Janice D. Gobert

With six patents for her work, Dr. Gobert develops educational technology including AI for STEM. Her system, Inq-ITS, leverages AI, including machine-learning, knowledge-engineering, and natural language processing to support students’ learning and teachers’ instruction and assessment.  The cutting edge, AI-based assessments in Inq-ITS both replace traditional tests such as multiple choice items and supplement typically-used written lab reports as means of assessment because neither of these are fine-grained enough to evaluate how well students can do authentic science tasks. In brief, the technology tracks and uses students’ interaction data while they are conducting an experiment in Inq-ITS, a virtual science environment, and generates fine-grained performance assessment data in real time. This computational approach is particularly important for assessing students who have difficulties describing what they know in words, such as those who are writing in their second language or students who are simply parroting what they have read or heard, or have used ChatGPT to generate their lab report (in which case, little to no learning has occurred)! The algorithms provide real time reports, alerts, and instructional tips to the teacher as to who is struggling, precisely how they are struggling, and how to help them; the algorithms also trigger an AI tutor to provide personalized help in real-time to students, when it is optimal for learning so as to maximize understanding for all students who are at risk for falling behind.

Dr. Gobert studied psychology during her undergraduate years with a deep focus on experimental and cognitive psychology. Propelled by her interest in learning how people understand visually-rich diagrams in the sciences and engineering, she went on to pursue her master’s (at McGill University) and Doctorate (at University of Toronto) in Cognitive Science. She believes that educational technology undergirded by AI has the potential to be a great equalizer for students and aims to further her goal of deeply investing in future citizens. She currently teaches Psychology of Learning, How People Learn, and Inquiry II and constantly looks to offer her students the cutting edge in technology to enhance their research and future career goals. Dr. Gobert considers teachers’ love and implementation of her technology to be one of her most significant professional achievements. As of September 2025, Inq-ITS has been used in 50 states and 104 countries.

• Expertise & Research Interest

  Science Inquiry

  Intelligent Tutoring Systems for Science

  Performance Assessment for Science

• Patents

  Gobert, J. & Toto, E. (September, 2019). An Instruction System with Eyetracking-based Adaptive Scaffolding. Canada Patent no. 2864166 (issued).

  Gobert, J., Sao Pedro, M., Betts, C., & Baker, R.S. (January, 2019). Inquiry skills tutoring system (child patent for additional claims to Inq-ITS). US Patent no. 10,186,168 (issued).

  Gobert, J., Sao Pedro, M., Betts, C., & Baker, R.S. (February, 2017). Inquiry skills tutoring system (child patent for alerting system). US Patent no. 9,564,057 (issued).

  Gobert, J.D., Baker, R.S., & Sao Pedro, M.A. (June, 2016). Inquiry skills tutoring system. US Patent no. 9,373,082 (issued).

  Gobert, J. & Toto, E. (February 22, 2013). An Instruction System with Eyetracking-based Adaptive Scaffolding. US Patent no. 9,230,221 (issued).

  Gobert, J. & Toto, E. (February 22, 2013). An Instruction System with Eyetracking-based Adaptive Scaffolding (child patent for additional use cases). US Patent no. 9,317,115 (issued).

• Recent & Selected Publications

  Gobert, J., Dickler, R., Adair, A. (2024). Using an AI-based dashboard to help teachers support students’ learning progressions for science practices. In H. Jin, D. Yan, and J. Krajcik (Eds.), Handbook of Research on Science Learning Progressions. Taylor & Francis Group. https://doi.org/10.4324/9781003170785

  Gobert, J.D., Li, H., Dickler, R., & Lott, C. (2024). Can AI-based scaffolding promote students’ robust learning of authentic science practices? In X. Zhai & J. Krajcik (Eds.), Uses of Artificial Intelligence in STEM Education. Oxford, UK: Oxford University Press. doi.org/10.1093/oso/9780198882077.003.0012

  Staneva, M., Baret, A., Gobert, J., et al. (2023). Assessing AI Capabilities with Education Tests. In AI and the Future of Skills: Methods and evaluation AI capabilities, Vol. 2. OECD, OECD Publishing, Paris. doi.org/10.1787/a9fe53cb-en

  Gobert, J. & Inq-ITS Research and Development Team. (2023). An AI-Based Platform for Real Time Assessment, Scaffolding, and Alerting on Students’ Science Practices. In H., Jiao, & R. W. Lissitz (Eds.), Machine Learning, Natural language Processing, and Psychometrics. Information Age Publishing.

