About

Carl Wieman holds a joint appointment as Professor of Physics and of the Graduate School of Education at Stanford University. He has done extensive experimental research in atomic and optical physics. His current intellectual focus is on undergraduate physics and science education. He has pioneered the use of experimental techniques to evaluate the effectiveness of various teaching strategies for physics and other sciences. Additionally, he served as Associate Director for Science in the White House Office of Science and Technology Policy. Wieman is also an Emeritus Faculty member, a member of the Academic Council in Physics, and participates in programs related to Learning Sciences and Technology Design, among others. His research interests include Brain and Learning Sciences, Higher Education, Science Education, and Teachers and Teaching.

Research topics

• Computer Science
• Political Science
• Social Science
• Artificial Intelligence
• Mathematics education
• Psychology
• Sociology
• Medicine
• Pedagogy
• Medical education

Selected publications

• Deconstructing Expert Cognitive Skills and Knowledge

  Gastrointestinal Endoscopy Clinics of North America · 2026-03-01

  article1st authorCorresponding
  PublisherDOI

• Teaching Medical Students to Reason like Skilled Physicians

  Gastrointestinal Endoscopy Clinics of North America · 2026-03-01

  article1st authorCorresponding
  PublisherDOI

• A novel assessment of mechanical engineering design skills

  International Journal of Mechanical Engineering Education · 2025-12-01

  articleOpen accessSenior authorCorresponding

  We transformed an introductory mechanical engineering (ME) project course guided by theories of learning and evaluated the impact of this transformation. The course is the first opportunity for students to make design decisions to meet requirements and build something. In this Part I paper we present the development of a new instrument, the “design practices assessment” (DPA), to measure how well the students learned the intended design skills. The assessment instrument is based on the specific design practices and their entailing decisions an engineer makes. It is a valid way to measure the mastery of mechanical engineering design practices and hence can serve as a template for assessment in other engineering design courses. We present the theoretical framework underlying the DPA, then explain the structure and development, followed by the explanation of the scoring system, measures of validity, and results from its use to measure student learning.

  PublisherDOI

• Investigating blended math-science sensemaking with historically marginalized STEM learners

  International Journal of STEM Education · 2025-09-01 · 1 citations

  articleOpen accessSenior author

  Blended mathematical sensemaking in science (“MSS”) involves deep conceptual understanding of quantitative relationships describing scientific phenomena. Previously we developed the cognitive framework describing proficiency in MSS across STEM disciplines, and specifically Physical Science. The framework was validated with undergraduate students using assessment built around PhET sims. Students in the prior study were from a reasonably selective university serving predominantly White student population. In this study we investigate whether the framework can help identify specific patterns of engagement in MSS among students from backgrounds historically marginalized in STEM [i.e., members of Black/African American, Hispanic/Latinx/Indigenous/Native American and People of Color (POC) communities] attending U.S. Minority-Serving Institutions (MSIs). This study provides insights on how to better support these students in building transferable MSS skills. The framework is generally effective in characterizing engagement in MSS by learners in this study. One distinction is that these students relied less than our previous population on quantitative pattern identification as a stage in successfully developing the mathematical relationship describing their observations. Unlike students from non-marginalized backgrounds, students in this study productively leverage lower level MSS to develop the formula for observations without using quantitative pattern identification. In addition, students in this study tend to rely more on PhET sims rather than other data sources (e.g., data tables) to find the correct formula compared to students from non-marginalized backgrounds. The MSS framework can guide the development of instructional and assessment strategies to support students from backgrounds historically marginalized in STEM in building MSS skills. The framework helped identify specific types of MSS that should be supported to facilitate the transition to the highest framework levels among these students, and these types of MSS turned out to be similar for both dominant and diverse student groups. Furthermore, PhET sims provide an effective environment for learning MSS skills, and their capabilities should be leveraged for designing learning experiences in the future. Finally, students at level 1 of the MSS framework should be supported in developing a deeper math understanding and integration of math and science when making sense of phenomena.

