
TOTAL VIEWS: 210
Artificial Intelligence is transforming the sources of data, analytical methodologies, and modes of result presentation within educational science research. As the utilization of data from learning platforms, classroom interaction records, student profiles, and other digital resources becomes increasingly prevalent, there is a growing need for a more explicit modeling framework in educational research. This study introduces the Point-Path-Plane-Pillar (P4) framework, which conceptualizes educational science research through four interconnected stages: node extraction, relationship analysis, system construction and validation, and scenario interpretation. Adhering to the sequential logic of “Point—Path—Plane—Pillar,” the framework integrates research data sources, structural relationships, evaluation mechanisms, and practical applications. It offers a systematic reference for developing educational research models within the context of Artificial Intelligence.
P4 framework; educational science research system; Artificial Intelligence
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https://doi.org/10.1016/j.caeai.2020.100001
Lambert, L. S., & Newman, D. A. (2023). Construct development and validation in three practical steps: Recommendations for reviewers, editors, and authors. Organizational Research Methods, 26(4), 574-607. https://doi.org/10.1177/10944281221115374
Lemay, D. J., Baek, C., & Doleck, T. (2021). Comparison of learning analytics and educational data mining: A topic modeling approach. Computers and Education: Artificial Intelligence, 2, 100016.
https://doi.org/10.1016/j.caeai.2021.100016
Oxley, E., Nash, H. M., & Weighall, A. R. (2025). Consensus building using the Delphi method in educational research: A case study with educational professionals. International Journal of Research & Method in Education, 48(1), 29-43.
https://doi.org/10.1080/1743727X.2024.2317851
Steinert, Y., Fontes, K., Mortaz-Hejri, S., Quaiattini, A., & Yousefi-Nooraie, R. (2024). Social network analysis in undergraduate and postgraduate medical education: A scoping review. Academic Medicine, 99(4), 452-465.
https://doi.org/10.1097/ACM.0000000000005620
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on Artificial Intelligence applications in higher education—Where are the educators? International Journal of Educational Technology in Higher Education, 16, 39.
https://doi.org/10.1186/s41239-019-0171-0
Jia, F., Sun, D., & Looi, C. K. (2024). Artificial intelligence in science education (2013-2023): Research trends in ten years. Journal of Science Education and Technology, 33(1), 94-117.
Lee, G., Yun, M., Zhai, X., & Crippen, K. (2025). Artificial intelligence in science education research: Current states and challenges. Journal of Science Education and Technology, 1-18.
Point‑Path‑Plane‑Pillar (P4): Innovative Paradigm of Educational Science Research System
How to cite this paper: Deming Li, Xinran Ma. (2026). Point‑Path‑Plane‑Pillar (P4): Innovative Paradigm of Educational Science Research System. The Educational Review, USA, 10(4), 193-198.
DOI: http://dx.doi.org/10.26855/er.2026.04.002