  Gobert, J. & Inq-ITS Research and Development Team. (2023). An AI-Based Platform for Real Time As

  Gobert, J. D., Sao Pedro, M.A., Betts, C.G. (2023). An AI-Based Teacher Dashboard to Support Students’ Inquiry: Design Principles, Features, and Technological Specifications. In N. Lederman, D. Zeidler, & J. Lederman (Eds.), Handbook of Research on Science Education, (Vol. 3, pp. 1011-1044). Routledge. https://doi.org/10.4324/9780367855758

  Gobert, J.D., Sao Pedro, M.A., Li, H., & Lott, C. (2023). Intelligent Tutoring systems: a history and an example of an ITS for science. In R.Tierney, F. Rizvi, K. Ercikan, & G.  Smith, (Eds.), International Encyclopaedia of Education (Vol. 4, pp. 460-470), Elsevier. Link to PDF

  Dickler, R., Gobert, J., & Sao Pedro, M. (2021). Using Innovative Methods to Explore the Potential of an Alerting Dashboard for Science Inquiry. Journal of Learning Analytics, 8(2), 105-122. https://doi.org/10.18608/jla.2021.7153

  Nicolay, B., Krieger, F., Stadler, M., Gobert, J., & Grieff, S. (2021). Lost in transition – Learning analytics on the transfer from knowledge acquisition to knowledge application in complex problem solving. Computers in Human Behavior, 115. https://doi.org/10.1016/j.chb.2020.106594

  Mislevy, R., Yan, D., Gobert, J., & Sao Pedro, M. (2020). Automated Scoring with Intelligent Tutoring Systems. In Yan, D., Rupp, A. & Foltz, P. (Eds.), Handbook of Automated Scoring: Theory into Practice. Chapman and Hall, London, UK. https://doi.org/10.1201/9781351264808

  Luan, Hui, Geczy Peter, Lai Hollis, Gobert, Janice, Yang Stephen J. H., Ogata Hiroaki, Baltes, Jacky, Guerra Rodrigo, Li Ping, Tsai Chin-Chung. (2020). Challenges and Future Directions of Big Data and Artificial Intelligence in Education. Frontiers in Psychology, 11, 1-11. https://doi.org/10.3389/fpsyg.2020.580820

  Gobert, J., Moussavi, R., Li, H., Sao Pedro, M., & Dickler, R. (2018). Real-time scaffolding of students’ online data interpretation during inquiry with inq-its using educational data mining. Invited book chapter in Abul K.M. Azad, Michael Auer, Arthur Edwards, and Ton de Jong (Eds), Cyber-Physical Laboratories in Engineering and Science Education. Springer. doi: 0.1007/978-3-319-76935-6_8

  Li, H., Graesser, A.C., & Gobert, J. (2017). Where is embodiment hidden in the intelligent tutoring system? Journal of South China Normal University, 3, 79-91. Link to PDF

  Gobert, J.D., & Sao Pedro, M.A. (2017). Digital Assessment Environments for Scientific Inquiry Practices. In Rupp, A.A. & Leighton, J.P (Eds.) The Wiley Handbook of Cognition and Assessment: Frameworks, Methodologies, and
  Applications. West Sussex, UK. 508-534. Link to PDF

  Gobert, J.D., Kim, Y.J, Sao Pedro, M.A., Kennedy, M., & Betts, C.G. (2015). Using educational data mining to assess students’ skills at designing and conducting experiments within a complex systems microworld. Thinking Skills and Creativity, 18, 81-90. doi: 10.1016/j.tsc.2015.04.008

  Kim, B., Pathak, S., Jacobson, M., Zhang, B., & Gobert, J.D. (2015). Cycles of Exploration, Reflection, and Consolidation in Model-Based Learning of Genetics. Journal of Science Education and Technology. Journal of Science Education and Technology, 24(6), 789-802. doi: 10.1007/s10956-015-9564-6

  Gobert, J.D., Baker, R., & Wixon, M. (2015). Operationalizing and Detecting Disengagement Within On-Line Science Microworlds. Educational Psychologist, 50(1), 43-57. https://doi.org/10.1080/00461520.2014.999919

