Institute for Advanced Learning Technologies

University of Florida’s AI in Education Workshop

April 6, 3-6:30 p.m. ET


Driven by advances in technologies and computer sciences, artificial intelligence has drastically improved over the past decades. We are now at a tipping-point where AI can be used to help solve a variety of challenging educational issues, across multiple fields of study.

UF’s AI in Education workshop brings together 18 top researchers in the fields of Computer Science/AI, Learning Sciences and Instructional Design, and Computational Psychometrics to discuss the latest developments in the field of artificial intelligence.

Specifically, we invited the first three NSF-funded AI Research Institutes to discuss their foci and accomplishments over the past three years. The AI Institute for Student-AI Teaming (iSAT), the Engage AI Institute, and the AI Institute for Adult Learning and Online Education (AI-ALOE), each received approximately $20M to design, develop, and evaluate AI tools that can improve learning in various levels of education.

Additionally, we invited three well-known learning companies that use AI in their products: Duolingo, Khan Academy, and ASSISTments. Finally, we invited 10 top scientists whose work has been nationally and internationally recognized to discuss important topics around AI-in-Education. See the full list of presenters and other contributors below.

Workshop Structure

This 3.5 hour workshop will include:

  • Presentations from AI Institutes and Learning Companies
  • Important topics in AI-in-Education Panel Discussions (Breakout Rooms)
  • Whole Group Discussion and Final Remarks


This workshop will have three main outcomes: (1) all the presentations and discussions will be recorded and will be freely available for the public on this website after the workshop; (2) we will put together a news reports from the workshop with the main takeaways from the workshop and put on this website; (3) we will write a paper that (a) defines AI-in-Education based on the literature and the discussions throughout the workshop, and how is it distinctive from other overlapping fields (e.g., learning Analytics, EDM, learning engineering), (b) provides a background of the field and related work that has been done, (c) provides a brief description of the six examples presented during the workshop,(d) lists recommendations/guidelines about research and practice in AI-in-Education for young researchers or the senior researchers who might want to apply for an AI-institution grant; and (e) discusses the future of AI in education together with the challenges and concerns we may face. This paper will be sent for publication in a scientific journal and the presenters and the invited contributors will be included as the co-authors.

Presenters and Contributors

Seyedahmad Rahimi

Dr. Seyedahmad Rahimi

Workshop Director

  • Assistant Professor of Educational Technology, Institute for Advanced Learning Technologies at the University of Florida College of Education
  • Director, Game-based Assessment & Measurement in Education Lab
  • Director, AI in Education Workshop

More Information

Seyedahmad Rahimi, Ph.D., is an Assistant Professor of Educational Technology in the School of Teaching and Learning at the University of Florida. He is the director of Game-based Assessment & Measurement (GAME) Lab. Dr. Rahimi’s research focuses on assessing and fostering students’ 21st-century skills (e.g., creativity) and STEM-related knowledge acquisition (e.g., physics understanding). Toward that end, Dr. Rahimi designs, develops, and evaluates AI-powered, immersive learning environments (e.g., educational games) equipped with Stealth Assessment and Educational Data Mining, Learning Analytics, and Natural language Processing models. These learning environments can diagnostically assess students’ various competency levels, predict different outcomes, and act accordingly in real-time (e.g., adapt the game challenges to students’ level of competency or support students’ learning by triggering the appropriate learning supports). Dr. Rahimi is also actively researching various aspects of educational games (e.g., game mechanics, game difficulty, cognitive and affective supports, dashboard design, and incentive systems) and how they affect students’ motivation, performance, and learning.


Chris Dede

Dr. Chris Dede

Associate Director for Research
NSF AI Institute for Adult Learning and Online Education

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Chris Dede is a Senior Research Fellow at the Harvard Graduate School of Education and was for 22 years its Timothy E. Wirth Professor in Learning Technologies.  His fields of scholarship include emerging technologies, policy, and leadership.  From 2001-2004, he was Chair of the HGSE department of Teaching and Learning.  In 2007, he was honored by Harvard University as an outstanding teacher, and in 2011 he was named a Fellow of the American Educational Research Association.  In 2020 Chris co-founded the Silver Lining for Learning initiative ( He is currently a Member of the OECD 2030 Scientific Committee and an Advisor to the Alliance for the Future of Digital Learning, sponsored by the Mohammed bin Rashid Global Initiative (MBRGI). Also, Chris is a Co-Principal Investigator and Associate Director for Research of the NSF-funded National Artificial Intelligence Institute in Adult Learning and Online Education. His most recent co-edited books include: Teacher Learning in the Digital Age: Online Professional Development in STEM Education; Virtual, Augmented, and Mixed Realities in Education; Learning engineering for online education: Theoretical contexts and design-based examples; and The 60-Year Curriculum: New Models for Lifelong Learning in the Digital Economy.

