Amplifying Learning and Teaching Effectiveness through Generative Artificial Intelligence: A Qualitative Approach with Case Studies on Supply Chain and Cold Chain Management

The increasing popularity of Generative Artificial Intelligence (GAI) technology offers new possibilities and challenges for teaching and promoting human-AI collaborative learning. In order to enhance the learning quality and experience of students, it is important to develop an effective instructional design, particularly in defining the goals and strategies, solving individual needs, and enhancing learning performance. However, the impact of implementing GAI in student learning and performance enhancement considerations is still lacking. Therefore, this study proposes an experimental framework for evaluating the effectiveness of GAI-based learning for students. By scrutinizing instructional goals and design strategies to achieve desirable learning outcomes, it provides guidelines on coverage design and development in GAI-based learning environments. Experiment with case studies on supply chain and cold chain management is adopted to analyze the effectiveness and facilitate instructional design in GAI-based learning to enhance the student learning experience. The result indicates that the treatment group outperformed the control group. This study is expected to provide insights into academic development and future education on GAI-based learning.