Enhancing students’ critical thinking skills through the use of DALL-E

• Stefan Küchemann ^[1] ; Steffen Steinert ^[1] ; Karina E. Avila ^[1] ; Jochen Kuhn ^[1]
  1. [1] Ludwig Maximilian University of Munich

     Ludwig Maximilian University of Munich

     Kreisfreie Stadt München, Alemania

     
• Localización: The Physics Teacher, ISSN 0031-921X, Vol. 63, Nº. 2, 2025, págs. 138-139
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • Large language models have emerged as a novel and popular phenomenon that attracts many people. These models are trained on comprehensive text data sets, equipping them with the ability to execute tasks such as text recognition, translation, prediction, and generation. However, their application in education is controversial, as they are known to produce content that can sometimes be misleading and erroneous.1 This poses a challenge to young students’ ability to discern between reliable and unreliable information that is generated by artificial intelligence (AI).2 Particularly in physics, advanced large language models demonstrate quite remarkable abilities, for example, being able to support teachers3 or students during inquiry-based learning.4 One example of such a generative AI model is DALL-E 2, which can create novel images based on text prompts. Many of these realistic-looking images are widely circulated on the web, making it difficult to protect the young generation from exposure to the generated AI. Therefore, it is critical to reinforce critical thinking in the classrooms in this era of misinformation. Instead of viewing this as a disadvantage, one could use this as an opportunity to generate exercises for students to enhance their critical thinking and discussion abilities. In this paper, we propose the use of DALL-E 2 as a tool to improve students’ critical thinking skills by engaging them in peer discussions about the accuracy of the generated images. In this way, this exercise has two aims: (i) to train students to discriminate between physically correct and incorrect depictions of natural phenomena, thus learning physical concept knowledge (here, the refraction of light); and (ii) to enhance students’ critical thinking skills in the context of generative artificial intelligence.