  ​​Gobert, J., Sao Pedro, M., Raziuddin, J., and Baker, R. S., (2013). From log files to assessment metrics for science inquiry using educational data mining. Journal of the Learning Sciences, 22(4), 521-563. doi: 10.1080/10508406.2013.837391

  Hershkovitz, A., Baker, R.S.J.d., Gobert, J., Wixon, M., Sao Pedro, M. (2013). Discovery with models: A case study on carelessness in computer-based science inquiry. American Behavioral Scientist, 57 (10), 1479-1498. doi:10.1177/0002764213479365

  Gobert, J., Wild, S., & Rossi, L. (2012). Examining Geoscience Learning with Google Earth: Testing the Effects of Prior Coursework and Gender. Special issue on Google Earth and Virtual Visualizations in Geoscience Education and Research. Geological Society of America, Special Paper 492, 453-468. doi:10.1130/2012.2492(35)

  Gobert, J., Sao Pedro, M., Baker, R.S., Toto, E., & Montalvo, O. (2012). Leveraging educational data mining for real time performance assessment of scientific inquiry skills within microworlds, Journal of Educational Data Mining, Article 15, Volume 4, 153-185. https://doi.org/10.5281/zenodo.3554645

  Timms, Michael, Clements, Douglas H., Gobert, Janice, Ketelhut, Diane J., Lester, James, Reese, Debbie D., and Wiebe, Eric. (2012). New measurement paradigms. Report to the National Science Foundation. http://research.acer.edu.au/ar_misc/9. 

  Gobert, J., O’Dwyer, L., Horwitz, P., Buckley, B., Levy, S.T. & Wilensky, U. (2011). Examining the relationship between students’ epistemologies of models and conceptual learning in three science domains: Biology, Physics, & Chemistry. International Journal of Science Education, 33(5), 653-684. doi:10.1080/09500691003720671

  Gobert, J.D, Pallant, A.R., & Daniels, J.T.M. (2010). Unpacking inquiry skills from content knowledge in Geoscience: A research perspective with implications for assessment design. International Journal of Learning Technologies, 5(3), 310-334. doi:10.1504/IJLT.2010.037309

  Cobern, W., Schuster, D., Adams, B., Undreiu, A., Applegate, B., Skjold, B., Loving, C. & Gobert, J. (2010). Experimental Comparison of Inquiry and Direct Instruction in Science. Research in Science and Technological Education, 28(1), 81-96. doi:10.1080/02635140903513599

  Buckley, B.C., Gobert, J., Horwitz, P. & O’Dwyer, L. (2010). Looking inside the black box: Assessments and decision-making in BioLogica. International Journal of Learning Technologies, 5(2), 166 – 190. doi:10.1504/IJLT.2010.034548

  Horwitz, P., Gobert, J., & Buckley, B., & O’Dwyer, L. (2010). Learning Genetics With Dragons: From Computer-Based Manipulatives to Hypermodels.  In Jacobson, M. J., & Reimann, P. (Eds.). Designs for learning environments of the future: International perspectives from the learning sciences. Springer Publishers, pp. 61-87.  Link to PDF

  Gobert, J. (2005). The effects of different learning tasks on conceptual understanding in science: teasing out representational modality of diagramming versus explaining. Journal of Geoscience Education, 53(4), 444-455. Link to PDF

  Gobert, J. (2005). Leveraging technology and cognitive theory on visualization to promote students’ science learning and literacy. In Visualization in Science Education, J. Gilbert (Ed.), pp. 73-90. Springer-Verlag Publishers, Dordrecht, The Netherlands. ISBN 10-1-4020-3612-4. doi: 10.1007/1-4020-3613-2_6

  Buckley, B.C., Gobert, J.D., Kindfield, A., Horwitz, P., Tinker, R., Gerlits, B., Wilensky, U., Dede, C., & Willett, J. (2004). Model-based Teaching and Learning with BioLogica™: What do they learn? How do they learn? How do we know? Journal of Science Education and Technology. Vol 13(1), 23-41. Link to PDF 

  Gobert, J.D., & Pallant, A., (2004). Fostering students’ epistemologies of models via authentic model-based tasks. Journal of Science Education and Technology. Vol 13(1), 7-22.  doi:10.1023/B:JOST.0000019635.70068.6f

  Gobert, J.D., & R. Tinker (2004). Introduction to the Issue. Journal of Science Education and Technology, Vol 13(1), 1-6.