James Lester

Dr. James Lester

NSF AI Institute for Engaged Learning

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James C. Lester is Distinguished University Professor of Computer Science and Director of the Center for Educational Informatics at North Carolina State University. He is the Director of the National Science Foundation AI Institute for Engaged Learning. His research centers on transforming education with artificial intelligence. His current work ranges from AI-driven narrative-centered learning environments and virtual agents for learning to multimodal learning analytics and sketch-based learning environments. He is the recipient of a National Science Foundation CAREER Award, numerous Best Paper Awards, and the International Federation for Autonomous Agents and Multiagent Systems Influential Paper Award. He has served as Editor-in-Chief of the International Journal of Artificial Intelligence in Education. He is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI).
Sidney D'Mello

Dr. Sidney D’Mello

Principal Investigator
NSF AI Institute for Student-AI Teaming

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Sidney D’Mello (PhD in Computer Science) is a Professor in the Institute of Cognitive Science and Department of Computer Science at the University of Colorado Boulder. He is interested in the dynamic interplay between cognition and emotion while individuals and groups engage in complex real-world activities. He applies insights gleaned from this basic research program to develop intelligent technologies that help people achieve to their fullest potential by coordinating what they think and feel with what they know and do. D’Mello has co-edited seven books and has published more than 300 journal papers, book chapters, and conference proceedings. His research has received 17 awards at international conferences and has been funded by numerous grants. D’Mello serves(d) as Associate Editor and on the Editorial Boards of 11 journals. He leads the NSF National Institute for Student-Agent Teaming (2020-2025), which aims to develop AI technologies to facilitate rich socio-collaborative learning experiences for all students. [CV] [Website] [Google Scholar] [Contact]

Peter Foltz

Dr. Peter Foltz

Executive Director
NSF AI Institute for Student-AI Teaming

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Dr. Peter Foltz is Research Professor at the University of Colorado’s Institute of Cognitive Science and Executive Director of the National Science Foundation AI Institute for Student-AI Teaming. His work covers machine learning and natural language processing for educational and clinical assessments, large-scale data analytics, cognitive skills in reading and writing, team collaboration, and 21st Century skills learning, Much of his work has focused on NLP techniques for automatically analyzing the meaning of language through writing and speaking. He was a Founder and Chief Scientist at Knowledge Analysis Technologies, which was acquired by Pearson Education and then served as Vice President for Research at Pearson. He has served as the content lead for the framework development for Organisation of Economic Cooperation and Development’s (OECD) Programme for International Student Assessment (PISA) assessments, including the 2018 Reading Literacy assessment, the 2015 assessment of Collaborative Problem Solving, and a new assessment of reading literacy for developing countries.

Alina A von Davier

Dr. Alina A von Davier

Chief of Assessment

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Dr. Alina A von Davier is a psychometrician and researcher in computational psychometrics, machine learning, and education. Von Davier is a researcher, innovator, and an executive leader with over 20 years of experience in EdTech and in the assessment industry. She is the Chief of Assessment at Duolingo, where she leads the Duolingo English Test research and development area. She is also the Founder and CEO of EdAstra Tech, a service-oriented EdTech company. In 2022, she joined the University of Oxford as an Honorary Research Fellow, and Carnegie Mellon University as a Senior Research Fellow.

Kristen DiCerbo

Dr. Kristen DiCerbo

Chief Learning Officer
Khan Academy

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Dr. Kristen DiCerbo is the Chief Learning Officer at Khan Academy, a nonprofit dedicated to providing a free world class education to anyone, anywhere. She leads the content, product management, and customer support teams and is responsible for developing and implementing a research-based teaching and learning strategy for Khan Academy’s offerings in order to improve student and teacher engagement and outcomes. Dr. DiCerbo’s work has focused on embedding what we know from education research about how people learn into digital learning experiences. Prior to her role at Khan Academy, she was Vice-President of Learning Research and Design at Pearson, served as a research scientist supporting teaching and learning in the Cisco Networking Academies, and worked as a school psychologist in an Arizona school district. Kristen received her Bachelor’s Degree from Hamilton College and Master’s Degree and Ph.D. in Educational Psychology at Arizona State University.
Neil T. Heffernan

