About

Rebecca Black is an associate professor in the Department of Informatics at the University of California, Irvine. She received her Ph.D. in Curriculum and Instruction from the University of Wisconsin, Madison in 2006 and her M.A. in Applied Linguistics from the University of Massachusetts, Boston in 2002. Her research interests center on how young people, particularly those who feel marginalized in traditional academic settings, are using new technologies to learn, create, and communicate.

Research topics

• Computer Science
• Political Science
• Sociology
• Psychology
• Engineering
• Artificial Intelligence
• Medicine
• Law
• Internet privacy
• Mathematics education
• Pedagogy
• Engineering ethics
• Public relations
• World Wide Web

Selected publications

• Students prefer personalized, AI-generated educational videos over non-personalized, human-recorded videos

  Scientific Reports · 2026-05-13

  articleOpen access

  Personalization is a well-established driver of student engagement, yet delivering individualized instruction at scale remains a challenge in online education. Recent advances in generative AI make scalable personalization feasible, but AI-generated educational videos are often perceived as inferior to human-recorded content. This tension raises the question: how does the value of personalization compare to that of human presence? We investigated this question through a field deployment in two offerings of a large undergraduate online course (493 respondents). AI-generated personalized videos served as the primary instructional modality, alongside a smaller set of non-personalized human-recorded and AI-generated videos. At the end of the course, students ranked preferences across personalized and non-personalized formats and human-recorded versus AI-generated content. In a direct comparison, students preferred AI-generated personalized videos over human-recorded non-personalized videos (mean rank 2.26 vs. 2.69; Wilcoxon signed-rank test, [Formula: see text]). Across analyses, students preferred personalized over non-personalized content, and human-recorded over AI-generated content. The magnitude of the personalization effect substantially exceeded the effect of human presence. Open-ended responses highlighted perceived benefits of relevance and conciseness in personalized AI videos, alongside concerns about naturalness and expressiveness. Together, these findings suggest that personalization can outweigh human presence in students' evaluations of educational video.

  PublisherOA PDFDOI

• Building writing identity and professional science writing skills in a lab report-free organic Chemistry II Laboratory

  2025-04-02

  preprint1st authorCorresponding
  PublisherDOI

• University students describe how they adopt AI for writing and research in a general education course

  Scientific Reports · 2025-03-14 · 44 citations

  articleOpen access1st authorCorresponding

  University students have begun to use Artificial Intelligence (AI) in many different ways in their undergraduate education, some beneficial to their learning, and some simply expedient to completing assignments with as little work as possible. This exploratory qualitative study examines how undergraduate students used AI in a large General Education course on sustainability and technology at a research university in the United States in 2023. Thirty-nine students documented their use of AI in their final course project, which involved analyzing conceptual networks connecting core sustainability concepts. Through iterative qualitative coding, we identified key patterns in students' AI use, including higher-order writing tasks (understanding complex topics, finding evidence), lower-order writing tasks (revising, editing, proofreading), and other learning activities (efficiency enhancement, independent research). Students primarily used AI to improve communication of their original ideas, though some leveraged it for more complex tasks like finding evidence and developing arguments. Many students expressed skepticism about AI-generated content and emphasized maintaining their intellectual independence. While some viewed AI as vital for improving their work, others explicitly distinguished between AI-assisted editing and their original thinking. This analysis provides insight into how students navigate AI use when it is explicitly permitted in coursework, with implications for effectively integrating AI into higher education to support student learning.

  PublisherOA PDFDOI

• Conducting a national expansion of a text-based argument writing intervention for secondary students using a cognitive strategies approach.

  Journal of Educational Psychology · 2025-03-06

  article
  PublisherDOI

• Animating queer figured worlds: How young adult animation is cultivating queer spaces and narratives

  Queer Studies in Media & Popular Culture · 2024-06-01 · 2 citations

  article

  This article explores how three contemporary animated television series for young people expand the concept of figured worlds to create queer figured worlds that challenge notions of both hetero- and homonormativity. Through a textual, thematic/detailed, qualitative content analysis of these shows, we demonstrate how the worldbuilding in these series goes beyond creating sites for queer resistance to heteronormativity by creating queer figured worlds. We examine three aspects of worldbuilding – characters, conflict and culture – and how they serve to resist stereotyping and normalization to present a diversity of queer experiences and provide a framework for queer people to imagine queer futures. Queer figured worlds can allow young people to imagine social roles and life possibilities that are not presented in traditional figured worlds.

