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AI4Architect: An Intelligent Help System to Support Students in the Design Domain

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Methodologies and Intelligent Systems for Technology Enhanced Learning, 13th International Conference (MIS4TEL 2023)

Abstract

Chatbots are important tools for improving learning by assisting students in their daily learning activities. In recent years, Deep Learning techniques have made it possible to train chatbots in increasingly comprehensive and even adaptive ways. ChatGPT and DALL-E are the latest and most advanced examples of chatbots that are capable, respectively, of providing textual responses to questions about almost any knowledge domain and of creating an image produced by a concept expressed in a textual form. More and more, students use such applications as a support in their learning process at all levels of study. In this paper, we present the AI4Architect system, a web application to support students in creating their projects in the design domain. The system consists of two modules, structured in a pipe configuration. The first module is based on ChatGPT and the second on DALL-E: students can first post a question and then ask for one or more images to be created based on the response to that question. Our goal is to investigate if such an AI-based system can help students as draw guidance and inspiration for the production of their design projects. A controlled experiment was conducted that showed the effectiveness of the system as a valid educational support.

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Notes

  1. 1.

    http://openai.com/.

  2. 2.

    Available at: https://design-edu.bubbleapps.io/.

  3. 3.

    https://www.usability.gov/how-to-and-tools/methods/system-usability-scale.html.

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Correspondence to Filippo Sciarrone .

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Abrusci, L., Dabaghi, K., D’Urso, S., Sciarrone, F. (2023). AI4Architect: An Intelligent Help System to Support Students in the Design Domain. In: Milrad, M., et al. Methodologies and Intelligent Systems for Technology Enhanced Learning, 13th International Conference. MIS4TEL 2023. Lecture Notes in Networks and Systems, vol 764. Springer, Cham. https://doi.org/10.1007/978-3-031-41226-4_7

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