The Role of AI in Fostering Computational Thinking and Self-Efficacy in Educational Settings: A Systematic Review

Muhammad Hassan Massaty, Slamet Kurniawan Fahrurozi, Cucuk Wawan Budiyanto

Abstract

This systematic review explores the impact of artificial intelligence (AI) on the development of computational thinking and self-efficacy within educational environments. Computational thinking, a foundational skill in problem-solving, is increasingly recognized as essential for navigating a technology-driven world. AI tools and methodologies have been integrated into educational practices to enhance learning experiences and outcomes. This review synthesizes current literature to examine how AI technologies contribute to the cultivation of computational thinking skills among students of various ages and educational levels. Furthermore, it investigates the role of AI in bolstering students' self-efficacy confidence in one's ability to achieve goals in learning computational concepts. By analyzing empirical studies and educational interventions, this review identifies key mechanisms through which AI supports the development of these critical skills. Insights from this synthesis underscore the potential of AI to transform educational paradigms, providing opportunities for personalized learning, adaptive feedback, and collaborative problem-solving. The findings highlight implications for educators, policymakers, and researchers aiming to leverage AI effectively in educational settings to foster computational thinking and enhance students' self-efficacy, thereby preparing them for future challenges in a digital age.

Keywords

Artificial Intelligence, Computational Thinking, Self-Efficacy.

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References

Abelson, H., & Breazeal, C. (2022). Computational Thinking Education in K–12: Artificial Intelligence Literacy and Physical Computing. The MIT Press. https://doi.org/10.7551/mitpress/13375.001.0001

Alkhatlan, A., & Kalita, J. (2019). Intelligent Tutoring Systems: A Comprehensive Historical Survey with Recent Developments. International Journal of Computer Applications, 181, 1-20. https://doi.org/10.5120/ijca2019918451

Allsop, Y. (2019). Assessing computational thinking process using a multiple evaluation approach. International Journal of Child-Computer Interaction, 19, 30-55. https://doi.org/https://doi.org/10.1016/j.ijcci.2018.10.004

Almasri, F. (2024). Exploring the Impact of Artificial Intelligence in Teaching and Learning of Science: A Systematic Review of Empirical Research. Research in Science Education. https://doi.org/10.1007/s11165-024-10176-3

Ashrafi, M., & Aslam, M. (2024). Generative AI in Education: Anticipating Challenges for Future Learning. https://doi.org/10.13140/RG.2.2.31144.38408

Bandura, A. (2006). Guide for Constructing Self-Efficacy Scales (Revised). Self-efficacy beliefs of adolescents, 5, 307–337.

Celik, I. (2023). Exploring the Determinants of Artificial Intelligence (AI) Literacy: Digital Divide, Computational Thinking, Cognitive Absorption. Telematics and Informatics, 83, 102026. https://doi.org/https://doi.org/10.1016/j.tele.2023.102026

Chai, C. S., Lin, P.-Y., Jong, M. S.-Y., Dai, Y., Chiu, T. K. F., & Qin, J. (2021). Perceptions of and Behavioral Intentions towards Learning Artificial Intelligence in Primary School Students. Educational Technology & Society, 24(3), 89-101. https://www.jstor.org/stable/27032858

Chatterjee, S., & Bhattacharjee, K. K. (2020). Adoption of artificial intelligence in higher education: a quantitative analysis using structural equation modelling. Education and Information Technologies, 25(5), 3443-3463. https://doi.org/https://doi.org/10.1007/s10639-020-10159-7

Chen, X., Xie, H., Zou, D., & Hwang, G.-J. (2020). Application and theory gaps during the rise of Artificial Intelligence in Education. Computers and Education: Artificial Intelligence, 1, 100002. https://doi.org/10.1016/j.caeai.2020.100002

Chiu, T. K. F., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2023). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 4, 100118. https://doi.org/https://doi.org/10.1016/j.caeai.2022.100118

Chu, S.-T., Hwang, G.-J., & Tu, Y.-F. (2022). Artificial intelligence-based robots in education: A systematic review of selected SSCI publications. Computers and Education: Artificial Intelligence, 3, 100091. https://doi.org/https://doi.org/10.1016/j.caeai.2022.100091

