arXiv subjects

Julie A. Kientz

Publications and source records attributed to Julie A. Kientz.

7 recordsLinked to original sources

arXiv2026

SAVEstate: A Method for Documenting Player Reflection in Digital Games

In recent years, interest in eudaimonic player experiences (PX) - concerning reflection, meaning-making, and personal growth - has increased. However, most games user research methods are not well-suited to study eudaimonic PX, as they have been developed to evaluate features of hedonic PX, such as flow, immersion, and playability. To more deeply explore eudaimonic PX, we require methods that can 1) investigate how moment-to-moment PX shapes player reflection and 2) explore how players reengage and reinterpret their experiences longitudinally. In this paper, we present SAVEstate, a method that uses documentation as a means of studying player reflection. SAVEstate consists of two phases: reflection-in-action and reflection-on-action, which allow researchers to probe players' in-situ reflections and how they reengage with their gameplay, respectively. Using SAVEstate, we were able to observe in-situ meaning-making and connect it to post-game reflection-on-action, and view synchronous sensemaking across multiple participants. We also developed an open-source desktop application for researchers to adapt in their own SAVEstate deployments. We discuss implications for how researchers might use SAVEstate to conduct future research in meaningful PX.

cs.HC↗

arXiv2026

Toys that listen, talk, and play: Understanding Children's Sensemaking and Interactions with AI Toys

Generative AI (genAI) is increasingly being integrated into children's everyday lives, not only through screens but also through so-called "screen-free" AI toys. These toys can simulate emotions, personalize responses, and recall prior interactions, creating the illusion of an ongoing social connection. Such capabilities raise important questions about how children understand boundaries, agency, and relationships when interacting with AI toys. To investigate this, we conducted two participatory design sessions with eight children ages 6-11 where they engaged with three different AI toys, shifting between play, experimentation, and reflection. Our findings reveal that children approached AI toys with genuine curiosity, profiling them as social beings. However, frequent interaction breakdowns and mismatches between apparent intelligence and toy-like form disrupted expectations around play and led to adversarial play. We conclude with implications and design provocations to navigate children's encounters with AI toys in more transparent, developmentally appropriate, and responsible ways.

cs.HC↗

arXiv2026

Where Does AI Leave a Footprint? Children's Reasoning About AI's Environmental Costs

Two of the most socially consequential issues facing today's children are the rise of artificial intelligence (AI) and the rapid changes to the earth's climate. Both issues are complex and contested, and they are linked through the notable environmental costs of AI use. Using a systems thinking framework, we developed an interactive system called Ecoprompt to help children reason about the environmental impact of AI. EcoPrompt combines a prompt-level environmental footprint calculator with a simulation game that challenges players to reason about the impact of AI use on natural resources that the player manages. We evaluated the system through two participatory design sessions with 16 children ages 6-12. Our findings surfaced children's perspectives on societal and environmental tradeoffs of AI use, as well as their sense of agency and responsibility. Taken together, these findings suggest opportunities for broadening AI literacy to include systems-level reasoning about AI's environmental impact.

cs.HC↗

arXiv2025

Relief or displacement? How teachers are negotiating generative AI's role in their professional practice

As generative AI (genAI) rapidly enters classrooms, accompanied by district-level policy rollouts and industry-led teacher trainings, it is important to rethink the canonical ``adopt and train'' playbook. Decades of educational technology research show that tools promising personalization and access often deepen inequities due to uneven resources, training, and institutional support. Against this backdrop, we conducted semi-structured interviews with 22 teachers from a large U.S. school district that was an early adopter of genAI. Our findings reveal the motivations driving adoption, the factors underlying resistance, and the boundaries teachers negotiate to align genAI use with their values. We further contribute by unpacking the sociotechnical dynamics -- including district policies, professional norms, and relational commitments -- that shape how teachers navigate the promises and risks of these tools.

cs.HC↗

arXiv2025

"If anybody finds out you are in BIG TROUBLE": Understanding Children's Hopes, Fears, and Evaluations of Generative AI

As generative artificial intelligence (genAI) increasingly mediates how children learn, communicate, and engage with digital content, understanding children's hopes and fears about this emerging technology is crucial. In a pilot study with 37 fifth-graders, we explored how children (ages 9-10) envision genAI and the roles they believe it should play in their daily life. Our findings reveal three key ways children envision genAI: as a companion providing guidance, a collaborator working alongside them, and a task automator that offloads responsibilities. However, alongside these hopeful views, children expressed fears about overreliance, particularly in academic settings, linking it to fears of diminished learning, disciplinary consequences, and long-term failure. This study highlights the need for child-centric AI design that balances these tensions, empowering children with the skills to critically engage with and navigate their evolving relationships with digital technologies.

cs.HC↗

arXiv2025

Children's Mental Models of AI Reasoning: Implications for AI Literacy Education

As artificial intelligence (AI) advances in reasoning capabilities, most recently with the emergence of Large Reasoning Models (LRMs), understanding how children conceptualize AI's reasoning processes becomes critical for fostering AI literacy. While one of the "Five Big Ideas" in AI education highlights reasoning algorithms as central to AI decision-making, less is known about children's mental models in this area. Through a two-phase approach, consisting of a co-design session with 8 children followed by a field study with 106 children (grades 3-8), we identified three models of AI reasoning: Deductive, Inductive, and Inherent. Our findings reveal that younger children (grades 3-5) often attribute AI's reasoning to inherent intelligence, while older children (grades 6-8) recognize AI as a pattern recognizer. We highlight three tensions that surfaced in children's understanding of AI reasoning and conclude with implications for scaffolding AI curricula and designing explainable AI tools.

cs.AI↗

arXiv2025

"AI just keeps guessing": Using ARC Puzzles to Help Children Identify Reasoning Errors in Generative AI

The integration of generative Artificial Intelligence (genAI) into everyday life raises questions about the competencies required to critically engage with these technologies. Unlike visual errors in genAI, textual mistakes are often harder to detect and require specific domain knowledge. Furthermore, AI's authoritative tone and structured responses can create an illusion of correctness, leading to overtrust, especially among children. To address this, we developed AI Puzzlers, an interactive system based on the Abstraction and Reasoning Corpus (ARC), to help children identify and analyze errors in genAI. Drawing on Mayer & Moreno's Cognitive Theory of Multimedia Learning, AI Puzzlers uses visual and verbal elements to reduce cognitive overload and support error detection. Based on two participatory design sessions with 21 children (ages 6 - 11), our findings provide both design insights and an empirical understanding of how children identify errors in genAI reasoning, develop strategies for navigating these errors, and evaluate AI outputs.

cs.HC↗