Workshops are a very common mode of interaction for team learning, and depending on the type of workshop, there are different ways of interaction and questions. In digital media workshops, design and computation coexist, but the logical conflict between them is always difficult for designers to understand and master. In the research team's previous study, we found that designers often had difficulty combining their imagination with the computational tools we were given at the time of the workshop, or not sure what tools could help them. In a multi-person workshop, it is difficult for the facilitator to take care of both the activity and the participants, resulting in an increased burden on the facilitator and a decrease in the participants’ willingness and self-confidence to engage in computation.
In the previous study, we tested and adjusted the interaction process of the workshop and developed a new workshop process. In this study, we took the role of the facilitator as the main part, the scenario as the computing workshop, and the user as the designer. The process of the previous study was used as the base concept for the next iteration of the system, for which we designed a model of the workshop support system.
After the participants have gone through the design thinking process, they put their design ideas into the system, and after a set of natural language processing, the system can suggest the participants’ computational tools based on the data, and provide them with the direction to choose the right tools for their ideas.
The system should also be able to make iterative corrections and learn from the iterative corrections in the original prescribed route to generate new answers. Through machine learning, the system can automatically learn and update, and can switch between different computational workshops more freely, so that the results can be more flexible.