Practical Applications of Generative AI in Educational Support

This paper investigates the practical application and effectiveness of a custom generative AI teaching assistant, developed as a GPTs application named DS-ASST, within a "Data Science" course at Kansai University of International Studies. As generative AI technologies like Large Language Models rapidly advance, their potential to transform educational paradigms becomes increasingly evident, offering solutions to challenges inherent in traditional methodologies, particularly in areas requiring personalized learning and large-scale data interaction. This research utilizes the 'Reconsidering Education in the AI Era' framework, focusing on educational AI alignment and redesigning practices to support both instructors and students. The DS-ASST system was developed using Retrieval-Augmented Generation technology to integrate course-specific materials, including lecture notes and textbook content, ensuring responses are contextually relevant and minimizing factual inaccuracies or "hallucinations". We detail the system architecture, iterative prompt design experiments aimed at optimizing educational value, and strategies employed to mitigate technical challenges like hallucination. The system's effectiveness was evaluated through formative assessment across four key dimensions: enhancing teaching preparation efficiency, supporting active student learning, improving data analysis processes, and promoting advanced learning activities. Key findings indicate significant improvements, including a notable reduction in instructor preparation time (approximately 42%) and increased student engagement in discussions (38%) compared to control groups. The AI assistant effectively provided on-demand concept clarification, guided problem-solving, facilitated interaction with complex data, and supported advanced activities like critical evaluation and ethical reasoning. While demonstrating substantial benefits over traditional methods in scalability and flexibility, limitations related to domain specificity, assessment capabilities, and technical requirements were noted. This research assessed the wider ramifications of generative AI for educational reform, based on its practical implementation in education. It specifically considered the changing roles of teachers, developments in assessment techniques, and the necessity of ethical literacy. The study also outlined potential future research, emphasizing hybrid teaching approaches and the formulation of sound ethical guidelines.