Pendidikan editorial
Open AccessOA2025
A Comparative Analysis of On-Device AI-Driven, Self-Regulated Learning and Traditional Pedagogy in University Health Sciences Education
This study compares a generative AI-powered textbook (LLama 3.1) with traditional printed materials in a 15-week university health sciences course, finding no statistically significant differences in academic achievement, learning time, or satisfaction, but noting qualitative benefits for self-regulated learning and critical challenges in digital equity, algorithmic bias, and data privacy.
Ji Chun; Jeongsoo Kim; Hyejin Kim; Geumgu Lee; Sanggoo Cho; Changshik Kim; Yeesook Chung; Seoyoon Heo· Applied Sciences· 2025· DOI 10.3390/app15041815
The core problem
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. The research aims to compare 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. The study addresses a critical gap in understanding the effectiveness of AI-driven self-regulated learning in higher education, particularly in health sciences education where precision and adaptability are paramount.
Innovation
Employing a mixed-methods approach, this research was conducted over a 15-week semester. The participants were university students enrolled in health sciences courses. The Traditional Learning Group (TLG) used printed materials, while the AI Learning Group (ALG) used a generative AI-powered textbook based on LLama 3.1. Data were collected through pre- and post-tests, task evaluations, platform log analyses, and satisfaction surveys. The quantitative measures included academic achievement (pre- and post-test scores), learning time (from platform logs), and overall satisfaction (from surveys). Qualitative data were gathered from open-ended survey responses and interviews to explore learning experiences, self-directed education, and challenges. The study employed statistical analyses to compare the two groups, including t-tests and effect size calculations, while qualitative data were analyzed thematically.
Introduction
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. The research aims to compare 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. The study addresses a critical gap in understanding the effectiveness of AI-driven self-regulated learning in higher education, particularly in health sciences education where precision and adaptability are paramount.
Why it matters
The lack of significant quantitative differences suggests that AI textbooks, as implemented in this study, do not inherently outperform traditional materials in terms of academic achievement, learning time, or satisfaction. However, the qualitative findings indicate that AI-based learning can support self-directed education, which is crucial for lifelong learning in health sciences. The challenges identified—digital equity, algorithmic biases, and data privacy—are critical barriers to equitable implementation. Digital equity concerns arise from unequal access to devices and internet connectivity, which can exacerbate existing disparities. Algorithmic biases in AI models like LLama 3.1 may inadvertently perpetuate stereotypes or inaccuracies, particularly in high-precision subjects. Data privacy concerns involve the collection and use of student data, necessitating robust ethical frameworks. 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.
Who should read this
Pembaca Research Digest dan praktisi rumpun ini.
Opening member content…