  PublisherOA PDFDOI

• Scaffold or Crutch? Examining College Students' Use and Views of Generative AI Tools for STEM Education

  2025-04-22 · 11 citations

  article

  Developing problem-solving competency is central to Science, Technology, Engineering, and Mathematics (STEM) education, yet translating this priority into effective approaches to problem-solving instruction and assessment has been a significant challenge. The recent proliferation of generative artificial intelligence (genAI) tools like ChatGPT in higher education introduces new considerations: how to define problem-solving competency in a genAI era, and how these tools can help or hinder students' development of STEM problem-solving competency. Our research takes steps in examining these considerations by studying how and why college students are currently using genAI tools in their STEM coursework, with a specific focus on how they employ these tools to support their problem-solving. We conducted an online survey of 40 STEM college students from diverse institutions across the US. In addition, we surveyed 28 STEM faculty to understand instructor views on effective and ineffective genAI tool use in STEM courses and their guidance for students. Our findings reveal high adoption rates and diverse applications of genAI tools among STEM students. The most common use cases of genAI tools in STEM coursework include finding explanations, exploring related topics, summarizing readings, and helping with problem-set questions. The primary motivation for using genAI tools in STEM coursework was to save time. Moreover, we found that over half of the student participants reported simply inputting a problem for AI to generate solutions, potentially bypassing their own problem-solving processes. These findings indicate that despite high adoption rates, students' current approaches to utilizing genAI tools often fall short in enhancing their own STEM problem-solving competencies. The study also explored students' and STEM instructors' perceptions of the benefits and risks associated with using genAI tools in STEM education. Our findings provide insights into how to guide students on appropriate genAI use in STEM courses and how to design genAI-based tools to foster students' problem-solving competency.

  PublisherDOI

• GLOBAL CONSENSUS ON TRANSFORMING ENDOSCOPY TRAINING THROUGH EXPERTISE SCIENCE PRINCIPLES: THE BALI ENDOSCOPY SIMULATION TRAINING (BEST) CONSENSUS

  Gastrointestinal Endoscopy · 2025-05-01 · 1 citations

  article
  PublisherDOI

• Scaffold or Crutch? Examining College Students' Use and Views of Generative AI Tools for STEM Education

  arXiv (Cornell University) · 2024-12-03 · 2 citations

  preprintOpen access

  Developing problem-solving competency is central to Science, Technology, Engineering, and Mathematics (STEM) education, yet translating this priority into effective approaches to problem-solving instruction and assessment remain a significant challenge. The recent proliferation of generative artificial intelligence (genAI) tools like ChatGPT in higher education introduces new considerations about how these tools can help or hinder students' development of STEM problem-solving competency. Our research examines these considerations by studying how and why college students use genAI tools in their STEM coursework, focusing on their problem-solving support. We surveyed 40 STEM college students from diverse U.S. institutions and 28 STEM faculty to understand instructor perspectives on effective genAI tool use and guidance in STEM courses. Our findings reveal high adoption rates and diverse applications of genAI tools among STEM students. The most common use cases include finding explanations, exploring related topics, summarizing readings, and helping with problem-set questions. The primary motivation for using genAI tools was to save time. Moreover, over half of student participants reported simply inputting problems for AI to generate solutions, potentially bypassing their own problem-solving processes. These findings indicate that despite high adoption rates, students' current approaches to utilizing genAI tools often fall short in enhancing their own STEM problem-solving competencies. The study also explored students' and STEM instructors' perceptions of the benefits and risks associated with using genAI tools in STEM education. Our findings provide insights into how to guide students on appropriate genAI use in STEM courses and how to design genAI-based tools to foster students' problem-solving competency.

  PublisherOA PDFDOI

• Exploring Differences in Clinical Decisions Between Medical Students and Expert Clinicians

