Tootfinder

Intergenerational AI Literacy in Korean Immigrant Families: Interpretive Gatekeeping Meets Convenient Critical Deferment
Jeongone Seo, Ryan Womack, Tawfiq Ammari
https://arxiv.org/abs/2506.10197

Intergenerational AI Literacy in Korean Immigrant Families: Interpretive Gatekeeping Meets Convenient Critical Deferment
As artificial intelligence (AI) becomes deeply integrated into family life, immigrant families must navigate unique intergenerational, linguistic, and cultural challenges. This study examines how Korean immigrant families in the United States negotiate the use of AI tools such as ChatGPT and smart assistants in their homes. Through 20 semi-structured interviews with parents and teens, we identify two key practices that shape their engagement: interpretive gatekeeping, where parents mediate thei…

Generative AI in Science: Applications, Challenges, and Emerging Questions
Ryan Harries, Cornelia Lawson, Philip Shapira
https://arxiv.org/abs/2507.08310 h…

Generative AI in Science: Applications, Challenges, and Emerging Questions
This paper examines the impact of Generative Artificial Intelligence (GenAI) on scientific practices, conducting a qualitative review of selected literature to explore its applications, benefits, and challenges. The review draws on the OpenAlex publication database, using a Boolean search approach to identify scientific literature related to GenAI (including large language models and ChatGPT). Thirty-nine highly cited papers and commentaries are reviewed and qualitatively coded. Results are cat…

A Practical SAFE-AI Framework for Small and Medium-Sized Enterprises Developing Medical Artificial Intelligence Ethics Policies
Ion Nemteanu, Adir Mancebo Jr., Leslie Joe, Ryan Lopez, Patricia Lopez, Warren Woodrich Pettine
https://arxiv.org/abs/2507.01304

A Practical SAFE-AI Framework for Small and Medium-Sized Enterprises Developing Medical Artificial Intelligence Ethics Policies
Artificial intelligence (AI) offers incredible possibilities for patient care, but raises significant ethical issues, such as the potential for bias. Powerful ethical frameworks exist to minimize these issues, but are often developed for academic or regulatory environments and tend to be comprehensive but overly prescriptive, making them difficult to operationalize within fast-paced, resource-constrained environments. We introduce the Scalable Agile Framework for Execution in AI (SAFE-AI) desig…

Transformers for Stratified Spectropolarimetric Inversion: Proof of Concept
Ryan James Campbell, Mihalis Mathioudakis, Carlos Quintero Noda
https://arxiv.org/abs/2506.16810

Transformers for Stratified Spectropolarimetric Inversion: Proof of Concept
Solar spectropolarimetric inversion -- inferring atmospheric conditions from the Stokes vector -- is a key diagnostic tool for understanding solar magnetism, but traditional inversion methods are computationally expensive and sensitive to local minima. Advances in artificial intelligence (AI) offer faster solutions, but are often restricted to shallow models or a few spectral lines. We present a proof-of-concept study using a transformer machine learning (ML) model for multi-line, full-Stokes i…