Grade Level Dynamics of the Predictive Role of College Students' Computational Thinking on Generative Artificial Intelligence Attitudes: Moderating Role of Gender and Major.
Background: The development of generative artificial intelligence (GenAI) technology has triggered ripple effects in teaching, learning and assessment in higher education, and college students' GenAI attitudes are key in determining its effective integration. Computational thinking (CT), as an amalg...
| Publicado en: | Journal of Computer Assisted Learning Vol. 42; no. 1; pp. 1 - 17 |
|---|---|
| Autores principales: | , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Feb2026
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=191181644&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 191181644 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Feb2026 vid: 42 iid: 1 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 191181644 191181644 191181644 10.1002/jcal.70192 191181644 ppf: 1 ppct: 16 formats: tig: atl: Grade Level Dynamics of the Predictive Role of College Students' Computational Thinking on Generative Artificial Intelligence Attitudes: Moderating Role of Gender and Major. aug: au: Sun, Lihui Liu, Junjie affil: School of Education, Minzu University of China, Beijing, China sug: subj: Students, College Psychosocial Factors Artificial Intelligence, Generative Student Attitudes Attitude to Computers Problem Solving Critical Thinking Sex Factors Predictive Value of Tests Academic Performance Human Male Female Adolescence Young Adult China Cross Sectional Studies Stratified Random Sample Questionnaires Descriptive Statistics Linear Regression T-Tests Data Analysis Software Funding Source Scales Science Engineering Educational Status Adolescent: 13-18 years Male Female ab: Background: The development of generative artificial intelligence (GenAI) technology has triggered ripple effects in teaching, learning and assessment in higher education, and college students' GenAI attitudes are key in determining its effective integration. Computational thinking (CT), as an amalgam of skills such as algorithmic thinking and problem‐solving, has become an essential skill for adapting to the digital age represented by GenAI technologies. Objectives: While previous research has confirmed the impact of CT on college students' technological attitudes, it has not clarified the relationship between CT and GenAI attitudes among college students and whether this relationship is affected by grade level, gender and major factors. This research gap limits our understanding of how CT contributes to the effective integration of GenAI in higher education. Methods: This study conducted a cross‐sectional survey of 1089 college students from China. Descriptive statistical analyses were conducted to present college students' GenAI attitudes and CT scores; sub‐group linear regression was employed to explore the predictive role of CT on GenAI attitudes; parametric t‐tests were used to analyse the differential effects of gender and major on GenAI attitudes and CT; and moderated models were applied to explore the mechanisms of influence among multiple factors. However, a cross‐sectional study can only reflect the static relationships among variables rather than their dynamic developmental processes. Future research could further explore these relationships through a longitudinal design. Results and Conclusion: First, this study found that CT positively predicted GenAI attitudes in all grades, showing an 'inverted U‐shaped' predictive curve, with the strongest predictive effect in the sophomore year. Second, the study also found that although female students and college students majoring in science and engineering had significantly higher GenAI attitudes and CT levels than male students and college students majoring in literature and history, the differences in GenAI attitudes and CT caused by gender and major changed with grade level. The differences caused by college students' gender were more significant in their freshman and sophomore years, and the differences caused by their major were more significant in their junior and senior years. Furthermore, the study discovered that the moderating effects of gender and major on the relationship between CT and GenAI became more significant as college students increased in grade level, and that female students and college students majoring in science and engineering were better predictors of changes in GenAI attitudes with respect to CT. Implications for Practice: This study revealed the non‐linear relationship between college students' CT and GenAI attitudes and revealed the dynamic differences caused by gender and major, providing precise evidence to support GenAI and CT phased education in higher education. Key Points: What is already known about this topic○Students' Generative Artificial Intelligence (GenAI) attitudes influence how GenAI is accepted and used in higher education.○Computational thinking (CT) supports students in understanding and applying GenAI technologies.○How CT relates specifically to GenAI attitudes across different grade levels is not yet well understood.What this paper adds○This study demonstrates that CT positively predicts college students' GenAI attitudes across all grade levels.○The strength of this predictive relationship varies by grade level and exhibits an "inverted U‐shaped" predictive curve.○Gender and major influence both CT levels and the extent to which CT predicts GenAI attitudes.Implications for practice and/or policy○Higher education institutions should systematically integrate CT into GenAI‐related curricula.○Teaching strategies should be adjusted according to students' grade level, gender, and major.○Additional curricular support is needed for literature and history majors to foster positive GenAI attitudes. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|