Differential Learning Gains in Design Education with Generative AI Tools and Pedagogical Responses

• Jiahui Guo

  Nanning University

  Author

While design production has become more efficient and design results seem to be stronger, this does not prove learning from the use of Generative AI tools. The key question for generative AI's entry into design education is whether students can internalize and integrate external intelligence to make it their own. This study analyzes learning gains in three aspects: cognitive deepening, knowledge internalization and transfer and application, by using literature analysis and theoretical synthesis. It defines three parallel states – performance enhancement, process transformation, and transfer development. Pedagogical responses are suggested in the study that will maintain cognitive engagement, reconstruct knowledge, enable transfer and offer adaptive support. The educational value should not be measured by the speed of generation or by completed artefacts but by the extent to which learners acquire professional capabilities that they can internalise and transfer. Hence, the emphasis of design education is no longer on AI-enhanced performance, but on capability of learners.

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