Recent advances in generative AI have positioned text-to-image models as promising creativity support tools. These systems enable visually compelling outputs through intuitive interactions, offering new avenues for creative expression even for non-expert users. Particularly for children, such tools lower the barriers to creative expression by allowing them to visualize their ideas. However, most existing tools are designed for adults, relying on complex prompts and emphasizing output quality over the creative process, which may limit opportunities for children to engage in open-ended ideation, reflection, and critical evaluation-key aspects of creative learning. To address these challenges, we developed KidPrompt, a learner-centered text-to-image generative AI tool that incorporates three design components: (1) keyword-based input to simplify prompt construction, (2) choice induction to encourage comparison across multiple generations, and (3) sequential creation to support iterative idea development. A comparative study with 12 children suggests that these components can scaffold broader idea exploration, reflective comparison, and iterative refinement during creative interaction. Rather than emphasizing only polished outputs, KidPrompt encouraged children to explore multiple directions and revisit their ideas over time through structured yet flexible interaction. Based on these findings, we propose design implications for integrating generative AI into children’s creativity support in more learner-centered and process-oriented ways.

As generative AI becomes embedded in adolescents’ everyday lives, it is important to understand what young users want from these systems, what concerns they hold, and how these perspectives should shape youth-centered AI design. This study investigates early adolescents’ perspectives on generative AI through a four-week art-based participatory workshop with 55 middle school students. Using comic storytelling, AI-themed clay modeling, and reflective discussions, participants expressed their understandings, concerns, and expectations through visual and narrative artifacts. Inductive qualitative analysis revealed recurring patterns in how youths imagined AI’s roles, articulated tensions around autonomy, dependency, and responsibility, and identified values important for maintaining agentic relationships with AI. Our findings show that participants came to recognize the importance of autonomy, critical understanding, and relational boundaries in engaging with AI. Based on these insights, we propose design implications for youth-centered AI systems that support agency and developmentally appropriate, ethically grounded engagement.