Dr. Neil T. Heffernan

William Smith Dean’s Professor of Computer Science
Worcester Polytechnic Institute

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Dr. Neil Heffernan is William Smith Dean’s Professor of Computer Science and Director of the Learning Sciences & Technology program at Worcester Polytechnic Institute. He co-founded ASSISTments, a web-based learning platform, which he developed not only to help teachers be more effective in the classroom, but also so that he could use the platform to conduct studies to improve the quality of education. Professor Heffernan is passionate about educational data mining and enjoys supervising WPI students, helping them create ASSISTments content and features. Several student projects have resulted in peer-reviewed publications that compare different ways to optimize student learning. Professor Heffernan’s goal is to give ASSISTments to millions across the U.S. and internationally as a free service. In October 2016 Dr. Heffernan was asked to present at the White House on the reproducibility crisis in educational research and the need for pre-registration and open-data. In December 2016, the Heffernans presented at the White House for a second time on the SRI evaluation that found ASSISTments increased student learning by seventy-five percent. Heffernan has received national press from U.S. News, Scientific American, The New York Times, The Boston Globe, and NPR. Dr. Heffernan has written 100+ papers on learning analytics and over two dozen papers on the results of randomized controlled trials. Dr. Heffernan continues to work on machine-learning methods as it relates to improving student learning. WPI and The ASSISTments Foundation collaborate on many federally and philanthropically funded projects.


Valerie Shute

Dr. Valerie Shute

Professor Emerita
Florida State University

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Valerie Shute is a Professor Emerita in the Department of Educational Psychology and Learning Systems at Florida State University. She’s been involved with basic and applied research related to measurement, assessment, cognitive diagnosis, and learning from advanced instructional systems. Her general research interests hover around the design, development, and evaluation of learning–particularly related to 21st century competencies. Her specific work involves using games with stealth assessment to support learning—of cognitive and noncognitive knowledge, skills, and dispositions. Her research has resulted in numerous grants, journal articles, books, chapters in books (with 21,000 citations according to Google Scholar), as well as a patent (U.S. Patent #7,828,552: Method and System for Designing Adaptive, Diagnostic Assessments, 2010).

Danielle S. McNamara

Dr. Danielle S. McNamara

Professor, Director of the Science of Learning and Educational Technology (SoLET) Lab, and Executive Director of the Learning Engineering Institute
Arizona State University

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Executive Director, Learning Engineering Institute, Arizona State University
Director, Science of Learning and Educational Technology (SoLET) Lab, Arizona State
Professor, Department of Psychology, Arizona State University
Dr. McNamara develops educational technologies and conducts research to better understand cognitive processes of comprehension, learning, comprehension strategies, text coherence, and individual differences. Over the years, Dr. McNamara and her team have developed a number of educational technologies (e.g., iSTART, iSTART-ME, Coh-Metrix, and Writing-Pal). With decades of experience as a senior researcher in cognitive psychology, Dr. McNamara has solidified herself as one of the world’s premier researchers in her field. She has published hundreds of works including journal articles, books, book chapters, and conference proceedings. She has also been invited to deliver keynote addresses at annual conferences for Educational Data Mining, Learning Analytics and Knowledge, and the European Conference on Technology Enhanced Learning, to name a few. As such, Dr. McNamara was recently named the Founding and Executive Director of the Learning Engineering Institute at Arizona State University. In her research, Dr. McNamara is particularly interested in how the effects of educational technologies interact with individual differences and can be optimized for individual Learners.
Ryan Baker

Dr. Ryan Baker

Associate Professor and Director of the Penn Center for Learning Analytics
University of Pennsylvania

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Ryan Baker is Associate Professor at the University of Pennsylvania, and Director of the Penn Center for Learning Analytics. His lab conducts research on engagement and robust learning within online and blended learning, seeking to find actionable indicators that can be used today but which predict future student outcomes. Baker has developed models that can automatically detect student engagement in over a dozen online learning environments, and has led the development of an observational protocol and app for field observation of student engagement that has been used by over 150 researchers in 7 countries. Predictive analytics models he helped develop have been used to benefit over a million students, over a hundred thousand people have taken MOOCs he ran, and he has coordinated longitudinal studies that spanned over a decade. He was the founding president of the International Educational Data Mining
Society, is currently serving as Editor of the journal Computer-Based Learning in Context, is Associate Editor of the Journal of Educational Data Mining, was the first technical director of the Pittsburgh Science of Learning Center DataShop, and currently serves as Co-Director of the MOOC Replication Framework (MORF). Baker has co-authored published papers with over 400 colleagues.
George Siemens

