As a result of technological advancements, design tools and approaches are constantly evolving, leading to paradigm shifts in design education. Currently, innovations brought by artificial intelligence tools are particularly prominent. Designers who do not learn to effectively use these tools risk losing their competitiveness. Therefore, more study is needed to uncover the yet unexplored potentials of artificial intelligence tools in architectural education, examine their possible uses, and establish them as a common resource to enrich the design process. Within the scope of this study, firstly, a literature review was conducted on artificial intelligence, diffusion models, and the use of these tools in architectural education. While studies focusing on specific design stages were found in the literature, there is limited research on the integration of these tools across different stages. The aim of the study is to reveal the potentials of different diffusion models and explore the uses of these tools in various stages of the studio through student experiences. For this purpose, data were collected by conducting semi-structured interviews with students who have incorporated various artificial intelligence tools into the studio process and the instructors of these studios, audio-recording the interviews and analyzing these recordings. In addition, students' productions during the process were collected and evaluated together with the interview data. The differences in students' experiences were examined in three categories: inputs, outputs, and tool usage. As the design process progresses, inputs become more defined, while the number of variations in production decreases, requiring an increase in technical knowledge for production. Additionally, the views of studio instructors were obtained, revealing the potentials and threats of the tools. The study found that the tools have the potential to inspire ideas, facilitate idea transmission, accelerate production, and support atmosphere creation, but they may also pose threats such as limiting ideas, preventing deepening of design, and tool hijacking the process. Based on the findings from examining and analyzing the use of various diffusion models by students in studio processes, the study developed recommendations for the integration of artificial intelligence into the studio. The results demonstrate the potential for artificial intelligence tools to be integrated into various stages of the studio. It is believed that this study can contribute to future research on the integration of artificial intelligence into the studio and the development of alternative strategies.