About
I design and research STEM education experiences and technologies, with a longstanding interest in understanding how people engage and learn with data across a range of settings. Other current lines of work include AI literacy, elementary school computer science education, and science teaching and learning.
Research topics
- Computer Science
- Artificial Intelligence
- Psychology
- Pedagogy
- Sociology
- Epistemology
- Political Science
- Social psychology
- Management science
- Engineering ethics
- Applied psychology
- Mathematics
- Mathematics education
- Human–computer interaction
Selected publications
Clinical Cancer Research · 2026-02-17
articleAbstract Background: Clinical trials are critical to advancing cancer care but often raise patient concerns regarding treatment delays, additional procedures and visits, and toxicity. Understanding factors that influence trial enrollment decisions is essential for patient-centered trial design. Window of opportunity trials, which introduce investigational therapies between diagnosis and standard treatment initiation and are increasingly important in breast cancer research, exemplify a design where these concerns may be magnified. Limited data exist on patients’ evaluation of participation in such trials. Methods: We conducted a randomized survey study to determine patients’ willingness to participate in a window of opportunity trial. Adults (≥18 years) with a self-reported history of stage 0-III breast cancer were recruited via advocacy organizations, online social media platforms, and IRB-approved outreach at breast oncology clinics. All participants were first presented with a standardized definition and visual representation of a window of opportunity trial. They were then randomized in a 2:1 ratio to receive the primary endpoint question assessing willingness to participate either after exposure to reflective questions about potential barriers (e.g., additional biopsies, clinic visits, treatment delays) and motivators (e.g., desire to contribute to science), or immediately without prior reflective questions. Participants were also asked to answer questions about demographics and clinical characteristics as well as attitudes toward clinical research using the Corbie-Smith Index. The primary endpoint was willingness to participate, assessed on a Likert scale. Results: Of 1,104 total responses, 874 met eligibility criteria and 749 completed the primary endpoint question and were included in the analytic cohort. Respondents were primarily female (99%), non-Hispanic White (76%), and college-educated (64%). Among survey respondents, those Participants exposed to reflective questions before the primary question were significantly more likely to express willingness to enroll in a window of opportunity trial (76% vs 64%, OR 1.88, 95% CI 1.22-2.90; P < 0.001). Most demographic and clinical features did not correlate with willingness to participate, including age, race and ethnicity, income, location of treatment (academic vs community), geographic location (urban vs suburban vs rural), time since breast cancer diagnosis, nodal involvement, patient assessment of risk of recurrence, and initial treatment after diagnosis (surgery vs systemic therapy); an exception was that willingness was higher among those without a college degree (80% vs. 71%; P = 0.027), not significant after multiple hypothesis testing. Research distrust (Corbie-Smith Index) was significantly associated with lower willingness to participate (P < 0.001). Willing participants were more open to additional procedures, with 93% accepting at least one biopsy (vs. 57% of those unwilling) and 48% accepting five or more extra clinic visits (vs. 23% of those unwilling). Conclusions: After exposure to reflective questions addressing potential barriers and motivators, participants had significantly greater willingness to participate in a window of opportunity trial. The effect of reflective question exposure on willingness to participate was substantially greater than that of any demographic or clinical characteristic measured in our survey, emphasizing the potential of simple, scalable behavioral strategies to improve patient engagement in clinical trials. These results have important implications for the development of both digital tools and face-to-face communication strategies to enhance clinical trial enrollment and patient satisfaction in early-stage breast cancer treatment Citation Format: C. Bergstrom, D. Parikh, S. Brain, D. Heditsian, V. Lee, B. Shaw, C. Thompson, G. Sledge Jr, J. Caswell-Jin. A randomized survey study of structured reflective questions and willingness to participate in window of opportunity trials [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS5-10-30.