Das, A., Malaviya, S., & Singh, M. (2023). The Impact of AI-Driven Personalization on Learners' Performance. International Journal of Computer Sciences and Engineering, 11, 15-22. https://doi.org/10.26438/ijcse/v11i8.1522

Dignum, V. (2021). The role and challenges of education for responsible AI. London Review of Education, 19. https://doi.org/10.14324/LRE.19.1.01

Eminoğlu, A., & Çelikkanat, Ş. (2024). Assessment of the relationship between executive Nurses’ leadership Self-Efficacy and medical artificial intelligence readiness. International Journal of Medical Informatics, 184, 105386. https://doi.org/https://doi.org/10.1016/j.ijmedinf.2024.105386

Fryer, L. K., Thompson, A., Nakao, K., Howarth, M., & Gallacher, A. (2020). Supporting self-efficacy beliefs and interest as educational inputs and outcomes: Framing AI and Human partnered task experiences. Learning and Individual Differences, 80, 101850. https://doi.org/https://doi.org/10.1016/j.lindif.2020.101850

Gadanidis, G. (2017). Artificial intelligence, computational thinking, and mathematics education. The International Journal of Information and Learning Technology, 34(2), 133-139. https://doi.org/10.1108/IJILT-09-2016-0048

García-Martínez, I., Fernández-Batanero, J. M., Fernández-Cerero, J., & León, S. P. (2023). Analysing the Impact of Artificial Intelligence and Computational Sciences on Student Performance: Systematic Review and Meta-analysis. Journal of New Approaches in Educational Research; Vol 12, No 1 (2023)DO - 10.7821/naer.2023.1.1240.

https://naerjournal.com/article/view/1240

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education. Promise and Implications for Teaching and Learning.

Holstein, K., McLaren, B., & Aleven, V. (2018). Student Learning Benefits of a Mixed-Reality Teacher Awareness Tool in AI-Enhanced Classrooms. In (pp. 154-168). https://doi.org/10.1007/978-3-319-93843-1_12

Hong, H., & Kim, Y. (2024). Applying artificial intelligence in career education for students with intellectual disabilities: The effects on career self-efficacy and learning flow. Education and Information Technologies. https://doi.org/10.1007/s10639-024-12809-6

Hsu, T.-C., Chang, C., & Lin, Y.-W. (2023). Effects of voice assistant creation using different learning approaches on performance of computational thinking. Computers & Education, 192, 104657. https://doi.org/https://doi.org/10.1016/j.compedu.2022.104657

Huang, L. (2023). Ethics of Artificial Intelligence in Education: Student Privacy and Data Protection. Science Insights Education Frontiers, 16, 2577-2587. https://doi.org/10.15354/sief.23.re202

Huang, X. (2021). Aims for cultivating students’ key competencies based on artificial intelligence education in China. Education and Information Technologies, 26(5), 5127-5147. https://doi.org/https://doi.org/10.1007/s10639-021-10530-2

Huang, X., & Qiao, C. (2024). Enhancing Computational Thinking Skills Through Artificial Intelligence Education at a STEAM High School. Science & Education, 33(2), 383-403. https://doi.org/10.1007/s11191-022-00392-6

Jing, Y., Zhao, L., Zhu, K., Wang, H., Wang, C., & Xia, Q. (2023). Research Landscape of Adaptive Learning in Education: A Bibliometric Study on Research Publications from 2000 to 2022. Sustainability, 15, 3115. https://doi.org/10.3390/su15043115

Ke, Z., & Ng, V. (2019). Automated Essay Scoring: A Survey of the State of the Art. https://doi.org/10.24963/ijcai.2019/879

Kim, B.-J., & Kim, M.-J. (2024). The influence of work overload on cybersecurity behavior: A moderated mediation model of psychological contract breach, burnout, and self-efficacy in AI learning such as ChatGPT. Technology in Society, 77, 102543. https://doi.org/https://doi.org/10.1016/j.techsoc.2024.102543

Kirkwood, A., & Price, L. (2013). Technology-enhanced Learning and Teaching in Higher Education: What is ‘enhanced’ and how do we know? A Critical Literature Review. Learning Media & Technology, 39. https://doi.org/10.1080/17439884.2013.770404