  Advances in Medical Education and Practice · 2024-12-01 · 4 citations

  articleOpen access

  Background: Numerous challenges exist in effectively bridging theory and practice in the teaching and assessment of clinical reasoning, despite an abundance of theoretical models. This study compares clinical reasoning practices and decisions between medical students and expert clinicians using a problem-solving framework from the learning sciences, which identifies clinical reasoning as distinct, observable actions in clinical case solving. We examined students at various training stages against expert clinicians to address the research question: How do expert clinicians and medical students differ in their practices and decisions during the diagnostic process?. Methods: We developed a questionnaire about a pediatric infectious disease case based on the problem-solving framework from the learning sciences to probe clinical reasoning decisions. The questionnaire had four sections: medical history, physical examination, medical tests, and working diagnosis. The questionnaire was administered at Stanford University between January 2019 and June 2023 to collect data from 10 experts and 74 medical students. We recruited participants through maximum variation sampling. We applied deductive content analysis to systematically code responses to identify patterns in the execution of the practices and decisions across the questionnaire. Results: This research introduces a highly detailed, empirically developed framework that holds potential to bridge theory and practice, offering practical insights for medical instructors in teaching clinical reasoning to students across various stages of their training. This framework involves nine practices, with a total of twenty-nine decisions that need to be made when carrying out these practices. Differences between experts and students centered on ten decisions across the practices: Differential diagnosis formulation, Diagnostic plan and execution, Clinical data reassessment, and Clinical solution review. Conclusion: We were able to identify nuanced differences in clinical reasoning between students and expert physicians under one comprehensive problem-solving framework from the learning sciences. Identifying key clinical reasoning practices and decision differences could help develop targeted instructional materials and assessment tools, aiding instructors in fostering clinical reasoning in students.

  PublisherOA PDFDOI

• Characterizing decision-making opportunities in undergraduate physics coursework

  Physical Review Physics Education Research · 2024-07-29 · 6 citations

  articleOpen accessSenior author

  A major goal of physics education is to develop strong problem-solving skills for students. To become expert problem solvers, students must have opportunities to deliberately practice those skills. In this work, we adopt a previously described definition of problem solving that consists of a set of 29 decisions made by expert scientists. We quantified the amount of practice undergraduate physics students get at making each decision by coding the decisions required in assignments from introductory, intermediate, and advanced physics courses at a prestigious university. A research-focused capstone course was the only example that offered substantial practice at a large range of decisions. Problems assigned in the traditional coursework required only a few decisions and routinely reduced potential opportunities for students to make other decisions. In addition, we modified traditional physics coursework to offer more decision-making practice. We observed that this increased the number of decisions students actually made in solving the problems. This work suggests that to better prepare undergraduates for solving problems in the real world, we must offer more opportunities for students to make and act on problem-solving decisions. Published by the American Physical Society 2024

  PublisherOA PDFDOI

• Exploring the learning experiences of neurodivergent college students in <scp>STEM</scp> courses

  Journal of Research in Special Educational Needs · 2024-02-21 · 10 citations

  articleSenior author

  Abstract Neurodivergent students exhibit an inclination towards Science, Technology, Engineering and Mathematics (STEM) fields, yet their learning experiences in STEM courses remain underexamined. Utilizing an online survey of neurodivergent ( n = 60) and neurotypical ( n = 83) US college students, this study identified various factors influencing their self‐perceived learning experiences, including interest in the course content, instruction quality and performance outcomes. Compared to their neurotypical peers, neurodivergent students attributed negative experiences in STEM courses less frequently to performance‐related factors and more often to a mismatch between their interests and the course content. Both groups also articulated a variety of strengths and challenges encountered in their STEM studies. Neurodivergent students were more likely to report having interest and passion for STEM and less likely to report having peer support and effective study skills and habits as their primary strength for studying STEM. Conversely, while neurotypical students cited difficult content as their central challenge, neurodivergent students more commonly faced challenges with focus and attention. Despite the study's limited sample size, it revealed emerging patterns that emphasize the importance of developing inclusive teaching methods and specific support mechanisms to cater to the unique strengths and challenges of neurodivergent students in higher education.

  PublisherDOI

Recent grants

• Physics Education Technology Project

  NSF · $443k · 2005–2009

• Creating a new assessment tool for quantitative critical thinking in introductory lab courses

  NSF · $299k · 2016–2021

Frequent coauthors

• Eric Cornell

  Joint Institute for Laboratory Astrophysics

  317 shared

• C. J. Myatt

  MBio Diagnostics (United States)

  107 shared

• Carol E. Tanner

  106 shared

• M. R. Matthews

  99 shared

• B. P. Masterson

  98 shared

• Nathan R. Newbury

  National Institute of Standards and Technology

  96 shared

• Jacob Roberts

  Colorado State University

  88 shared

• D. Sesko

  National Institute of Standards and Technology

  87 shared

Education

• Ph.D, Physics

  Stanford University

  1977

• Bachelor, Science

  Massachusetts Institute of Technology

  1973

Awards & honors

• $4 million prize for education research (2020)