  Gilbert, J.K., Treagust, D., & Gobert, J. (2003). Science Education: from the past, through the present, to the future. International Journal of Science Education, 25 (6), 643-644. 643-644. doi:10.1080/09500690305019

  Gobert, J. (2000). A typology of models for plate tectonics: Inferential power and barriers to understanding. International Journal of Science Education, 22(9), 937-977. https://doi.org/10.1080/095006900416857

  Gobert, J. & Buckley, B. (2000). Special issue editorial: Introduction to model-based teaching and learning. International Journal of Science Education, 22(9), 891-894. Link to article 

  Gobert, J. (1999). Expertise in the comprehension of architectural plans: Contribution of representation and domain knowledge. In Visual And Spatial Reasoning In Design ’99, John S. Gero and B. Tversky (Eds.), Key Centre of Design Computing and Cognition, University of Sydney, AU. Link to PDF

  Gobert, J. & Clement, J. (1999). Effects of student-generated diagrams versus student-generated summaries on conceptual understanding of causal and dynamic knowledge in plate tectonics. Journal of Research in Science Teaching, 36(1), 39-53. https://doi.org/10.1002/(SICI)1098-2736(199901)36:1<39::AID-TEA4>3.0.CO;2-I

• Honors & Awards

  2024 Gobert, J.D. (June, 2024). Awardee, OISE/University of Toronto Leaders & Legends – Innovation. https://www.oise.utoronto.ca/alumni-friends/awards/leaders- legends

  Gobert, J.D. (2022, August 4). Panelist for the White House Office of Science and Technology Policy and the U.S. Department of Education to advise on the future of Artificial Intelligence in Education.

  Gobert, J.D. (2022). Expert panelist, AI and the Future of Skills, OECD.

  Gobert, J.D. 2021 Nominee, Grawemeyer Award in Education. http://grawemeyer.org/education/ (competition postponed due to COVID-19)

  Honorable Mention, AERA Cognition and Assessment SIG, 2015

  James Chen Best Student Paper Award, Michael Sao Pedro (advisee), 20^th Conference on User Modeling, Adaptation, and Personalization, 2012

  Best Student Paper Award, Michael Sao Pedro (advisee), AERA SIG on Advanced Learning Technologies, 2012

  Best Interactive Event, 10^th International Conference on Intelligent Tutoring Systems, 2010

  Young Researchers’ Award, Michael Sao Pedro (advisee), 10^th International Conference on Intelligent Tutoring Systems, 2010

  Research Development Award, Western Michigan University, 1997

  Dean’s Appreciation Award, Western Michigan University, 1996

• Recent & Selected Grants

  Developing & Testing Real-time Assessment & Scaffolding for Mathematics Use & Modeling During Science Inquiry (R305A210432). Gobert, J. (Principal Investigator) & Sao Pedro, M. (Co- Investigator). Awarded May, 2021 by the U.S. Dept. of Education, $1,905,787.00

  Inq-Blotter: Designing Supports for Teachers’ Real Time Instruction (NSF-IIS- 1902647). Gobert, J. (Principal Investigator), Awarded July 17, 2019 from the National Science Foundation, $299,999.00

  Inq-Blotter – A Real Time Alerting Tool to Transform Teachers’ Assessment of Science Inquiry Practices, (NSF-IIS-1629045). Gobert, J. (Principal Investigator), M. Sao Pedro (Co-Principal Investigator). Awarded August 2016 from the National Science Foundation, $550,000.00

  Testing the Effects of Real-time Scaffolding of Science Inquiry Driven by Automated Performance Assessment, (NSF-DRL-1252477), Gobert, J., Principal Investigator, Awarded September 2013 from the National Science Foundation, $1,499,346.00

  The Development of an Intelligent Pedagogical Agent for Physical Science Inquiry Driven by Educational Data Mining. Gobert, J. & Baker, R. Proposal (R305A120778) Awarded May, 2012 by the U.S. Dept. of Education, $1,499,588.00

  Empirical Research: Emerging Research: Using Automated Detectors to Examine the Relationships Between Learner Attributes and Behaviors During Inquiry in Science (NSF-DRL# 1008649). Janice Gobert, Principal Investigator, Ryan Baker, Co-Principal Investigator. Awarded July 1, 2010 from the National Science Foundation; $986,111.00