AI literacy predicts computational thinking through multidimensional interactions among Chinese high school students

With the promotion of AI applications, people have undergone various changes, especially high school students who have a strong sense of engagement in AI-related activities. In order to explore the interactive mechanism and influencing factors between AI literacy and computing thinking level of high school students in H city, this study focuses on the relationship between student background, AI literacy, and computational thinking. By conducting a questionnaire survey of hundreds of high school students and using SPSS for descriptive statistics, analysis of variance, and correlation analysis, the focus is on exploring the impact and interrelationships between the three dimensions of AI literacy and the five dimensions of computational thinking. A structural equation model (SEM) was constructed by using AMOS software to further explore its internal complex correlation relationship. The results show that parental education and daily use of AI tools significantly affect students' AI knowledge and skills, while factors such as gender and family location have different degrees of positive or negative effects on creativity, algorithmic thinking, and critical thinking. In addition, artificial intelligence literacy is moderately positively correlated with some dimensions of computational thinking. This study provides empirical support for the rational planning of AI courses in the basic education stage, strengthening the cultivation of students' computational thinking and optimizing teaching practice.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2025-09-26
DOI
https://doi.org/10.1038/s41598-025-16712-z
Citations
1
Primary Topic
Teaching and Learning Programming
Type
article
Field-Weighted Citation Impact
1.69
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AI literacy predicts computational thinking through multidimensional interactions among Chinese high school students

Xiaogang Jin, Chenzhi Zhang, Tao Bi, Yurong Guan et al.
1 citations
Scientific Reports
Teaching and Learning Programming
1.69
article

AI literacy predicts computational thinking through multidimensional interactions among Chinese high school students

Xiaogang Jin, Chenzhi Zhang, Tao Bi, Yurong Guan, Zhihua Hu, Huili He, W. Zhang
article en
1 citations

Abstract

With the promotion of AI applications, people have undergone various changes, especially high school students who have a strong sense of engagement in AI-related activities. In order to explore the interactive mechanism and influencing factors between AI literacy and computing thinking level of high school students in H city, this study focuses on the relationship between student background, AI literacy, and computational thinking. By conducting a questionnaire survey of hundreds of high school students and using SPSS for descriptive statistics, analysis of variance, and correlation analysis, the focus is on exploring the impact and interrelationships between the three dimensions of AI literacy and the five dimensions of computational thinking. A structural equation model (SEM) was constructed by using AMOS software to further explore its internal complex correlation relationship. The results show that parental education and daily use of AI tools significantly affect students' AI knowledge and skills, while factors such as gender and family location have different degrees of positive or negative effects on creativity, algorithmic thinking, and critical thinking. In addition, artificial intelligence literacy is moderately positively correlated with some dimensions of computational thinking. This study provides empirical support for the rational planning of AI courses in the basic education stage, strengthening the cultivation of students' computational thinking and optimizing teaching practice.

Scientific ReportsVol. 15(1)
Huanggang Normal University (CN)
Openalex Percentile: Top 10%
Teaching and Learning Programming
1.69
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.

AI literacy predicts computational thinking through multidimensional interactions among Chinese high school students — Xiaogang Jin, Chenzhi Zhang, et al. · Scientific Reports (2025) | TGRS Research Map | TGRS