About

Shayan Doroudi's research is focused on the learning sciences, educational technology, and the educational data sciences. He is particularly interested in studying the prospects and limitations of data-driven algorithms in learning technologies, including equity considerations and lessons that can be drawn from the rich history of educational technology.

Research topics

• Computer Science
• Sociology
• Political Science
• Psychology
• Artificial Intelligence
• Pedagogy
• Data science
• Engineering ethics
• Cognitive science
• Engineering

Selected publications

• What undergraduate students need to know and actually know about generative AI

  Computers and Education Artificial Intelligence · 2026-02-07

  articleOpen accessSenior author

  In November 2022, the release of ChatGPT sparked widespread adoption of generative AI chatbots among students, yet little is known about what undergraduate students actually understand about these tools or how accurately they perceive their capabilities. To address this gap, we propose a theoretically grounded framework for Generative AI (GenAI) literacy that integrates three forms of conceptual knowledge about large language models (LLMs)—foundations, capabilities, and limitations, and societal impact—with students’ perceptions and folk theories of AI chatbots. We then developed and validated a GenAI literacy survey, including multiple-choice knowledge items and perception items, using expert review and item response theory (IRT) modeling. To examine GenAI literacy across different educational contexts, we conducted two complementary studies: Study 1 surveyed students enrolled in courses at a large public R1 university, and Study 2 surveyed a national sample of U.S. undergraduate students recruited via Prolific. Across both samples, approximately 60% of students reported using AI chatbots weekly or daily. However, many students overestimated chatbots’ capabilities, particularly on tasks involving reasoning and counting, and often anthropomorphized or treated chatbots as search engines. Knowledge scores were higher among computer science students at the R1 university and frequent chatbot users, while perception accuracy varied by group. Critically, greater conceptual knowledge was associated with less overestimation of chatbot abilities, suggesting that knowledge supports more calibrated and responsible use. This work introduces a validated framework and instrument for assessing GenAI literacy and highlights the need for AI literacy initiatives that move beyond tool usage to address misconceptions, beliefs, and responsible engagement with generative AI in higher education.

  PublisherDOI

• Learning Under Algorithmic Conditions

  University of Minnesota Press eBooks · 2026-04-23

  book
  PublisherDOI

• Artificial Integrity: Concerning Patterns of AI Usage Among Undergraduate Students

  2026-04-17

  articleOpen accessSenior author

  The introduction of large language models has intensified concerns around breaches of academic integrity in higher education. The current narrative on AI-enabled cheating among students has been largely shaped by survey-based measures in academic outlets and anecdotal evidence in media outlets, and personal experiences. We present a multi-pronged method for detecting Concerning AI Usage (CAI) in a preregistered study with 81 undergraduate students at an R1 university in the US. We find that—depending on which signals we rely upon to detect AI usage—41% to 70% of students engaged in CAI. We also investigate the relationships between CAI and demographic features, self-reported intellectual virtue, and learning outcomes, and show the limitations of relying on self-reports for AI usage and intellectual virtue.

  PublisherDOI

• Designing for AI-mediated Science Communication: Insights from Researchers’ Challenges, Experiences, and Perception

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• Artificial Integrity: Concerning Patterns of AI Usage Among Undergraduate Students

  EdArXiv (OSF Preprints) · 2026-04-13

  preprintOpen access1st authorCorresponding

  The introduction of large language models has intensified concerns around breaches of academic integrity in higher education. The current narrative on AI-enabled cheating among students has been largely shaped by survey-based measures in academic outlets and anecdotal evidence in media outlets, and personal experiences. We present a multi-pronged method for detecting Concerning AI Usage (CAI) in a preregistered study with 81 undergraduate students at an R1 university in the US. We find that—depending on which signals we rely upon to detect AI usage—41% to 70% of students engaged in CAI. We also investigate the relationships between CAI and demographic features, self-reported intellectual virtue, and learning outcomes, and show the limitations of relying on self-reports for AI usage and intellectual virtue.

