arXiv subjects

Jaemarie Solyst

Publications and source records attributed to Jaemarie Solyst.

7 recordsLinked to original sources

arXiv2026

Designing Transformational Games to Support Socio-ethical Reasoning about Generative AI

There is an increasing need for young people to become critically AI literate, understanding not only how AI works but also its limitations and ethical nuances. Yet, designing learning experiences that make such complex, serious topics engaging remains a challenge. This paper explores transformational games as a promising approach for supporting youth learning about generative AI (GenAI) and ethics. We designed and implemented two games, Diversity Duel and Secret Agent, that integrate GenAI tools with gameplay elements. This work investigates how the games' elements: (1) peer evaluation, (2) constraint-based creativity, and (3) social deduction supported socio-ethical reasoning about GenAI. Participants recognized and debated bias in GenAI outputs, connected these patterns to real-world inequities, and developed nuanced understandings of bias. Participants further came to see how prompt design shapes AI behavior. Our findings suggest that group-based games with these elements can support fostering critical AI literacy.

cs.HC↗

arXiv2025

"Grillz on a hijabi": Intersectional Identities in Fostering Critical AI Literacy

As AI increasingly saturates our daily lives, it is crucial that youth develop skills to critically use and assess AI systems and envision better alternatives. We apply theories from culturally responsive computing to design and study a learning experience meant to support Black Muslim teen girls in developing critical literacy with generative AI (GenAI). We investigate fashion design as a culturally-rich, creative domain for youth to apply GenAI and then reflect on GenAI's socio-ethical aspects in relation to their own intersectional identities. Through a case study of a three-day, voluntary informal education program, we show how fashion design with GenAI exposed affordances and limitations of current GenAI tools. As the girls used GenAI to create realistic depictions of their dream fashion collections, they encountered socio-ethical limitations of AI, such as biased models and malfunctioning safety systems that prohibited their generation of outputs that reflected their creative ideas, bodies, and cultures. Discussions anchored in the phenomenology of impossible creative realization supported participants' development of critical AI literacy and descriptions of how preferable, identity-affirming technologies would behave. Our findings contribute to the field's growing understanding of how computing education experience designs linking creativity and identity can support critical AI literacy development.

cs.HC↗

arXiv2025

Investigating Youth AI Auditing

Youth are active users and stakeholders of artificial intelligence (AI), yet they are often not included in responsible AI (RAI) practices. Emerging efforts in RAI largely focus on adult populations, missing an opportunity to get unique perspectives of youth. This study explores the potential of youth (teens under the age of 18) to engage meaningfully in RAI, specifically through AI auditing. In a workshop study with 17 teens, we investigated how youth can actively identify problematic behaviors in youth-relevant ubiquitous AI (text-to-image generative AI, autocompletion in search bar, image search) and the impacts of supporting AI auditing with critical AI literacy scaffolding with guided discussion about AI ethics and an auditing tool. We found that youth can contribute quality insights, shaped by their expertise (e.g., hobbies and passions), lived experiences (e.g., social identities), and age-related knowledge (e.g., understanding of fast-moving trends). We discuss how empowering youth in AI auditing can result in more responsible AI, support their learning through doing, and lead to implications for including youth in various participatory RAI processes.

cs.HC↗

arXiv2024

Children's Overtrust and Shifting Perspectives of Generative AI

The capabilities of generative AI (genAI) have dramatically increased in recent times, and there are opportunities for children to leverage new features for personal and school-related endeavors. However, while the future of genAI is taking form, there remain potentially harmful limitations, such as generation of outputs with misinformation and bias. We ran a workshop study focused on ChatGPT to explore middle school girls' (N = 26) attitudes and reasoning about how genAI works. We focused on girls who are often disproportionately impacted by algorithmic bias. We found that: (1) middle school girls were initially overtrusting of genAI, (2) deliberate exposure to the limitations and mistakes of generative AI shifted this overtrust to disillusionment about genAI capabilities, though they were still optimistic for future possibilities of genAI, and (3) their ideas about school policy were nuanced. This work informs how children think about genAI like ChatGPT and its integration in learning settings.

cs.HC↗

arXiv2023

Comparative Design-Based Research: How Afterschool Programs Impact Learners' Engagement with a Video Game Codesign

Community-based afterschool programs are valuable spaces for researchers to codesign technologies with direct relevance to local communities. However, afterschool programs differ in resources available, culture, and student demographics in ways that may impact the efficacy of the codesign process and outcome. We ran a series of multi-week educational game codesign workshops across five programs over twenty weeks and found notable differences, despite deploying the same protocol. Our findings characterize three types of programs: Safe Havens, Recreation Centers, and Homework Helpers. We note major differences in students' patterns of participation directly influenced by each program's culture and expectations for equitable partnerships and introduce Comparative Design-Based Research (cDBR) as a beneficial lens for codesign.

cs.HC↗

arXiv2023

Making Sense of Machine Learning: Integrating Youth's Conceptual, Creative, and Critical Understandings of AI

Understanding how youth make sense of machine learning and how learning about machine learning can be supported in and out of school is more relevant than ever before as young people interact with machine learning powered applications everyday; while connecting with friends, listening to music, playing games, or attending school. In this symposium, we present different perspectives on understanding how learners make sense of machine learning in their everyday lives, how sensemaking of machine learning can be supported in and out of school through the construction of applications, and how youth critically evaluate machine learning powered systems. We discuss how sensemaking of machine learning applications involves the development and integration of conceptual, creative, and critical understandings that are increasingly important to prepare youth to participate in the world.

cs.CY↗

arXiv2023

Investigating Girls' Perspectives and Knowledge Gaps on Ethics and Fairness in Artificial Intelligence in a Lightweight Workshop

Artificial intelligence (AI) is everywhere, with many children having increased exposure to AI technologies in daily life. We aimed to understand middle school girls' (a group often excluded group in tech) perceptions and knowledge gaps about AI. We created and explored the feasibility of a lightweight (less than 3 hours) educational workshop in which learners considered challenges in their lives and communities and critically considered how existing and future AI could have an impact. After the workshop, learners had nuanced perceptions of AI, understanding AI can both help and harm. We discuss design implications for creating educational experiences in AI and fairness that embolden learners.

cs.HC↗