Educational Technology Research and Development · 2026-02-03
articleOpen accessAbstract This study examines the evolving relationship between AI chatbots and academic integrity and students’ AI chatbot usage in high schools one and a half years after the release of ChatGPT. Through a comprehensive survey of students across six schools (N = 4,354) in the United States, we investigated students' self-reported cheating behaviors, patterns of AI use across different school-related tasks, and student perspectives on appropriate AI use in academic settings. Our findings revealed that overall cheating rates remain stable at 72.06%, consistent with historical baselines and prior studies, suggesting that AI availability has not changed overall cheating prevalence in high school. Additionally, more students reported using AI chatbots for support tasks like concept explanation and idea generation. Regarding students' reported preferences for allowing AI chatbots for school-related tasks, at this point, they still strongly supported using AI for conceptual understanding and brainstorming, and they maintained clear boundaries against using it for completing entire assignments. These findings suggest that while AI’s prevalence has not altered the patterns of academic integrity at schools, students' evolving perspectives on appropriate AI use provide valuable insights for schools and administrators integrating AI into traditional school settings.
Figshare · 2026-05-08
datasetOpen access1st authorCorrespondingThis paper presents findings from a design-based research project that integrates quantitative text analytics data visualizations into middle school English Language Arts (ELA) classrooms, with particular attention to multilingual learners. In collaboration with a public school district, we co-designed data visualizations (e.g., word clouds, word frequency graphs) and instructional routines to support literary engagement and text comprehension using quantitative features of text. Drawing on survey data and classroom video excerpts, we examined how students engaged with these materials and how their perceptions of reading and data work changed. Participating teachers aided in visualization and routine design and implemented visualization use at least three times in their classroom. A control group of teachers proceeded with existing curriculum and activities. Compared to a control group, students who participated in these activities—especially those still developing English proficiency—showed more positive shifts in attitudes toward reading and working with data. These findings suggest that lightweight data integrations in ELA can support students' interpretive practices that are emphasized in ELA classrooms while also promoting data literacy, particularly for linguistically diverse populations.
arXiv (Cornell University) · 2026-04-08
preprintOpen access1st authorCorrespondingEnabling AI literacy in the general population at scale is a complex challenge requiring multiple stakeholders and institutions collaborating together. Industry and technology companies are important actors with respect to AI, and as a field, we have the opportunity to consider how researchers and companies might be partners toward shared goals. In this symposium, we focus on a collection of partnership projects that all involve Google and all address AI literacy as a comparative set of examples. Through a combination of presentations, commentary, and moderated group discussion, the session, we will identify (1) at what points in the life cycle do research, practice, and industry partnerships clearly intersect; (2) what factors and histories shape the directional focus of the partnerships; and (3) where there may be future opportunities for new configurations of partnership that are jointly beneficial to all parties.
arXiv (Cornell University) · 2026-04-08
articleOpen access1st authorCorrespondingEnabling AI literacy in the general population at scale is a complex challenge requiring multiple stakeholders and institutions collaborating together. Industry and technology companies are important actors with respect to AI, and as a field, we have the opportunity to consider how researchers and companies might be partners toward shared goals. In this symposium, we focus on a collection of partnership projects that all involve Google and all address AI literacy as a comparative set of examples. Through a combination of presentations, commentary, and moderated group discussion, the session, we will identify (1) at what points in the life cycle do research, practice, and industry partnerships clearly intersect; (2) what factors and histories shape the directional focus of the partnerships; and (3) where there may be future opportunities for new configurations of partnership that are jointly beneficial to all parties.
Figshare · 2026-05-08
datasetOpen access1st authorCorrespondingThis paper presents findings from a design-based research project that integrates quantitative text analytics data visualizations into middle school English Language Arts (ELA) classrooms, with particular attention to multilingual learners. In collaboration with a public school district, we co-designed data visualizations (e.g., word clouds, word frequency graphs) and instructional routines to support literary engagement and text comprehension using quantitative features of text. Drawing on survey data and classroom video excerpts, we examined how students engaged with these materials and how their perceptions of reading and data work changed. Participating teachers aided in visualization and routine design and implemented visualization use at least three times in their classroom. A control group of teachers proceeded with existing curriculum and activities. Compared to a control group, students who participated in these activities—especially those still developing English proficiency—showed more positive shifts in attitudes toward reading and working with data. These findings suggest that lightweight data integrations in ELA can support students' interpretive practices that are emphasized in ELA classrooms while also promoting data literacy, particularly for linguistically diverse populations.
