Tingting Li

Research Interests

 My research explores the underlying complex learning process of building usable knowledge and how student engagement interacts with this process by leveraging state-of-the-art technologies such as artificial intelligence (AI). Anchored in the convergence of learning theories, educational psychology, technology, and science education research, my work stands at the crossroads of deep science learning, student engagement, and AI. My research has three main lines, including: (1) advance usable knowledge building process through designing equity-oriented, coherent learning environments by applying or expanding learning theories; (2) delve into the process of building usable knowledge by examining how various forms of student engagement—personal, situational, and productive disciplinary engagement—interact with and influence this process; and (3) advance the underlying complex learning process of building usable knowledge by leveraging AI.

I design learning environments and assessments that not only support students’ usable knowledge building but also address their psychological well-being (e.g., engagement, social and emotional learning). I dissect complex learning processes through lenses like the social constructivist theory while also underscoring the psychological foundations of learning, particularly personal, situational, and productive disciplinary engagement. Furthermore, by harnessing the power of AI, I have been designing technologically enhanced learning environments that provide timely, structured, and personalized feedback for students. My research methodologies span rigorous qualitative and quantitative techniques, complemented by cutting-edge AI techniques. My research’s distinctiveness lies in its interdisciplinary synthesis of teaching and learning principles, promising to advance the domain by unraveling and supporting the complex learning process of building usable knowledge, keeping both psychological and technological facets in the foreground.

 

Education

2024	Ph.D., Educational Psychology and Educational Technology, Michigan State University
2022	Ph.D., Curriculum and Instruction (in science education), Northeast Normal University & Michigan State University
2016	M.Ed., Curriculum and Instruction in Chemistry, Northeast Normal University
2014	B.S., Chemistry, Daqing Normal University

 

Teaching

MIT 534 Elementary School Science Methods

 

Selected Accomplishments

Peer-Reviewed Journal Articles

13. He, P., Zhang, Y., & Li, T. (Accepted). Diagnosing middle school students’ proficiency in constructing scientific explanations with the integration of chemical reaction and patterns: A cognitive diagnostic modeling approach. International Journal of Science Education.

12. Guo, J., Ma, Y., Li, T., Noetel, M., Liao, K., & Greiff, S. (in press). Harnessing Artificial Intelligence in Generative Content for enhancing motivation in learning. Learning and Individual Differences. https://doi.org/10.1016/j.lindif.2024.102547

11. Li, T., Adah Miller, E., Simani, M. C., & Krajcik, J. (2024). Adapting scientific modeling practice for promoting elementary students’ productive disciplinary engagement. International Journal of Science Education, 1-35.    https://doi.org/10.1080/09500693.2024.2361488

10. Li, T., Chen, I., Miller, E., Miller, C., Schneider, B., & Krajcik, J. (2023). The relationships between elementary students’ knowledge-in-use performance and their science achievement. Journal of Research in Science Teaching. https://doi.org/10.1002/tea.21900 [SSCI, IF = 4.6]

9. Li, T., Reigh, E., He, P., & Adah Miller, E. (2023). Can we and should we use artificial intelligence for formative assessment in science? Journal of Research in Science Teaching, 60(6), 1385–1389. https://doi.org/10.1002/tea.21867 [SSCI, IF = 4.6]

8. Li, T., He, P., & Peng, L. (2023). Measuring high school student engagement in science learning: an adaptation and validation study. International Journal of Science Education, 1-24. https://doi.org/10.1080/09500693.2023.2248668 [SSCI, IF = 2.3]

7. Li, T., Miller, E., Chen, I.C., Bartz, K., Codere, S., & Krajcik, J. (2021). The relationship between teacher’s support of literacy development and elementary students’ modelling proficiency in project-based learning science classrooms. Education 3-13: International Journal of Primary, Elementary and Early Years Education, 49(3), 302-316. https://doi.org/10.1080/03004279.2020.1854959  [ESCI, IF = 1.0]

6. Fackler, A. K., Adah Miller, E., Li, T. (2024). Promoting meaningful and equitable modeling practices in science classrooms. Science and Children.

5. He, P., Zheng, C., & Li, T. (2022). Development and validation of an instrument for measuring Chinese chemistry teachers’ perceived self-efficacy towards chemistry core competencies. International Journal of Science and Mathematics Education. 20(7),1337-1359. https://doi.org/10.1007/s10763-021-10216-8 [SSCI, IF = 2.2]

4. He, P., Zheng, C., & Li, T. (2022). High school students’ conceptions of chemical equilibrium in aqueous solutions: Development and validation of a two-tier diagnostic instrument. Journal of Baltic Science Education. 21(3), 428-444. https://doi.org/10.33225/jbse/22.21.428. [SSCI, IF = 1.2]

3. He, P., Zheng, C., & Li, T. (2021). Development and validation of an instrument for measuring Chinese chemistry teachers’ perceptions of pedagogical content knowledge for teaching chemistry core competencies. Chemistry Education Research and Practice, 22(2), 513-531. https://doi.org/10.1039/C9RP00286C [SSCI, IF = 3.0]

