Developed by Kelsey Dufresne and Alin Yalcinkaya
Over the course of the semester, students embarked on a creative and critical journey through the evolving worlds of storytelling, data, and artificial intelligence. Beginning with reflections on identity and positionality, they grounded their practice in personal narrative and the ethics of self-representation. Through readings on AI art, data feminism, and algorithmic bias, students questioned the cultural, aesthetic, and social dimensions of machine-made imagery.
The classroom became a studio for exploration—blending workshops, discussions, and data diary check-ins—where students played with generative tools and shaped visual narratives from their lived experiences. They moved fluidly between code and creativity, investigating what data can mean when it comes from the self, and what it might become when shaped by AI.
Anchored by works like “The Yellow Wallpaper,” “Everyday Use,” and speculative reflections on AI’s potential and pitfalls, students developed a layered understanding of storytelling—one that embraces emotion, memory, and critical inquiry. The semester culminated in final projects that showcased each student’s process, practice, and vision for where art, data, and AI might go next.
Throughout this class, students have creatively engaged with AI-image generation tools to explore storytelling, self-expression, and visual knowledge sharing. They critically examined the role of data science—its possibilities and limitations—as a form of knowledge, questioning what counts as data and how it can be represented. Through artistic practice and reflection, they investigated the intersections of AI, data, and art, and developed critical questions and analysis alongside their own AI-generated visual narratives.
This material is based upon work supported by the National Science Foundation under Grant #DGE-2222148. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
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