Generative artificial intelligence (AI) has introduced transformative paradigms into education, facilitating personalized, real-time, and interactive learning experiences. This study investigates the integration of AI-based textbooks within regular college curricula, with a specific focus on their application in ancillary engineering subjects that demand high precision. AI textbooks enable customized learning pathways, enhance student engagement through adaptive content, and provide educators with data-driven insights. Employing a mixed-methods approach, this research compares the academic performance and learning experiences of two groups: the Traditional Learning Group (TLG), which utilized printed materials; and the AI Learning Group (ALG), which employed a generative AI-powered textbook based on LLama 3.1. Over a 15-week semester, data were collected through pre- and post-tests, task evaluations, platform log analyses, and satisfaction surveys. The findings reveal no statistically significant differences between the two groups in quantitative measures such as academic achievement, learning time, and overall satisfaction. However, the qualitative assessments underscore the role of AI-based learning in supporting self-directed education, while also highlighting the critical challenges, including digital equity, algorithmic biases, and data privacy concerns. Furthermore, the study emphasizes the necessity of comprehensive educator training, the establishment of ethical frameworks, and the development of scalable implementation strategies to fully leverage the potential of AI textbooks. Future research should prioritize randomized controlled trials (RCTs) to evaluate the long-term impacts of AI-based educational tools and develop adaptive frameworks that balance technological advancements with the emotional and motivational dimensions of human-centered education. This pioneering research lays the groundwork for advancing generative AI in higher education and fostering an innovative and equitable learning ecosystem.

As digital technologies and food environments evolve, ensuring children’s food safety has become a pressing public health priority. This study examines how the policy discourse on children’s dietary safety in Korea has shifted over time by applying Latent Dirichlet Allocation (LDA) topic modeling to news articles from 2010 to 2024. Using a large-scale news database (BigKinds), the analysis identifies seven key themes that have emerged across five phases of the national Comprehensive Plans for Safety Management of Children’s Dietary Life. These include experiential education, data-driven policy approaches, safety-focused meal management, healthy dietary environments, nutritional support for children’s growth, customized safety education, and private-sector initiatives. A significant increase in digital keywords—such as “big data” and “artificial intelligence”—highlights a growing emphasis on data-oriented policy tools. By capturing the evolving language and priorities in food safety policy, this study provides new insights into the digital transformation of public health governance and offers practical implications for adaptive and technology-informed policy design.