While legacy sketchnotes capture rich design rationales and inspirations, they are rarely reused in contemporary practice. We present Co-Ideation Across Time (CIAT), utilizing Large Language Models (LLMs) to transform decades-old design sketchnotes into interactive "AI-augmented Knowledge Objects". Our system digitizes over 2,000 pages of alumni sketchnotes and connects them with conversational agents trained on corresponding theses and publications, enabling current and future students to engage in multimodal dialogue with past ideas and researchers. An exploratory evaluation with 12 participants showed that interacting with the system stimulated deeper understanding of abstract concepts, idea diversity, and fostered a stronger sense of continuity with the community’s legacy. Our contributions are threefold: (1) a method for integrating design legacies with LLM-driven conversational agents; (2) an empirical study demonstrating how this approach supports learning and intergenerational knowledge sharing; and (3) a conceptual framing of AI-Augmented Knowledge Objects as active participants in design ideation.

Learning a motor skill is essential for many aspects of our lives. The complexity of some of these activities makes it hard for novices to understand through observation. Calligraphy writing is one such artistic practice where learners compare the visual differences between their writing and expert manuscripts and adjust until they have achieved similar results. We propose an accessible plotter-based system that guides the learner’s arm and hand in three directions with an actuated brush. It converts static Chinese calligraphy manuscripts to G-code that reproduces the calligrapher’s movement. Through a user study with twelve novice calligraphy learners, we validated the efficacy of our system as a learning tool that allows novices to gain an intuition of nuanced skills such as depth variation more effectively compared to watching a video recording of the same movement.