Visual designers often seek inspiration from Chinese paintings when tasked with creating Chinese-style illustrations, posters, etc. Our formative study (N=10) reveals that during ideation, designers learn the cultural symbols, emotions, compositions, and styles in Chinese paintings but face challenges in searching, analyzing, and integrating these dimensions. This paper leverages multi-modal large models to annotate the value of each dimension in 16,315 Chinese paintings, built on which we propose InkIdeator, an ideation support system for Chinese-style visual designs. InkIdeator suggests cultural symbols associated with the task theme, provides dimensional keywords to help analyze Chinese paintings, and generates visual examples integrating user-selected keywords. Our within-subjects study (N=12) using a baseline system without extracted dimensional keywords, along with two extended use cases by Chinese painters, indicates InkIdeator's effectiveness in creative ideation support, helping users efficiently explore cultural dimensions in Chinese paintings and visualize their ideas. We discuss implications for supporting culture-related visual design ideation with generative AI.

Fine-tuning facilitates the adaptation of text-to-image generative models to novel concepts (e.g., styles and portraits), empowering users to forge creatively customized content. Recent efforts on fine-tuning focus on reducing training data and lightening computation overload but neglect alignment with user intentions, particularly in manual curation of multi-modal training data and intent-oriented evaluation. Informed by a formative study with fine-tuning practitioners for comprehending user intentions, we propose IntentTuner, an interactive framework that intelligently incorporates human intentions throughout each phase of the fine-tuning workflow. IntentTuner enables users to articulate training intentions with imagery exemplars and textual descriptions, automatically converting them into effective data augmentation strategies. Furthermore, IntentTuner introduces novel metrics to measure user intent alignment, allowing intent-aware monitoring and evaluation of model training. Application exemplars and user studies demonstrate that IntentTuner streamlines fine-tuning, reducing cognitive effort and yielding superior models compared to the common baseline tool.

Online assessments allow instructors to facilitate exams and quizzes in both virtual and large classes. Having a clear online timer during these assessments is vital to help students manage their time. However, these same timers can be a cause of anxiety, affecting student performance. Our goals were to determine (i) which types of visualizations are currently in use, (ii) which styles of online timer were preferred by students, and (iii) if providing students a choice of timer impacted their performance.