GenAI on academic writing from a student perspective and offer valuable considerations for instructional support in higher education.

Deaf and hard-of-hearing (DHH) children often encounter substantial hurdles in their language development and literacy due to their limited language exposure, which leads to lower print literacy levels compared to their hearing peers. The advent of Al technology offers a promising way to address these challenges when integrated into reading materials. This study examines the design prototype for a Korean Al digital textbook (AIDT) for DHH students, employing participatory design involving both teachers and DHH students. Through encompassing classroom observations, teacher interviews, and prototype drawing activities with students, the study identifies features essential for enhancing the learning experience of DHH students through AIDT. Teacher interviews revealed several critical needs, including the necessity to restructure existing textbooks for DHH learners, incorporate diverse visual materials, address unique linguistic challenges, provide personalized instruction, and support the comprehension of abstract concepts. Furthermore, the prototype drawings by DHH students revealed additional requirements such as the ability to draw pictures from written text, create and share visual materials with teachers and classmates, simplify complex reading materials, provide immediate feedback, and utilize visual aids. These findings underscore the potential of AIDT to individualized learning experiences and alleviate certain teaching burdens, thereby fostering improved learning outcomes for DHH students.

Real-world datasets are dirty and contain many errors, such as violations of integrity constraints and entity duplicates. Learning over dirty databases may result in inaccurate models. Data scientists spend most of their time on preparing and repairing data errors to create clean databases for learning. Moreover, as the information required to repair these errors is not often available, there may be numerous possible clean versions for a dirty database. We propose Dirty Learn, DLearn, a novel learning system that learns directly over dirty databases effectively and efficiently without any preprocessing. DLearn leverages database constraints to learn accurate relational models over inconsistent and heterogeneous data. Its learned models represent patterns over all possible clean versions of the data in a usable form. Our empirical study indicates that DLearn learns accurate models over large real-world databases efficiently.