| ChatGPT user experience: Implications for education X Zhai SSRN Journal, 2022 | 516 | 2022 |
| Assessing computational thinking: A systematic review of empirical studies X Tang, Y Yin, Q Lin, R Hadad, X Zhai Computers & Education 148, 103798, 2020 | 516 | 2020 |
| Artificial intelligence in education research during 2013– 2023: A review based on bibliometric analysis S Guo, Y Zheng, X Zhai Education and Information Technologies, 2024 | 316* | 2024 |
| Applying machine learning in science assessment: A systematic review X Zhai, Y Yin, JW Pellegrino, KC Haudek, L Shi Studies in Science Education 56 (1), 111-151, 2020 | 172 | 2020 |
| ChatGPT for next generation science learning X Zhai XRDS: Crossroads, The ACM Magazine for Students 29 (3), 42-46, 2023 | 118 | 2023 |
| From substitution to redefinition: A framework of machine learning‐based science assessment X Zhai, K C Haudek, L Shi, R H Nehm, M Urban‐Lurain Journal of Research in Science Teaching 57 (9), 1430-1459, 2020 | 75 | 2020 |
| Large-Scale Assessment in Science Education X Zhai, JW Pellegrino Handbook of Research on Science Education 3, 1045- 1098, 2022 | 73* | 2022 |
| Assessing learning in technology-rich maker activities: A systematic review of empirical research Q Lin, Y Yin, X Tang, R Hadad, X Zhai Computers & Education 157, 103944, 2020 | 68 | 2020 |
| Understanding the relationship between levels of mobile technology use in high school physics classrooms and the learning outcome X Zhai, M Zhang, M Li, X Zhang British journal of educational technology 50 (2), 750-766, 2019 | 63 | 2019 |
| Practices and theories: How can machine learning assist in innovative assessment practices in science education X Zhai Journal of Science Education and Technology 30 (2), 139-149, 2021 | 61 | 2021 |
| A meta-analysis of machine learning-based science assessments: Factors impacting machine-human score agreements X Zhai, L Shi, RH Nehm Journal of Science Education and Technology 30, 361-379, 2021 | 54 | 2021 |
| AGI: Artificial general intelligence for education E Latif, G Mai, M Nyaaba, X Wu, N Liu, G Lu, S Li, T Liu, X Zhai https://doi.org/10.48550/arXiv.2304.12479, 2023 | 53* | 2023 |
| Applying machine learning to automatically assess scientific models X Zhai, P He, J Krajcik Journal of Research in Science Teaching 59 (10), 1765-1794, 2022 | 53 | 2022 |
| One‐to‐one mobile technology in high school physics classrooms: Understanding its use and outcome X Zhai, M Zhang, M Li British Journal of Educational Technology 49 (3), 516-532, 2018 | 51 | 2018 |
| Using machine learning to score multi-dimensional assessments of chemistry and physics S Maestrales, X Zhai, I Touitou, Q Baker, B Schneider, J Krajcik Journal of Science Education and Technology 30, 239-254, 2021 | 43 | 2021 |
| On the validity of machine learning-based Next Generation Science Assessments: A validity inferential network X Zhai, J Krajcik, JW Pellegrino Journal of Science Education and Technology 30, 298-312, 2021 | 43 | 2021 |
| Understanding how the perceived usefulness of mobile technology impacts physics learning achievement: A pedagogical perspective X Zhai, L Shi Journal of Science Education and Technology 29 (6), 743-757, 2020 | 42 | 2020 |
| Context matters: A strategy to pre-train language model for science education Z Liu, X He, L Liu, T Liu, X Zhai International Conference on Artificial Intelligence in Education, 666–674, 2023 | 37 | 2023 |
| Examining the uses of student-led, teacher-led, and collaborative functions of mobile technology and their impacts on physics achievement and interest X Zhai, M Li, S Chen Journal of Science Education and Technology 28, 310-320, 2019 | 35 | 2019 |
| Developing Learning Progressions: Nature and Instructional Strategies X Zhai, Y Guo, M Li Education Science 31 (2), 47-51, 2015 | 32* | 2015 |
Kevin C. HaudekAssistant Professor, Michigan State UniversityVerified email at msu.edu