  PublisherDOI

• Reconciling the contrasting narratives on the environmental impact of large language models

  Scientific Reports · 2024-11-01 · 37 citations

  articleOpen access

  The recent proliferation of large language models (LLMs) has led to divergent narratives about their environmental impacts. Some studies highlight the substantial carbon footprint of training and using LLMs, while others argue that LLMs can lead to more sustainable alternatives to current practices. We reconcile these narratives by presenting a comparative assessment of the environmental impact of LLMs vs. human labor, examining their relative efficiency across energy consumption, carbon emissions, water usage, and cost. Our findings reveal that, while LLMs have substantial environmental impacts, their relative impacts can be dramatically lower than human labor in the U.S. for the same output, with human-to-LLM ratios ranging from 40 to 150 for a typical LLM (Llama-3-70B) and from 1200 to 4400 for a lightweight LLM (Gemma-2B-it). While the human-to-LLM ratios are smaller with regard to human labor in India, these ratios are still between 3.4 and 16 for a typical LLM and between 130 and 1100 for a lightweight LLM. Despite the potential benefit of switching from humans to LLMs, economic factors may cause widespread adoption to lead to a new combination of human and LLM-driven work, rather than a simple substitution. Moreover, the growing size of LLMs may substantially increase their energy consumption and lower the human-to-LLM ratios, highlighting the need for further research to ensure the sustainability and efficiency of LLMs.

  PublisherOA PDFDOI

• The carbon emissions of writing and illustrating are lower for AI than for humans

  Scientific Reports · 2024-02-14 · 61 citations

  articleOpen access

  As AI systems proliferate, their greenhouse gas emissions are an increasingly important concern for human societies. In this article, we present a comparative analysis of the carbon emissions associated with AI systems (ChatGPT, BLOOM, DALL-E2, Midjourney) and human individuals performing equivalent writing and illustrating tasks. Our findings reveal that AI systems emit between 130 and 1500 times less CO2e per page of text generated compared to human writers, while AI illustration systems emit between 310 and 2900 times less CO2e per image than their human counterparts. Emissions analyses do not account for social impacts such as professional displacement, legality, and rebound effects. In addition, AI is not a substitute for all human tasks. Nevertheless, at present, the use of AI holds the potential to carry out several major activities at much lower emission levels than can humans.

  PublisherOA PDFDOI

• The Carbon Emissions of Writing and Illustrating Are Lower for AI than for Humans

  Research Square · 2023-03-29 · 2 citations

  preprintOpen access

  Abstract As AI systems proliferate, their greenhouse gas emissions are an increasingly important concern for human societies. In this article, we present a comparative analysis of the carbon emissions associated with AI systems (ChatGPT, BLOOM, DALL-E2, Midjourney) and human individuals performing equivalent writing and illustrating tasks. Our findings reveal that AI systems emit between 130 and 1500 times less CO2e per page of text generated compared to human writers, while AI illustration systems emit between 310 to 2900 times less CO2e per image than their human counterparts. Emissions analyses do not account for social impacts such as professional displacement, legality, and rebound effects. In addition, AI is not a substitute for all human tasks. Nevertheless, at present, the use of AI holds the potential to carry out several major activities at much lower emission levels than can humans.

  PublisherOA PDFDOI

• Late-Binding Scholarship in the Age of AI: Navigating Legal and Normative Challenges of a New Form of Knowledge Production

  arXiv (Cornell University) · 2023-05-04

  preprintOpen access

  Artificial Intelligence (AI) is poised to enable a new leap in the creation of scholarly content. New forms of engagement with AI systems, such as collaborations with large language models like GPT-3, offer affordances that will change the nature of both the scholarly process and the artifacts it produces. This article articulates ways in which those artifacts can be written, distributed, read, organized, and stored that are more dynamic, and potentially more effective, than current academic practices. Specifically, rather than the current "early-binding" process (that is, one in which ideas are fully reduced to a final written form before they leave an author's desk), we propose that there are substantial benefits to a "late-binding" process, in which ideas are written dynamically at the moment of reading. In fact, the paradigm of "binding" knowledge may transition to a new model in which scholarship remains ever "unbound" and evolving. An alternative form for a scholarly work could be encapsulated via several key components: a text abstract of the work's core arguments; hyperlinks to a bibliography of relevant related work; novel data that had been collected and metadata describing those data; algorithms or processes necessary for analyzing those data; a reference to a particular AI model that would serve as a "renderer" of the canonical version of the text; and specified parameters that would allow for a precise, word-for-word reconstruction of the canonical version. Such a form would enable both the rendering of the canonical version, and also the possibility of dynamic AI reimaginings of the text in light of future findings, scholarship unknown to the original authors, alternative theories, and precise tailoring to specific audiences (e.g., children, adults, professionals, amateurs).

  PublisherOA PDFDOI

• The Carbon Emissions of Writing and Illustrating Are Lower for AI than for Humans

  SSRN Electronic Journal · 2023-01-01 · 7 citations

  articleOpen access
  PublisherDOI

Frequent coauthors

• Bill Tomlinson

  University of California, Irvine

  38 shared

• Andrew W. Torrance

  26 shared

• Donald J. Patterson

  19 shared

• M. Six Silberman

  The London College

  7 shared

• Constance Steinkuehler

  6 shared

• Stephanie M. Reich

  University of California, Irvine

  6 shared

• Gillian R. Hayes

  University of California, Irvine

  6 shared

• Paramdeep S. Atwal

  University of California, Irvine

  6 shared