This study examines the use of Generative Artificial Intelligence (GenAI) tools within Design Thinking (DT) processes, particularly focusing on knowledge creation as a theoretical lens. The research setting is education, where active learning methodologies are adopted to realign education with new market-demand skills such as creativity and critical thinking, especially in a context of rapid transformation due to digitalization and Artificial Intelligence. In this regard, the present research posits that DT processes (a methodology) emphasize innovation and creativity, objectives that are shared with GenAI (a technology). Considering all the previous, this study aims to understand how GenAI intervenes in knowledge creation in DT processes, and its impact on students’ learning and skills development. To address these research objectives, the present article employs a mixed-method approach combining qualitative and quantitative methods. It involves classroom observations, student surveys and performance analysis in the context of an innovation management course that leverages a Challenge-based Learning approach and a DT methodology. Findings indicate that GenAI is more effectively used in convergent phases of DT, contributing to information synthesis and problem-solving. However, in divergent phases, excessive reliance on GenAI appears to diminish students’ inherent creativity and critical thinking, leading to poorer performance, and while GenAI aids in expanding the scope of research and idea generation in DT processes, it faces limitations in creativity and reliability. Furthermore, despite recognizing the utility of GenAI, there is a noticeable reluctance among students to declare its usage. Concerning the knowledge creation throughout the process, the results suggest that effectiveness of GenAI varies across different phases of the SECI model: it is more advantageous in the combination phase, but faces challenges in capturing tacit knowledge. This study contributes to theory with new insights into the integration of advanced technologies in well-established knowledge creation models, while also reconciling them with literature about DT processes. From the practitioner standpoint, this study provides valuable guidelines for educators and professionals on how GenAI can be effectively integrated into user-centered DT methodologies for active learning and innovation management.