About

Mark Warschauer is a Professor of Education at the University of California, Irvine, with affiliated appointments in the Department of Informatics, the Department of Language Science, and the Department of Psychological Science. He is also the director of the Digital Learning Lab at UC Irvine, where he collaborates with colleagues and students on research projects related to digital media and artificial intelligence in education. He is a member of the National Academy of Education, reflecting his significant contributions to the field of educational research and digital learning.

Research topics

• Computer Science
• Pedagogy
• Psychology
• Mathematics education
• Data science
• Medical education
• Political Science
• Data Mining
• Sociology
• Medicine
• World Wide Web
• Artificial Intelligence
• Engineering ethics
• Multimedia
• Linguistics
• Developmental psychology
• Mathematics
• Communication
• Engineering
• Programming language
• Geography
• Human–computer interaction

Selected publications

• Fine-Tuning the Educational Design Process: Lessons from Sustained Development of a Generative AI-Based Classroom Technology

  2025-10-03

  articleOpen accessSenior author

  Generative AI is rapidly entering classrooms, yet its evolving affordances and sociotechnical volatility make educational design challenging. Prior research has surfaced design guidelines based on short-term pilots. We present a three-year participatory design case study of a generative AI–based writing tutor deployed across higher education and K–12 contexts. Drawing on design artifacts, observations, interviews, and focus groups, we analyze over 100 design decisions to trace how the system was shaped. Our findings highlight four contributions: (1) a schema of values-based and constraint-driven design decisions for educational generative AI, (2) attention to rapid evolution of perceptions in design and adoption of new educational technologies, (3) principles for sustaining participatory design with teachers “in the loop”, and (4) a discussion of the utility of designing with weaknesses in technology. We extend HCI scholarship on emerging technologies by demonstrating how iterative, sustained, cross-contextual design can yield durable lessons amidst rapid technological change.

  PublisherOA PDFDOI

• What Do Secondary History Teachers Know About Teaching Writing? A Teacher Knowledge of Writing Instruction Measure

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Humanizing AI for Education: Conversations with the JLS 2026 Special Issue Contributors

  Proceedings. · 2025-06-10 · 1 citations

  articleOpen access

  This symposium will bring together the eight invited contributors for the 2026 special issue of the Journal of the Learning Sciences (JLS), which explores how educational researchers can humanize their designs of AI tools and activities for education.Each contributing author brings a unique perspective on what it means to humanize AI for education, working with different contexts, ages, and learning outcomes.Papers focus on a variety of layers of AI, including designing effective AI tools for learning, understanding how existing AI activities influence the learning process, and developing learners' literacies around ethical AI.In this session, authors will engage with attendees in rich conversations around how we should thoughtfully and ethically design AI futures in the learning sciences that center the needs of teachers, learners, their families, and their communities. Symposium overviewThere is an urgent need to thoughtfully integrate human-centered learning theories into the design and teaching of AI, highlighted by the public conversations occurring in governmental, educational, and industry sectors that seek to establish shared norms for how and why AI tools are used (e.g., The White House, 2023; UNESCO, 2023; Software & Industry Information Association, 2023).At the center of these discussions is a tension between technocentric views of AI that seek to improve the effectiveness and expand the reach of AI innovations and human-centered approaches that instead focus on the ethical, social, and pedagogical aspects of how AI impacts our classrooms and our world (Selwyn, 2024;Akgun & Greenhow, 2022).In this session, eight author teams who

  PublisherOA PDFDOI

• Co-designing Curriculum to Discuss Environmental Disparities Using Data Science

  2025-02-18

  articleSenior author
  PublisherDOI

• StoryPal: Supporting Young Children's Dialogic Reading with Large Language Models

  2025-06-23 · 3 citations

  articleOpen accessSenior author
  PublisherDOI

• From Wandering to Collaboration: Discourse Patterns in Middle School Generative AI Use

  2025-10-03

  articleOpen accessSenior author

  Generative artificial intelligence (AI) has become a prominent presence in classrooms, yet relatively little is known about how students actually engage with such tools in authentic school contexts. This study examines more than 17,000 messages across 1,512 conversations from 484 middle school students using a classroom-based generative AI writing tutor. We extracted linguistic, cognitive, and interactional features, reduced dimensionality with principal component analysis, and applied clustering to identify conversation and student-level patterns of engagement. Results revealed five conversation profiles—ranging from directive to reflective dialogue—and four student profiles, including collaborators, transactional tool users, independent thinkers, and chatters. These patterns aligned with both pedagogical intent and individual orientation, underscoring that student–AI dialogue is heterogeneous but systematic. Findings contribute empirical evidence to debates about generative AI in education and provide a methodological framework for analyzing human–AI interaction in learning analytics

