Integrating Generative AI Into Design Thinking: Assessing Impact on Creativity and Innovation in STEM Education

Design thinking (DT), widely recognized as a structured method for fostering creativity and innovation, has gained significant traction in research and practice across various disciplines. However, with the rise of disruptive technologies like artificial intelligence (AI), DT practices are gradually evolving, reshaping the innovation process. This study investigates the effectiveness of integrating generative AI into a product design thinking activity, employing a single-group pretest-posttest design (i.e., without a control group). The time interval between pretest and posttest measurements was four hours, coinciding with the duration of the DT activity. Conducted in a chemical engineering course at a private university in central Mexico, the research tasked nine students of average academic performance with designing a new beverage. Over a four-hour session, students used AI tools-ChatGPT, Perplexity, and Gemini-at various stages of the design process, including empathy mapping, need statements, idea classification, hills writing, and storyboarding. Several multidimensional constructs were measured using self-report questionnaires to assess the key attributes that DT stimulates: perceptions of creative self-efficacy, design thinking mindset, and empathy. Additionally, the study explored students' views on the usefulness of generative AI and their intention to use such tools. It was hypothesized that post-test scores for each construct would increase. The analysis involved two phases: first, psychometric indicators (alpha reliability) were obtained; second, a statistical approach for assessing individual change was applied, precisely the standardized individual difference (SID). The SID was set at a nominal level of 0.80 (right tail of the normal distribution). Scores with unacceptable measurement error (alpha <. 60) were excluded from the primary analysis. The results revealed a significant increase in students' perceived usefulness of AI between pre- and post-experiment measurements, with a moderate improvement in affective empathy. Other constructs also showed consistent, though modest, post-test score increases. However, only a few participants exceeded the SID threshold, indicating individual variations in response to the intervention. These preliminary findings highlight AI's potential to enhance student creativity through idea generation and expand their consideration of the end user in product design. The results provide valuable insights and recommendations for integrating AI into innovation-driven projects using the design thinking approach and implementing a single-group pretest-posttest design with short time intervals.