AI-enabled individual learning strategies and scientific innovation: a case from the field of computer science

With the rapid development of AI technology, individual learning strategies are undergoing profound changes. This study aims to investigate how AI-enabled individual learning strategies impact scientific innovation breakthrough. Addressing the limitations of existing research methods in dealing with this issue, we developed a simulation approach named AI-EEM and validated the theory that the trade-off between exploration and exploitation is crucial for innovation breakthrough using actual publication data from scientists. The results indicate that the AI-EEM model can effectively identify the optimal strategies at different levels of exploration and exploitation. Moreover, this study analyzes the interaction between AI technology and individual learning strategies on the degree of scientific innovation breakthroughs. We find that AI-enabled individual learning strategies may reshape the traditional balance between exploration and exploitation. Specifically, as the level of exploration increases, there is a significant linear positive correlation with the degree of innovation breakthroughs. However, as the level of exploitation increases, the enhancing effect of AI-enabled individual learning strategies on innovation breakthrough diminishes. Therefore, we recommend that scientific researchers prioritize exploratory learning when leveraging AI technology to enhance innovation, in order to fully utilize the opportunities provided by AI and achieve a higher degree of innovation breakthroughs.