Artificial Intelligence in Science Education Research: Current States and Challenges

Abstract The recent surge of artificial intelligence (AI) in science education has heightened interest among the NARST community—a curiosity about how technology can transform education that has lasted for decades. Founded in 1928, NARST is an international organization of thousands of members focused on improving science education through research. This growing interest is evidenced by the launch of the Research in Artificial Intelligence-Involved Science Education (RAISE) Research Interest Group in 2022 and the increasing number of AI-related studies presented at NARST conferences. Despite the growth, limited studies have shed light on how the community members integrate AI into science education and the challenges. We systematically reviewed 36 AI-related papers presented at the 2024 NARST conference to address this gap. We found that AI (1) is widely integrated into science education research with ethical considerations, (2) serves to continue the paradigm of science education rather than redefining it, and (3) evolves into a multipurpose, multimodal, and generative tool for research and teaching. The analysis also revealed five major challenges: (1) changes in educational goals, (2) renovating science learning content, (3) complexity of assessment for individualized feedback, (4) teachers’ preparedness and professional development, and (5) bias and equity in STEM education. Consequently, the role of NARST and RAISE RIG members that respond to those challenges is readdressed.

Authors

Institutions

Publication Details

Journal
Journal of Science Education and Technology
Published
2025-06-21
DOI
https://doi.org/10.1007/s10956-025-10239-8
Citations
11
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
18.63

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Artificial Intelligence in Science Education Research: Current States and Challenges

Gyeong-Geon Lee, Xiaoming Zhaı, Minji Yun, Kent J. Crippen
11 citations
Journal of Science Education and Technology
Online Learning and Analytics
18.63
article

Artificial Intelligence in Science Education Research: Current States and Challenges

Gyeong-Geon Lee, Xiaoming Zhaı, Minji Yun, Kent J. Crippen
article en
11 citations

Abstract

Abstract The recent surge of artificial intelligence (AI) in science education has heightened interest among the NARST community—a curiosity about how technology can transform education that has lasted for decades. Founded in 1928, NARST is an international organization of thousands of members focused on improving science education through research. This growing interest is evidenced by the launch of the Research in Artificial Intelligence-Involved Science Education (RAISE) Research Interest Group in 2022 and the increasing number of AI-related studies presented at NARST conferences. Despite the growth, limited studies have shed light on how the community members integrate AI into science education and the challenges. We systematically reviewed 36 AI-related papers presented at the 2024 NARST conference to address this gap. We found that AI (1) is widely integrated into science education research with ethical considerations, (2) serves to continue the paradigm of science education rather than redefining it, and (3) evolves into a multipurpose, multimodal, and generative tool for research and teaching. The analysis also revealed five major challenges: (1) changes in educational goals, (2) renovating science learning content, (3) complexity of assessment for individualized feedback, (4) teachers’ preparedness and professional development, and (5) bias and equity in STEM education. Consequently, the role of NARST and RAISE RIG members that respond to those challenges is readdressed.

Journal of Science Education and Technology
University of Georgia (US), University of Florida (US), Florida College (US)
National Science Foundation
Quality Education
Openalex Percentile: Top 1%
Online Learning and Analytics
18.63
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Artificial Intelligence in Science Education Research: Current States and Challenges — Gyeong-Geon Lee, Xiaoming Zhaı, et al. · Journal of Science Education and Technology (2025) | TGRS Research Map | TGRS