  Publisher

• Artificial Integrity: Concerning Patterns of AI Usage Among Undergraduate Students

  2026-03-21

  articleOpen accessSenior author

  The introduction of large language models has intensified concerns around breaches of academic integrity in higher education. The current narrative on AI-enabled cheating among students has been largely shaped by survey-based measures in academic outlets and anecdotal evidence in media outlets, and personal experiences. We present a multi-pronged method for detecting Concerning AI Usage (CAI) in a preregistered study with 81 undergraduate students at an R1 university in the US. We find that—depending on which signals we rely upon to detect AI usage—41% to 70% of students engaged in CAI. We also investigate the relationships between CAI and demographic features, self-reported intellectual virtue, and learning outcomes, and show the limitations of relying on self-reports for AI usage and intellectual virtue.

  PublisherDOI

• Artificial Integrity: Concerning Patterns of AI Usage Among Undergraduate Students

  EdArXiv (OSF Preprints) · 2026-03-18

  preprintOpen access

  The introduction of large language models has intensified concerns around breaches of academic integrity in higher education. The current narrative on AI-enabled cheating among students has been largely shaped by survey-based measures in academic outlets and anecdotal evidence in media outlets, and personal experiences. We present a multi-pronged method for detecting Concerning AI Usage (CAI) in a preregistered study with 81 undergraduate students at an R1 university in the US. We find that—depending on which signals we rely upon to detect AI usage—41% to 70% of students engaged in CAI. We also investigate the relationships between CAI and demographic features, self-reported intellectual virtue, and learning outcomes, and show the limitations of relying on self-reports for AI usage and intellectual virtue.

  Publisher

• What Perceptrons Might Tell Us About Our Own Abilities

  Underline Science Inc. · 2025-06-18

  otherOpen accessSenior author

  Minsky and Papert's (1969) book Perceptrons is often remembered as the book that (counter-productively) ended neural network research for nearly two decades. One of the authors' main results was that perceptrons (under reasonable limitations) cannot detect if a pattern is fully connected. Perhaps less known, to their initial surprise, the authors also showed that if guaranteed there are no holes in an image, perceptrons can detect if a pattern is fully connected. Given the simplicity of perceptrons, it seems reasonable to think that they might suggest a lower bound for what humans can visually detect without moving their eyes. If so, the results on connectedness suggest some counter-intuitive findings about human perception, namely that we should be able to learn to solve 2D mazes at a glance and detect how many objects are in an image at a glance (i.e., subitize) even when the number is large.

  PublisherDOI

• The Evolution of Research on AI and Education Across Four Decades: Insights from the AIxEd Framework

  International Journal of Artificial Intelligence in Education · 2025-05-15 · 4 citations

  articleOpen accessSenior author

  Abstract This paper presents a new framework (AI $$\times $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> Ed) to categorize the various kinds of relationships between artificial intelligence (AI) and education in terms of two axes. Using this framework, we examine the evolution of the field of Artificial Intelligence in Education over four decades by examining papers published in AIED proceedings (1985, 1993, 2021, and 2024) and the International Journal of Artificial Intelligence in Education (2004, 2014, and 2021). We argue that AI’s role in education extends beyond its use as a practical tool for solving educational problems. AI also serves as a conceptual analogy for understanding human intelligence and learning. However, we show that this way of thinking about AI and education, which was once prevalent, has received much less focus in recent years. We suggest that the growing enthusiasm among researchers for using generative AI, as evidenced by papers in AIED 2024, offers opportunities to deepen our insights into student knowledge and learning processes. Finally, we propose new directions for future AIED research that span the different kinds of research in AI $$\times $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> Ed.

  PublisherOA PDFDOI

• TurtleBench: A Visual Programming Benchmark in Turtle Geometry

  2025-01-01

  articleOpen accessSenior author

  Sina Rismanchian, Yasaman Razeghi, Sameer Singh, Shayan Doroudi. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

  PublisherOA PDFDOI

Recent grants

• NSF2026: EAGER: Involving the Public in the Discovery of Undiscovered Public Knowledge

  NSF · $300k · 2020–2024

Frequent coauthors

• Emma Brunskill

  21 shared

• Sina Rismanchian

  7 shared

• Kenneth Holstein

  Carnegie Mellon University

  7 shared

• Vincent Aleven

  7 shared

• Philip S. Thomas

  5 shared

• Daniel A. Epstein

  University of California, Irvine

  5 shared

• David Van Nguyen

  University of California, Santa Cruz

  5 shared

• Zhaohan Daniel Guo

  3 shared