König, P. D., & Wenzelburger, G. (2020). Opportunity for renewal or disruptive force? How artificial intelligence alters democratic politics. Government Information Quarterly, 37(3), 101489. https://doi.org/https://doi.org/10.1016/j.giq.2020.101489

Kulik, J., & Fletcher, J. D. (2015). Effectiveness of Intelligent Tutoring Systems: A Meta-Analytic Review. Review of Educational Research, 86. https://doi.org/10.3102/0034654315581420

Lin, Y.-S., Chen, S.-Y., Tsai, C.-W., & Lai, Y.-H. (2021). Exploring Computational Thinking Skills Training Through Augmented Reality and AIoT Learning [Brief Research Report]. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.640115

Luckin, R., & Holmes, W. (2016). Intelligence Unleashed: An argument for AI in Education. London: Pearson.

Luckin, R., Holmes, W., Griffiths, M., Lab, U. K., Corcier, L. B., Pearson, & University College, L. (2016). Intelligence Unleashed: An Argument for AI in Education. Pearson.

Massaty, M. H., Budiyanto, C. W., & Tamrin, A. G. (2020). Revisiting the roles of educational robotics in improving learners’ computational thinking skills and their positive behaviour. Journal of Physics: Conference Series, 1511(1), 012088. https://doi.org/https://doi.org/10.1088/1742-6596/1511/1/012088

Matovu, H., Ungu, D. A. K., Won, M., Tsai, C.-C., Treagust, D. F., Mocerino, M., & Tasker, R. (2023). Immersive virtual reality for science learning: Design, implementation, and evaluation. Studies in Science Education, 59(2), 205-244. https://doi.org/https://doi.org/10.1080/03057267.2022.2082680

Mhlongo, S., Mbatha, K., Ramatsetse, B., & Dlamini, R. (2023). Challenges, opportunities, and prospects of adopting and using smart digital technologies in learning environments: An iterative review. Heliyon, 9(6), e16348. https://doi.org/https://doi.org/10.1016/j.heliyon.2023.e16348

Montag, C., Kraus, J., Baumann, M., & Rozgonjuk, D. (2023). The propensity to trust in (automated) technology mediates the links between technology self-efficacy and fear and acceptance of artificial intelligence. Computers in Human Behavior Reports, 11, 100315. https://doi.org/https://doi.org/10.1016/j.chbr.2023.100315

Moorhouse, B. L., Yeo, M. A., & Wan, Y. (2023). Generative AI tools and assessment: Guidelines of the world's top-ranking universities. Computers and Education Open, 5, 100151. https://doi.org/https://doi.org/10.1016/j.caeo.2023.100151

Moroianu, N., Iacob, S.-E., & Constantin, A. (2023). Artificial Intelligence in Education: a Systematic Review. In (pp. 906-921). https://doi.org/10.2478/9788367405546-084

Ng, D. T. K., Su, J., & Chu, S. (2023). Fostering Secondary School Students’ AI Literacy through Making AI-Driven Recycling Bins. Education and Information Technologies, 1-32. https://doi.org/10.1007/s10639-023-12183-9

Nicolaou, C., Matsiola, M., & Kalliris, G. (2019). Technology-Enhanced Learning and Teaching Methodologies through Audiovisual Media. Education Sciences, 9(3), 196. https://doi.org/10.3390/educsci9030196

Ou, A. W., Stöhr, C., & Malmström, H. (2024). Academic communication with AI-powered language tools in higher education: From a post-humanist perspective. System, 121, 103225. https://doi.org/https://doi.org/10.1016/j.system.2024.103225

Ouyang, F., Zheng, L., & Jiao, P. (2022). Artificial intelligence in online higher education: A systematic review of empirical research from 2011 to 2020. Education and Information Technologies, 27(6), 7893-7925. https://doi.org/https://doi.org/10.1007/s10639-022-10925-9

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., . . . Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71

Paletta, L., Russegger, S., Albert, D., Reininghaus, E., Lenger, M., Fellner, M., . . . Pötz, G. (2023). AI-enabled Playful Enhancement of Resilience and Self-Efficacy with Psychological Learning Theory. https://doi.org/10.54941/ahfe1003973

