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.

Publisher Copyright: © 2025 Copyright held by the owner/author(s).

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.

Publisher Copyright: © 2025 Copyright held by the owner/author(s).

As artificial intelligence (AI) has become prevalent in users’ daily lives, it is becoming critical for HCI designers to envision and design future human-AI interactions. Recent studies have highlighted the importance of exploring user-centered future design directions before implementing a technology in users’ lives. However, it is challenging for HCI designers to envision the societal impact of future technology that does not exist and understand potential users’ perceptions. Therefore, to comprehensively envision future human-AI interactions and their impact and elicit potential users’ perceptions of the future technologies, we created high-fidelity illustrations with designers and illustrators for immersive experience. Subsequently, through an online exhibition of these illustrations, we derived potential users’ perceptions, expectations, and concerns about the future. Based on our findings, we explored user-centered considerations for implementing AI in users’ daily lives through elaborately articulated human-AI interactions.

While the rise of AI has benefited professionals, it also induces technostress that threatens their expertise and jobs. To ensure the human-centered advancement of technology, a deep understanding of users technostress and how to cope with it is essential. Despite technostress having long been discussed, the growing integration of AI tools into professionals’ everyday work amplifies these challenges and calls for further exploration. Accordingly, this is a timely moment to examine their real-world experiences and voices. Thus, our study aims to investigate AI-induced technostress experienced by professionals, and the coping strategies they employ. Through focus group interviews with 19 professionals from diverse fields, we identified seven AI-induced technostressors and examined their coping strategies along two dimensions: stress Coping Style (problem-focused and emotion-focused) and Value Orientation (AI-oriented and humanness-oriented). Drawing on professionals’ coping strategies, we suggest practical implications to support users in coping with AI-induced technostress.

A generative session that directly involves users in the design process is an effective way to design user-centered experiences by uncovering intrinsic needs. However, engaging users who lack coding knowledge in AI system design poses significant challenges. Recognizing this, the recently revealed GPT-creating tool, which allows users to customize ChatGPT through simple dialog interactions, is a promising solution. We aimed to identify the possibility of using this tool to uncover intrinsic users’ needs and expectations towards AI. We conducted individual participatory design workshops with generative sessions focusing on middle-aged individuals. This approach helped us to delve into the latent needs and expectations of conversational AI among this demographic. We discovered a wide range of unexpressed needs and expectations for AI among them. Our research highlights the potential and value of using the GPT-creating tool as a design method, particularly for revealing the users’ unexpressed needs and expectations.

Migrants often face a complex situation for navigating services and administrative processes to transition and integrate into a new environment. They rely on guidance from service advisors and informal peers like other migrants to assist them. Service advisors try to understand, explore, and resolve migrant concerns through conversational interaction, while often seeking advice from other domain experts. The emergence of artificial intelligence (AI)-mediated communication holds the potential for enhancing communication between migrants and service advisors while leveraging the expertise of service advisors and facilitating migrants' autonomy in the integration process. Our research investigates the social context, mental models, and challenges encountered in guidance counseling for migrants in Finland, while examining what role AI-mediated communication can play in supporting the process. Based on our findings, we propose design implications for improving shared understanding between service advisors and migrants through salient summarization of conversations and creating collaborative visual narratives. Our findings also indicate the crucial role of leveraging peer-based knowledge between (and within) service advisors, domain experts, and migrants, which can be facilitated by AI-based tools. Our exploratory research offers insights into human-centered AI integration in the migration process, as well as the opportunities and challenges of using AI-mediated communication to improve the migrant experience.

Autistic individuals often draw on insights from their supportive networks to develop self-help life strategies ranging from everyday chores to social activities. However, human resources may not always be immediately available. Recently emerging conversational agents (CAs) that leverage large language models (LLMs) have the potential to serve as powerful information-seeking tools, facilitating autistic individuals to tackle daily concerns independently. This study explored the opportunities and challenges of LLM-driven CAs in empowering autistic individuals through focus group interviews and workshops (N=14). We found that autistic individuals expected LLM-driven CAs to offer a non-judgmental space, encouraging them to approach day-to-day issues proactively. However, they raised issues regarding critically digesting the CA responses and disclosing their autistic characteristics. Based on these findings, we propose approaches that place autistic individuals at the center of shaping the meaning and role of LLM-driven CAs in their lives, while preserving their unique needs and characteristics.