## 1 Introduction
Digital textbooks in Korea are conceptualized not merely as digitized print versions but also as integrated tools that combine the essential elements of user-friendliness, utility, system quality, content reliability, and Interactivity to maximize learning effectiveness [11]. These resources support multimedia integration, personalized learning pathways, learning analytics, and the establishment of learning communities. Despite these advancements,concerns such as screen exposure and technical issues persist, highlighting the need for infrastructure development and policy support. As Korea prepares to introduce AI-powered digital textbooks by 2025, the urgency for a continuous digital learning ecosystem from primary through to higher education grows, yet explicit policies and research in tertiary contexts remain insufficient [9; 10]. The higher education setting faces unique challenges, including the substantial cost of implementing advanced features such as diagnostic dashboards, personalized multimedia, and cloud-based data storage, which all lack strong policy backing [11]. In the absence of government-level plans, employing open AI models presents an efficient alternative that can be used to enhance the self-directed learning competencies facilitated by digital textbooks. By leveraging these models, universities can maintain pedagogical continuity and further develop learner-centered, technologically enriched educational environments that reflect the evolving expectations of digital literacy and innovation in the modern information society [9; 10; 11]. Against this backdrop, the present study compares the academic outcomes and learning experiences of students who engage with an AI-driven, self-regulated learning environment, the \"KBU AI Textbook Project\", versus those who rely on traditional paper-based textbooks. Focusing on a cohort of 86 undergraduate students enrolled in assistive technology courses within a health sciences field, the aim of this study was to evaluate whether the integration of generative AI-driven resources leads to improvements in learning achievement and supports the cultivation of self-regulatory skills. Through a rigorous experimental design and mixed-methods evaluation process, this research addresses a critical gap in the literature by providing empirical evidence on the impact of AI textbooks in a specialized and highly practical field of study. Employing both quantitative and qualitative methodologies, this study aimed to provide valuable insights for educators, curriculum developers, and policymakers regarding the practical implications and pedagogical benefits of incorporating AI-based learning tools into higher education. The central hypothesis posits that, by 2024, instruction delivered using the \"AI textbook\", a generative AI-based model known as LLama, will foster superior academic achievement outcomes among students in their early twenties, a cohort increasingly accustomed to digitally mediated learning environments. These findings are anticipated to inform future strategies for employing generative AI models to enhance learner-centered instruction, thereby contributing to the ongoing advancement of educational practices in the health sciences and related domains.