Park, J., Teo, T., Teo, A., Chang, J., Huang, J., & Koo, S. (2023). Integrating artificial intelligence into science lessons: teachers’ experiences and views. International Journal of STEM Education, 10. https://doi.org/10.1186/s40594-023-00454-3

Reddy, S., Fox, J., & Purohit, M. P. (2018). Artificial intelligence-enabled healthcare delivery. Journal of the Royal Society of Medicine, 112(1), 22-28. https://doi.org/10.1177/0141076818815510

Rudolph, J., Tan, S., & Tan, S. (2023). ChatGPT: Bullshit Spewer or the End of Traditional Assessments in Higher Education? Journal of Applied Learning and Teaching, 6, 342-363. https://doi.org/https://doi.org/10.37074/jalt.2023.6.1.9

Scott, K. W., & Howell, D. (2008). Clarifying Analysis and Interpretation in Grounded Theory: Using a Conditional Relationship Guide and Reflective Coding Matrix. International Journal of Qualitative Methods, 7(2), 1-15. https://doi.org/10.1177/160940690800700201

Vanlehn, K. (2011). The Relative Effectiveness of Human Tutoring, Intelligent Tutoring Systems, and Other Tutoring Systems. Educational Psychologist, 46, 197-221. https://doi.org/10.1080/00461520.2011.611369

Wang, C.-J., Zhong, H.-X., Chiu, P.-S., Chang, J.-H., & Wu, P.-H. (2022). Research on the Impacts of Cognitive Style and Computational Thinking on College Students in a Visual Artificial Intelligence Course. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.864416

Wang, S., Sun, Z., & Chen, Y. (2023). Effects of higher education institutes’ artificial intelligence capability on students' self-efficacy, creativity and learning performance. Education and Information Technologies, 28(5), 4919-4939. https://doi.org/10.1007/s10639-022-11338-4

Wing, J. (2006). Computational Thinking. Communications of the ACM, 49, 33-35. https://doi.org/https://doi.org/10.1145/1118178.1118215

Wing, J. M. (2017). Computational thinking’s influence on research and education for all. Italian Journal of Educational Technology, 25(2), 7-14. https://doi.org/10.17471/2499-4324/922

Won, M., Ungu, D. A. K., Matovu, H., Treagust, D. F., Tsai, C.-C., Park, J., . . . Tasker, R. (2023). Diverse approaches to learning with immersive Virtual Reality identified from a systematic review. Computers & Education, 195, 104701. https://doi.org/https://doi.org/10.1016/j.compedu.2022.104701

Woolf, B. (2008). Building Intelligent Interactive Tutors, Student-Centered Strategies for Revolutionizing E-Learning. Elsevier & Morgan Kaufmann.

Xu, W., & Ouyang, F. (2022). The application of AI technologies in STEM education: a systematic review from 2011 to 2021. International Journal of STEM Education, 9. https://doi.org/10.1186/s40594-022-00377-5

Yang, Y.-F., Tseng, C. C., & Lai, S.-C. (2024). Enhancing teachers’ self-efficacy beliefs in AI-based technology integration into English speaking teaching through a professional development program. Teaching and Teacher Education, 144, 104582. https://doi.org/https://doi.org/10.1016/j.tate.2024.104582

Yilmaz, R., & Karaoglan Yilmaz, F. G. (2023). The effect of generative artificial intelligence (AI)-based tool use on students' computational thinking skills, programming self-efficacy and motivation. Computers and Education: Artificial Intelligence, 4, 100147. https://doi.org/https://doi.org/10.1016/j.caeai.2023.100147

Yin, M., Jiang, S., & Niu, X. (2024). Can AI really help? The double-edged sword effect of AI assistant on employees’ innovation behavior. Computers in Human Behavior, 150, 107987. https://doi.org/https://doi.org/10.1016/j.chb.2023.107987

Zawacki-Richter, O., Marín, V., 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, 1-27. https://doi.org/10.1186/s41239-019-0171-0

Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., . . . Li, Y. (2021). A Review of Artificial Intelligence (AI) in Education from 2010 to 2020. Complexity, 2021, 8812542. https://doi.org/10.1155/2021/8812542