2. Krajcik, J., Schneider, B., Miller, E., Chen, I.-C., Bradford, L., Baker, Q., Bartz, K., Miller, C., Li, T., Codere, S., & Peek-Brown, D. (2023). Assessing the effect of project-based learning on science learning in elementary schools. American Education Research Journal, 60(1), 70-102. https://doi.org/10.3102/00028312221129247 [SSCI, IF = 3.6]

1. Adah Miller, E., Makori, H., Akgun, S., Miller, C., Li, T., & Codere, S. (2022). Including teachers in the social justice equation of project-based learning: A response to Lee & Grapin. Journal of Research in Science Teaching,1-7. https://doi.org/10.1002/tea.21805. [SSCI, IF = 4.6]

 

Book Chapters & Proceedings

8. He, J., Li, T., Xu, Z., & Xie, K. (Accepted). Leveraging Generative AI in Designing and Delivering Individualized Responsive Feedback for Pre-Service Teachers in Higher Education. In M. Adarkwah., S. Amposah., K. Schneider., R. Huang., M. Thomas (Eds.). Artificial Intelligence and Human Agency: Perspectives on Cognitive, Social, and Psychosocial Contexts.

7. Li, T., Miller, E. A., & Krajcik, J. S. (2023). Theory into practice: Supporting knowledge-in-use through project-based learning. In G. Bansal & U. Ramnarain (Eds.), Fostering Science Teaching and Learning for the Fourth Industrial Revolution and Beyond (pp. 1-35). IGI Global. https://doi.org/10.4018/978-1-6684-6932-3.ch001.

6. Miller. E., Li, T., Chen, I., & Codere, S. (2023). Using flexible thinking to assess student sensemaking of phenomena in project-based learning. In R. Tierney., F. Rizvi., K. Ercikan (Eds.). International Encyclopedia of Education (Fourth Edition), (pp. 444-457). Elsevier. https://doi.org/10.1016/B978-0-12-818630-5.13047-7.

5. Li, T. (2021). Developing deep learning through systems thinking. In Krajcik, J., & Schneider, B (Eds.). Science Education Through Multiple Literacies: Project-based Learning in Elementary School. (pp. 79-94) Cambridge: Harvard Education Press. https://hep.gse.harvard.edu/9781682536629/science-education-through-multiple-literacies/ 4. Li, T., Adah Miller, E., & He, P. (2024) Culturally and linguistically “blind” or biased? Challenges for AI assessment of models with Multiple Language Students. Proceedings of the Annual meeting of the International Society of the Learning Sciences (ISLS). 3. Li, T., Liu, F., & Krajcik, J. (2023) Automatically assess elementary students’ hand-drawn scientific models using deep learning of Artificial Intelligence. Proceedings of the Annual meeting of the International Society of the Learning Sciences (ISLS). 2. Wang, H., Li, T., Haudek, K., Royse, E., Manzanares, M., Adams, S., Horne, L., & Romulo, C. (2023). Is ChatGPT a threat to formative assessment in college-level science? An analysis of linguistic and content-level features to classify response types. Proceedings of the 4th International Conference of Artificial Intelligence in Educational Technology (AIET). 1. Miller, E., Li, T., Bateman, K., Akgun, S., Makori, H., Codere, S., Danziger, S., Simani, Maria C., & Krajcik, J.  (November 2022). Adaptation principles to foster engagement and equity in project-based science learning. Proceedings of the Annual meeting of the International Society of the Learning Sciences (ISLS).

Grants and Awards

Grants

Awarded (2024-2025)	Co-PI, Exploring Generative AI-Assisted Formative Feedback: A Comparison Including Student and Instructor Experiences and Perceptions. SimBio Foundation Grant, $10,000
Semi-finalist (2024)	PI, Cognitive synergy: Exploring the transformation between human intelligence and artificial intelligence in equitable next generation science assessment design, National Academy of Education/Spencer Dissertation Fellowship, $27,500 (Note: Application withdrawn due to accepting a tenure-track assistant professor position.)
Awarded (2024-2025)	Co-PI, Integrating Generative AI to Enhance Critical Multilingual Awareness in World Language Teacher Preparation.  American Council on the Teaching of Foreign Languages (ACTFL) Grant, $2,500

Awards and Fellowships

2024	NSF-Funded Modern Meta-Analysis Research Institute (MMARI) Fellow, NSF
2024	AERA Division C (Learning and Instruction) Mentoring Program Fellow, AERA
2024	AERA SIG LS/ATL (Learning Sciences/Advanced Technology for Learning) Mentoring Program Fellow, AERA
2024	Jhumki Basu Fellowship,National Association of Research in Science Teaching (NARST)
2023	Dr. Cassandra L. Book Graduate Fellowship in Education, Michigan State University, College of Education
2023	Graduate Student Research Award, AERA Science Teaching and Leaning (STL) SIG https://www.aera.net/About-AERA/Awards/SIG-Science-Teaching-and-Learning
2023	Summer Research Fellowships, Michigan State University, College of Education

 

Service

Professional Organization Service

2024-2027

Strand 12 (Technology for Teaching, Learning, and Research) Co-Chair, NARST Program Committee

 

Editorial Board Member

2024-2027

Journal of Research in Science Teaching