  PublisherDOI

• Anchor Is the Key: Toward Accessible Automated Essay Scoring with Large Language Models Through Prompting

  2025-04-20 · 3 citations

  preprintOpen accessSenior author

  Automated Essay Scoring (AES) offers a scalable solution to the time-intensive and inconsistent nature of human scoring. While traditional AES systems require large sets of prompt-specific scored essays, large language models (LLMs) provide a powerful, adaptable alternative, capable of evaluating essays holistically without an extensive amount of pre-scored essays. However, most research on LLM-based AES focuses on resource-intensive optimization methods that are impractical for educators. In this study, we examine prompting – the most practical and accessible way for teachers to interact with LLMs – and its impact on holistic essay scoring. Using argumentative essays from secondary school students, we evaluate the effectiveness of incorporating grading rubrics, source materials, and anchor papers into prompts. Our results show that providing anchor papers significantly improves LLM-human agreement, bringing it closer to human-human scoring reliability. Moreover, while GPT-4o outperforms other models, GPT-4o mini achieves comparable results at a substantially lower cost. These findings highlight the potential of structured prompting strategies in enhancing the accuracy and accessibility of LLM-based AES in education.

  PublisherOA PDFDOI

• Incorporating generative AI into a writing-intensive undergraduate course without off-loading learning

  Discover Computing · 2025-05-08 · 7 citations

  articleOpen accessSenior author

  Abstract As generative AI becomes ubiquitous, writers must decide if, when, and how to incorporate generative AI into their writing process. Educators must sort through their role in preparing students to make these decisions in a quickly evolving technological landscape. We created an AI-enabled writing tool that provides scaffolded use of a large language model as part of a research study on integrating generative AI into an upper division STEM writing-intensive course. Drawing on decades of research on integrating digital tools into instruction and writing research, we discuss the framework that drove our initial design considerations and instructional resources. We then share our findings from a year of design-based implementation research during the 2023–2024 academic year. Our original instruction framework identified the need for students to understand, access, prompt, corroborate, and incorporate the generative AI use effectively. In this paper, we explain the need for students to think first, before using AI, move through good enough prompting to agentic iterative prompting, and reflect on their use at the end. We also provide emerging best practices for instructors, beginning with identifying learning objectives, determining the appropriate AI role, revising the content, reflecting on the revised curriculum, and reintroducing learning as needed. We end with an indication of our future directions.

  PublisherOA PDFDOI

• “Carlitos the Curious Caterpillar”: Exploring Teacher-AI Co-Creation of Culturally Responsive Educational Materials for Young Learners

  2025-06-23 · 4 citations

  articleOpen accessSenior author
  PublisherDOI

• Computer Science Curriculum for Culturally and Linguistically Diverse Students

  2025-01-01

  otherSenior author
  PublisherDOI

Recent grants

• Collaborative Network of Grades 3-5 Educators for Computational Thinking for English Learners

  NSF · $1.1M · 2019–2023

• Developing Conversational Videos to Support Children's STEM Learning and Engagement

  NSF · $3.0M · 2021–2027

• Using Conversational Agents to Foster Preschool Children's Science Learning and Engagement from Interactive Science Videos

  NSF · $300k · 2019–2022

• Investigating Virtual Learning Environments

  NSF · $2.5M · 2015–2021

Frequent coauthors

• Sharin Jacob

  Digital Promise

  45 shared

• Tamara Tate

  36 shared

• Ying Xu

  23 shared

• Binbin Zheng

  Uniformed Services University of the Health Sciences

  21 shared

• Christian Fischer

  University of Tübingen

  17 shared

• Penelope Collins

  Changwon National University

  16 shared

• Santiago Ojeda-Ramirez

  University of California, Irvine

  16 shared

• Fernando Rodriguez

  University of California, Irvine

  14 shared

Education

• Ph.D., Second Language Acquisition

  University of Hawaii at Manoa

  1997

• M.A., English: Teaching English as a Second Language

  San Francisco State University

  1991

• B.A., Psychology

  University of California, Santa Cruz

  1975

Awards & honors

• Member of the National Academy of Education