The penetration of generative AI into society is rapidly changing not only the educational environment but also students, so traditional lectures need to be updated to fit generative AI.

In this study, we propose a method to design lecture spaces as a knowledge transformation process in which generative AI and humans cooperate. The lecture space provides the way to capture the evolution history of lectures. The lecture space consists of three aspects, Quality, Architecture, and Process. The aspects of lecture spaces are called as QAP triangle.

The quality of lecture defines as to achieve specific learning outcomes. It also provides goals to which architecture and process shall realize. The architecture of lectures defines components and relationships of lectures. The process of lectures defines the way how lectures construct.

Thanks to the QAP triangle, it is easy to evaluate the lecture characteristics and improve it.

The QAP triangle can facilitate to identify the issues of generative AI based lectures in the three aspects, and trace the sustainable growth of lectures in the lecture spaces.
By using the QAP triangle, we can define the maturity of lectures from three aspects consisting of quality, architecture, and process of lecture designs.
In order to evaluate the maturity of lecture space design using generative AI, it is needed to define evaluation indicators consisting of three aspects consisting of the quality, architecture, and process of the lecture space. These indicators formulate the attributes common to the design object: why lecture, what is lectured, and how the lecture is provided.

Moreover, we apply the lecture space design by QAP triangle to specific lectures to clarify the effectiveness and show the capability of identifying lecture improvement measures. The QAP triangle-based lecture space design method was verified by a case study of requirements engineering lecture space design for practitioners, and its applicability to the evaluation of the evolution process of lecture spaces by generative AI was demonstrated.

This study explores the ideal form of change that digital technologies such as generative AI will bring to education, and presents a new approach to visualize the acceptability of technology for change. It is also a pioneering study that combines quality, architecture, and evolutionary processes in educational research, providing a meaningful interdisciplinary contribution to integrating technology and educational reform.

In future research, we aim to develop an integrated methodology by comparing it with design thinking and knowledge creation methodologies.

Keywords: Lecture space design, generative AI, Lecture evolution, QAP triangle.