arXiv subjects

René Kizilcec

Publications and source records attributed to René Kizilcec.

6 recordsLinked to original sources

arXiv2026

Generative AI Produces and Transmits Beauty Bias

Societies reward people judged to be attractive by standards intertwined with gender and race. In this paper, we examine if and how generative AI may contribute to these dynamics. We audited 27 text-to-image generators released between 2022 and 2026 using simple prompts that never mention attractiveness or beauty, analyzing 51,300 generated faces and 160,196 photographs of real people across primary and supplementary comparisons. We used three automated measures and collected ratings from 550 human raters on a subset of images. Nearly all generators produced faces scoring above photographed faces under automated measures, and human raters judged the outputs of all 23 generators as significantly more attractive than photographed faces, even after correction for multiple comparisons. When focusing our analysis on gender, we find that 37% of female-prompted generated faces outscored the median professional fashion model. Across racial, ethnic, and national categories, groups associated with darker skin tones tended to receive lower attractiveness scores than groups associated with lighter skin tones. In a preregistered hiring simulation experiment (N = 570), exposure to AI-generated faces selected for high human-rated attractiveness lowered hireability and trustworthiness ratings and reduced interview advancement for an identical test set of candidates paired with photographs of real people; exposure to retouched photographs produced no such effect. Generative AI can reproduce racial hierarchies and gendered patterns of beauty and alter the context in which real people are judged.

cs.CY↗

arXiv2026

Sandpiper: Orchestrated AI-Annotation for Educational Discourse at Scale

Digital educational environments are expanding toward complex AI and human discourse, providing researchers with an abundance of data that offers deep insights into learning and instructional processes. However, traditional qualitative analysis remains a labor-intensive bottleneck, severely limiting the scale at which this research can be conducted. We present Sandpiper, a mixed-initiative system designed to serve as a bridge between high-volume conversational data and human qualitative expertise. By tightly coupling interactive researcher dashboards with agentic Large Language Model (LLM) engines, the platform enables scalable analysis without sacrificing methodological rigor. Sandpiper addresses critical barriers to AI adoption in education by implementing context-aware, automated de-identification workflows supported by secure, university-housed infrastructure to ensure data privacy. Furthermore, the system employs schema-constrained orchestration to eliminate LLM hallucinations and enforces strict adherence to qualitative codebooks. An integrated evaluations engine allows for the continuous benchmarking of AI performance against human labels, fostering an iterative approach to model refinement and validation. We propose a user study to evaluate the system's efficacy in improving research efficiency, inter-rater reliability, and researcher trust in AI-assisted qualitative workflows.

cs.HC↗

arXiv2026

Million Tutoring Moves (MTM): An Open Multimodal Dataset for the Science of Tutoring

We introduce the Million Tutoring Moves (MTM) project, an open dataset initiative aimed at advancing the science of tutoring through large-scale, reusable, and multimodal interaction data. MTM is developed within the National Tutoring Observatory (NTO), a research infrastructure designed to study authentic tutoring interactions and translate them into actionable insights for research, practice, and AI-powered educational technology development. In this paper, we present the vision behind MTM and describe MTM v1, an initial release consisting of 4,654 math tutoring transcripts from a U.S.-based nonprofit online tutoring platform. MTM v1 serves as a first step toward a broader repository that is safe, open, large-scale, broad-coverage, and multimodal. By making tutoring interactions systematically observable and analyzable, MTM aims to support research on instructional processes, improve tutoring practice, and enable the development of AI systems grounded in real educational interactions.

cs.CY↗

arXiv2026

Poor Alignment and Steerability of Large Language Models: Evidence from College Admission Essays

People are increasingly using technologies equipped with large language models (LLM) to write texts for formal communication, which raises two important questions at the intersection of technology and society: Who do LLMs write like (model alignment); and can LLMs be prompted to change who they write like (model steerability). We investigate these questions in the high-stakes context of undergraduate admissions at a selective university by comparing lexical and sentence variation between essays written by 30,000 applicants to two types of LLM-generated essays: one prompted with only the essay question used by the human applicants; and another with additional demographic information about each applicant. We consistently find that both types of LLM-generated essays are linguistically distinct from human-authored essays, regardless of the specific model and analytical approach. Further, prompting a specific sociodemographic identity is remarkably ineffective in aligning the model with the linguistic patterns observed in human writing from this identity group. This holds along the key dimensions of sex, race, first-generation status, and geographic location. The demographically prompted and unprompted synthetic texts were also more similar to each other than to the human text, meaning that prompting did not alleviate homogenization. These issues of model alignment and steerability in current LLMs raise concerns about the use of LLMs in high-stakes contexts.

cs.CL↗

arXiv2024

Implementing New Technology in Educational Systems

Educators are more than workers within educational systems; they are stewards of educational systems. They must analyze student performance data, identify patterns that inform targeted interventions and personalized learning plans, continuously develop the curriculum, set ambitious learning goals and use up-to-date pedagogical theory to adapt instructional strategies, act as advocates for educational policies that promote inclusivity and equity, and much more. Most educators deeply care about the learning and wellbeing of their students and colleagues. Given the chance, they will do whatever they can to make improvements to these ends. In this role as architects of change, educators deal with conflicting definitions of success, multiple stakeholders, complex causal relationships, ambiguous data, and intricate human factors. Amid all this, most educators and the educational systems around them are strained to the capacity of what their time, training, and budgets allow. The problem is not merely that they must perform demanding tasks, but more so that they must constantly implement improvements and interventions amid the complex challenges of the organizations in which they work. These challenges can be especially difficult in implementation of related education technology, which is continuously developing at sometimes rapid pace. Whether the context is an individual classroom, a school district, or a postsecondary institution, implementing beneficial human-technology partnerships requires attending to the needs and constraints of these classrooms, districts, institutions, and so forth as organizations and engaging in this work as a partnership with educators. This chapter lays out the principles and processes of developing successful educator-technology partnerships including key considerations for each step and an example protocol for engaging in this endeavor.

physics.ed-ph↗

arXiv2023

Assessing the Usability of GutGPT: A Simulation Study of an AI Clinical Decision Support System for Gastrointestinal Bleeding Risk

Applications of large language models (LLMs) like ChatGPT have potential to enhance clinical decision support through conversational interfaces. However, challenges of human-algorithmic interaction and clinician trust are poorly understood. GutGPT, a LLM for gastrointestinal (GI) bleeding risk prediction and management guidance, was deployed in clinical simulation scenarios alongside the electronic health record (EHR) with emergency medicine physicians, internal medicine physicians, and medical students to evaluate its effect on physician acceptance and trust in AI clinical decision support systems (AI-CDSS). GutGPT provides risk predictions from a validated machine learning model and evidence-based answers by querying extracted clinical guidelines. Participants were randomized to GutGPT and an interactive dashboard, or the interactive dashboard and a search engine. Surveys and educational assessments taken before and after measured technology acceptance and content mastery. Preliminary results showed mixed effects on acceptance after using GutGPT compared to the dashboard or search engine but appeared to improve content mastery based on simulation performance. Overall, this study demonstrates LLMs like GutGPT could enhance effective AI-CDSS if implemented optimally and paired with interactive interfaces.

cs.HC↗