The proliferation of efficiency-focused AI tools in creative processes threatens to undermine critical, reflective practices foundational to design education. This approach can lead to creativity exhaustion and diminished agency among designers and students. As an antidote, we propose Reflective AI: an approach grounded in slow technology principles that reframes AI not as a production tool, but as a medium for reflecting on the creative process itself. This paper presents the Objective Portrait Workshop where design students engaged in slowed data collection, annotation, and model finetuning. Our contribution is threefold: we (1) document a methodology for implementing Reflective AI in design education; (2) provide empirical evidence that slow engagement cultivates reflection on creative processes and technical understanding of AI; and (3) propose material and temporal disentanglement as core mechanisms for Reflective AI practice. This work offers a practical alternative to “fast” AI, providing methodology that cultivates critical capabilities essential to design.

Artificial intelligence (AI) is increasingly being viewed as a creative partner rather than as a tool. How to design such collaborations is still a subject of speculation. In this pictorial, we propose a collaborative role for AI to prompt self-reflection. We explore this through a practice-based inquiry of whether and how AI could help a designer reflect on and relate to their own work. Three designers annotate a collection of images representing their fascinations, with subjective labels, indicating different dimensions of their visual concepts. These labels are used to teach an object detection model the designers’ perspectives. Then, they used this trained model on their own design work to evaluate the AI's potential to prompt self-reflection. By describing this process of AI-training we explore how an AI can help us become aware of our own implicit perspectives.