Journal of Statistics and Data Science Education · 2026-05-08
articleOpen access1st authorCorrespondingThis paper presents findings from a design-based research project that integrates quantitative text analytics data visualizations into middle school English Language Arts (ELA) classrooms, with particular attention to multilingual learners. In collaboration with a public school district, we co-designed data visualizations (e.g., word clouds, word frequency graphs) and instructional routines to support literary engagement and text comprehension using quantitative features of text. Drawing on survey data and classroom video excerpts, we examined how students engaged with these materials and how their perceptions of reading and data work changed. Participating teachers aided in visualization and routine design and implemented visualization use at least three times in their classroom. A control group of teachers proceeded with existing curriculum and activities. Compared to a control group, students who participated in these activities—especially those still developing English proficiency—showed more positive shifts in attitudes toward reading and working with data. These findings suggest that lightweight data integrations in ELA can support students' interpretive practices that are emphasized in ELA classrooms while also promoting data literacy, particularly for linguistically diverse populations.
Teaching high school students about generative AI: Cases of teacher lesson design
The Journal of Educational Research · 2025-06-11 · 6 citations
articleSenior authorProceedings. · 2025-06-10
articleOpen accessSenior authorWhile various frameworks exist to promote K-12 AI education, little is known about how teachers bring AI literacy into their classrooms.In this study, we examined how two teachers designed and implemented AI literacy in their individual classes.Using epistemic network analysis (ENA) to analyze co-design records and interviews, we found that both teachers shared a common goal of enhancing student learning with AI but diverged in how they emphasized foundational and technical understanding of AI versus responsible and practical use.These varied approaches highlight the complexity of AI literacy and illustrate that a onesize-fits-all model does not apply.By examining these multiple pathways, we offer insight into how educators can tailor AI literacy instruction to meet diverse student needs and instructional contexts.
User-Developer-Critic (UDC) Perspectives in AI Literacy Framework Documents
2025-10-14
article1st authorCorrespondingAI literacy is increasingly discussed but does not have an agreed-upon definition or operationalization, specifically for curriculum and instructional design guidance. Different groups have tried to address this by producing framework documents that articulate key ideas and activities for AI literacy. In order to help characterize the broad set of AI literacy frameworks that are emerging, Lee and Long [1] proposed the User-Developer-Critic (UDC) perspective space model as a meta-framework. UDC posits that AI literacy frameworks exist in a space with emphases on the perspectives of users, developers, and critics. To illustrate one use of this meta-framework, this paper presents a keyword analysis of five framework documents relative to the three perspectives of UDC. In doing this analysis, some differences appear between framework documents, such as more or less emphasis on developer perspectives and ideas about the mechanics of AI technology. Additionally, critic perspectives are present, but do not appear to be dominant in any of the framework documents.
Recent grants
NSF · $324k · 2019–2021
CAREER: Engaging Elementary Students in Data Analysis through Study of Physical Activities
NSF · $680k · 2011–2018
NSF · $300k · 2016–2019
NSF · $23k · 2019–2020
Frequent coauthors
- 30 shared
Jody Clarke‐Midura
- 24 shared
Jessica F. Shumway
Utah State University
- 20 shared
Mimi Recker
- 11 shared
Deborah Silvis
Utah State University
- 11 shared
Joel Drake
Brigham Young University - Idaho
- 11 shared
Ryan Cain
Weber State University
- 9 shared
Ilana Dubovi
- 9 shared
Bruce Sherin
Awards & honors
- National Science Foundation CAREER award
- National Academy of Education/Spencer Foundation Postdoctora…
- American Educational Research Association’s Jan Hawkins Awar…
- various best paper awards
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