Dr. George Siemens

Professor and Director of the Center for Change and Complexity in Learning (C3L)
University of South Australia

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Dr. George Siemens is an internationally renowned author, researcher, and theorist in the field of learning, knowledge management, and technology. Dr. Siemens is known for his theory of Connectivism. Connectivism is a theoretical framework for understanding learning in the digital age. Connectivism has been one of the most important theories taught and discussed, especially in comparison to Behaviorism and Constructivism in education. In addition, Siemens was a pioneer in the early development of massive online open courses (MOOCs). The concept of MOOCS developed by Siemens, and the evolving MOOCS platforms by industries such as Cousera, Edx, or Udacity have since exerted a significant influence on adult learning. In recent years, Siemens has led several other initiatives focusing on learning analytics (e.g., SoLAR and artificial intelligence in education (e.g., the Global Research Alliance for AI in Learning and Education, Dr. Siemens obtained his PhD from University of Aberdeen in Scotland. In addition, he has received honorary doctorates from the Universidad de San Martin de Porres in Peru and the University of the Fraser Valley in Canada. Siemens is a professor at University of South Australia and professor of practice at the University of Texas Arlington

Diego Zapata-Rivera

Dr. Diego Zapata-Rivera

Distinguished Presidential Appointee and Director
Learning and Assessment Foundations and Innovations (LAFI) Research Center / Educational Testing Service (ETS)

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Dr. Diego Zapata-Rivera is Distinguished Presidential Appointee and director of the Learning and Assessment Foundations and Innovations (LAFI) Research Center at Educational Testing Service in Princeton, NJ. He earned a Ph.D. in computer science (with a focus on artificial intelligence in education) from the University of Saskatchewan in 2003. His research at ETS has focused on the areas of innovations in score reporting and technology-enhanced assessment including work on adaptive learning and assessment environments, conversation-based assessment, caring assessment, and game-based assessment. His research interests also include Bayesian student modeling, open student models, conversation-based tasks, virtual environments, authoring tools and program evaluation. Dr. Zapata-Rivera has produced over 140 publications including edited volumes, journal articles, book chapters, and technical papers. He has served as a reviewer for several international conferences and journals. He has been a committee member and organizer of international conferences and workshops in his research areas. Dr. Zapata-Rivera was elected as a member of the International AI in Education Society Executive Committee (2022-2027). He is a member of the Editorial Board of User Modeling and User-Adapted Interaction, an Associate Editor for AI for Human Learning and Behavior Change, and a former Associate Editor of the IEEE Transactions on Learning Technologies Journal. Dr. Zapata-Rivera has been invited to contribute his expertise to projects sponsored by the National Research Council, the National Science Foundation, NASA and the US Army Research Laboratory.
Kristy Boyer

Dr. Kristy Boyer

University of Florida

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Kristy Elizabeth Boyer is a Professor in the Department of Computer & Information Science & Engineering and the Department of Engineering Education at the University of Florida. Her research focuses on how natural language dialogue and intelligent systems can support human learning across educational contexts including within and outside the classroom. Her research group builds computational models of the processes and phenomena during dialogue and learning, and these models drive the adaptivity of intelligent systems. The computational models in turn shed light on effective strategies for supporting human learning. Her group develops systems that support individual and collaborative learning, including tutorial dialogue systems, intelligent tutoring systems, and game-based learning environments. Boyer holds a Ph.D. in Computer Science from North Carolina State University, an M.S. in Applied Statistics from the Georgia Institute of Technology, and a B.S. in Mathematics and Computer Science from Valdosta State University. She has been recognized with an NSF CAREER award and an NSF Graduate Research Fellowship.

Victor Lee

Dr. Victor Lee

Associate Professor
Stanford Graduate School of Education

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Victor R. Lee is Associate Professor of Learning Sciences and Technology Design at the Stanford Graduate School of Education. His scholarship focuses on the possibilities and pathways for students to engage in future-facing STEM education focusing on tools and competencies that are increasingly necessary for participation in a digitally-infused world. This currently includes empirical research on K-12 data science education, AI literacy, and computing (including maker education and elementary grades computer science) and collaborative educational design research with schools and district partners. Lee is a past president and elected fellow of the International Society of the Learning Sciences and has previously been recognized with the NSF CAREER Award, a National Academy of Education/Spencer Foundation Postdoctoral Fellowship, and the Jan Hawkins Award from the American Educational Research Association. He also currently serves as the Faculty Lead of AI + Education for the Stanford Accelerator for Learning, a university-wide innovation initiative.
Lin Lin Lipsmeyer

