Unpacking the Effects of GenAI on Cultivating Students' Computational Thinking: A Meta-Analysis
Saved in:
| Title: | Unpacking the Effects of GenAI on Cultivating Students' Computational Thinking: A Meta-Analysis |
|---|---|
| Language: | English |
| Authors: | Jie Xu (ORCID |
| Source: | Journal of Educational Computing Research. 2026 64(4):1024-1067. |
| Availability: | SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com |
| Peer Reviewed: | Y |
| Page Count: | 44 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Information Analyses |
| Education Level: | Elementary Secondary Education Postsecondary Education |
| Descriptors: | Computation, Thinking Skills, Problem Solving, Artificial Intelligence, Technology Uses in Education, Effect Size, Instructional Program Divisions, Elementary Secondary Education, Postsecondary Education, Geographic Regions, Intervention, Incidence, Teaching Methods, Interaction, Role, Feedback (Response) |
| DOI: | 10.1177/07356331261419586 |
| ISSN: | 0735-6331 1541-4140 |
| Abstract: | Computational thinking (CT) is crucial for enhancing students' complex problem-solving abilities in the intelligent era. The emergence of generative artificial intelligence (GenAI) is profoundly transforming the global educational landscape and demonstrating significant potential for promoting personalized learning. However, the literature offers varied results on the effectiveness of using GenAI to cultivate students' CT. This study comprehensively investigated the effects of GenAI on students' CT and the role of moderating factors, integrating 45 effect sizes from 25 empirical studies published between 2022 and 2025. A theoretical framework of factors influencing students' CT was proposed based on activity theory, and the moderating factors included educational level, region, intervention duration, teaching mode, interaction mode, role setting, and feedback type. The results indicated that GenAI had a significant overall positive effect on students' CT development. Specifically, the largest effect size was computational practice, followed by computational concept and computational perspective. Furthermore, the analysis revealed that region, teaching mode, and interaction mode had significant moderating effects. Based on these results, this study offers targeted implications across the dimensions of theoretical foundation, educational practice, and technological development, providing empirical evidence for implementing GenAI teaching and developing GenAI tools to cultivate students' CT. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1502166 |
| Database: | ERIC |
| Abstract: | Computational thinking (CT) is crucial for enhancing students' complex problem-solving abilities in the intelligent era. The emergence of generative artificial intelligence (GenAI) is profoundly transforming the global educational landscape and demonstrating significant potential for promoting personalized learning. However, the literature offers varied results on the effectiveness of using GenAI to cultivate students' CT. This study comprehensively investigated the effects of GenAI on students' CT and the role of moderating factors, integrating 45 effect sizes from 25 empirical studies published between 2022 and 2025. A theoretical framework of factors influencing students' CT was proposed based on activity theory, and the moderating factors included educational level, region, intervention duration, teaching mode, interaction mode, role setting, and feedback type. The results indicated that GenAI had a significant overall positive effect on students' CT development. Specifically, the largest effect size was computational practice, followed by computational concept and computational perspective. Furthermore, the analysis revealed that region, teaching mode, and interaction mode had significant moderating effects. Based on these results, this study offers targeted implications across the dimensions of theoretical foundation, educational practice, and technological development, providing empirical evidence for implementing GenAI teaching and developing GenAI tools to cultivate students' CT. |
|---|---|
| ISSN: | 0735-6331 1541-4140 |
| DOI: | 10.1177/07356331261419586 |
