AI tools have become increasingly popular and accessible in various domains, including industrial design engineering. However, there is a lack of empirical studies on how these tools affect the design process and outcomes, as well as the ethical implications of their use. In this paper, we present a research study that aims to explore how students at bachelor level in Industrial Design Engineering, particularly those without prior knowledge, perceive the usefulness of text-to-image generative tools. We also examine their main ethical considerations regarding the use of these types of tools, as well as AI tools in general. Our findings indicate a cautious but curious attitude towards AI technologies, underscoring the need for a nuanced approach in their integration into educational curricula. The apprehension towards adopting these tools reflects a broader concern about ethical implications, technological dependence, and the potential overshadowing of human creativity. However, the study also reveals an eagerness to understand and utilize these technologies, suggesting a latent potential for enhancing creative processes in design engineering. As our study shows, students are keenly aware of the challenges and opportunities presented by AI, highlighting the importance of clear guidelines and ethical frameworks. In conclusion, the integration of AI tools like text-to-image generative models in design engineering education presents both challenges and opportunities. The key to successfully navigating this integration lies in a balanced approach that emphasizes ethical usage, critical understanding, and creative collaboration. Through such an approach, we can prepare the next generation of designers to effectively and responsibly harness the potential of AI in their work, ensuring that human creativity remains at the forefront of design innovation.
STUDENT PERCEPTIONS OF ETHICS AND USEFULNESS OF AI TOOLS FOR DESIGN ENGINEERING
Reference Text
Proceedings of the 20th International CDIO Conference, ESPRIT, Tunis, Tunisia, June 10-13 2024 Year
2024 Authors
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Pages
717-727 Abstract
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730_CDIO 2024 Proceedings.pdf
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