Dr. Lin Lin Lipsmeyer

Professor and Chair of Teaching and Learning
SMU Simmons School of Education & Human Development

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Dr. Lin Lin Lipsmeyer is Professor and Department Chair of Teaching and Learning in the SMU’s Simmons School of Education & Human Development. Prior to coming to SMU, Lin was Professor of Learning Technologies and Director of the Texas Center for Educational Technology at the University of North Texas. Lin received her doctorate in Instructional Technology and Media from Teachers College Columbia University. Dr. Lin Lipsmeyer has conducted interdisciplinary research in mind, brain, and learning technology. Her research has resulted in over 110 scholarly publications including journal articles, books, and book chapters. In addition, she has been a PI, Co-PI, or researcher on multiple NSF and foundation grants bridging learning sciences, artificial intelligence, and STEAM learning.  Lin also serves as the Development Editor-in-Chief of the Educational Technology Research and Development (ETR&D,, one of the top journals in education and educational research.

Richard Golden

Dr. Richard Golden

University of Texas at Dallas

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Richard Golden is Professor of Cognitive Science and Participating Faculty Member in Electrical Engineering at the University of Texas at Dallas. His current research interests are concerned with the integration of psychometric methods and AI technologies for the purpose of supporting an evidence-based validity centered approach to personalized student assessment and pedagogy. This research direction builds upon his prior work in the areas of AI, statistical machine learning, detection of model misspecification, model selection, and misspecification-robust inference involving latent variables. Many of these prior research threads are summarized in his recently published book Statistical Machine Learning: A unified framework.
Russell Almond

Dr. Russell Almond

Associate Professor
Florida State University

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Russell Almond is an associate professor in the Department of Educational Psychology and Learning Systems in the College of Education at Florida State University His professional biography and curriculum viate (XHTML, PDF) are available on-line, as is a list of publications (both in html and as a BibTeX database). Russell has been a frequent contributer to the Uncertainty In Artificial Intelligence conference, and was a past chair of the Bayesian Applications workshop. Before coming to Florida State, Russell worked at Educational Testing Service and StatSci (later MathSoft and now Insightful) where he designed the Graphical-Belief software for building and evaluating graphical belief functions. Although this work was never formally released as a product, it has several interesting features which can be found in the link above. Russell has recently released an add-on export filter to JabRef for exporting bibtex databases as RTF files in APA format, the APAish package.

Rene Kizilcec

Dr. Rene Kizilcec

Assistant Professor of Information Science
Cornell University

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Dr. Rene Kizilcec is an Assistant Professor of Information Science at Cornell University. His research is on the use and impact of technology in formal and informal learning environments and scalable interventions to broaden participation and reduce achievement gaps. His recent work is on academic progress in higher education and algorithmic transparency and fairness in predictive analytics. Kizilcec received a B.A. in Philosophy and Economics from University College London, and a M.Sc. in Statistics and Ph.D. in Communication from Stanford.

Faculty at the Institute for Advanced Learning Technologies

At IALT, researchers from a variety of backgrounds bring a wide range of knowledge, needed to address tomorrow’s challenges. To cultivate wide-reaching implications for every member of our society, UF researchers are working at the intersection of data mining, computational psychometrics, machine learning and artificial intelligence to dramatically improve learning outcomes and to transform education, in both concept and practice.

Learn more about IALT faculty here:

Workshop Agenda


2:50 – 3:00 Zoom waiting room open

3:00 – 3:05 Welcome by the workshop’s director Dr. Rahimi

3:05 – 3:10 UF’s provost short presentation about UF’s AI initiative

Presentations (15 minutes each + 5 minutes Q&A)

3:10 – 3: 30 iSAT AI institute

3:30 – 3:50 Duolingo

3:50 – 4:10 Engage AI institute

4:10 – 4:20 10-minute break

4:20 – 4:40 Khan Academy4:40 – 5:00 AI Institute for Adult Learning and Online Education 

5:00 – 5:20 ASSISTments 

5:20 –5:25 5-minute break and transition to panel sessions

Panel discussions

5:25 – 6:15 Small-group panels 

6:15 – 6:30 Large-group debriefings and final remarks

Event Videos

You can view videos below of the presentation portion of this event and the following panel sessions. 

Institute for Advanced Learning Technologies | University of Florida