Computational Thinking in High School Mathematics
DOI:
https://doi.org/10.46328/ijemst.5250Keywords:
Computational Thinking (CT), Artificial Intelligence (AI), High school, Mathematics, Problem solvingAbstract
This literature review explores the integration of Computational Thinking (CT) and Artificial Intelligence (AI) in high school mathematics education. CT, which involves decomposition, pattern recognition, abstraction, and algorithm design, provides a structured framework for solving complex problems. When combined with AI tools such as Wolfram Alpha, Photomath, GeoGebra, Symbolab, and ChatGPT, these skills are enhanced through immediate feedback, solution verification, and personalized learning support. Mathematical problem-solving remains a foundational element of education and technological advancement. CT fosters logical reasoning and creativity, while AI enables efficient exploration of mathematical concepts through automation and intelligent systems. This integration not only supports deeper conceptual understanding but also helps educators tailor instruction to diverse learning needs. The review highlights three key themes: (1) AI integration improves students’ performance in mathematics; (2) game-based learning (GBL) supported by AI increases engagement; and (3) CT development strengthens mathematical reasoning.
References
Adiguzel, T., Kaya, M. H., & Cansu, F. K. (2023). Revolutionizing education with AI: Exploring the transformative potential of ChatGPT. Contemporary Educational Technology, 15(3), Article ep429https://doi.org/10.30935/cedtech/13152
Atlas, S. (2023). ChatGPT for higher education and professional development: A guide to conversational AI.
https://digitalcommons.uri.edu/cba_facpubs/548
Baez, R., Sanchez, H., & Pllana, D. (2024). Creativity in high school through drawing with polynomials. Online Submission, Advances in Social Sciences Research Journal, 11(1), 318–332. https://eric.ed.gov/?id=ED641705
Baez, R., Sanchez, H., Sandeep, N., & Pllana, D. (2025). Creativity and critical thinking in the AI age: A high School perspective. International Journal of Research in Education and Science (IJRES), 11(2), 448-477. https://doi.org/10.46328/ijres.1290
Baidoo-Anu, D., & Ansah, L. O. (2023). Education in the era of generative artificial intelligence (AI):Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, 7(1), 52–62. https://dergipark.org.tr/en/pub/jai/issue/77844/1337500
Bayaga, A. (2024). Enhancing mathematics problem-solving skills in an AI-driven environment: Integrated SEM-neural network approach. Computers in Human Behavior Reports, 10, Article 100491. https://doi.org/10.1016/j.chbr.2024.100491
BBC Bitesize. (n.d.). What does decomposition mean? BBC.https://www.bbc.co.uk/bitesize/articles/zxxdqfr#zhhqm39
Biography.com Editors. (2020, July 22). Alan Turing. Biography. https://www.biography.com/scientists/alan-turing
Botana, F., Recio, T., & Vélez, M. P. (2024). On using GeoGebra and ChatGPT for geometric discovery.Computers, 13(8), 187. https://doi.org/10.3390/computers13080187
Bryant, C. (2015, April 1). Mathematical habits of mind. Edutopia. https://www.edutopia.org/blog/mathematical-habits-of-mind-cindy-bryant
Buetow, S., & Lovatt, J. (2024). From insight to innovation: Harnessing artificial intelligence for dynamic literaturereviews. The Journal of Academic Librarianship, 50(4), 102814. https://doi.org/10.1016/j.acalib.2024.102814
Cee, K. (2024, December 28). 2025 math project ideas: Creative approaches for high school students. Inspirit AI. Retrieved June 29, 2025, from https://www.inspiritai.com/blogs/ai-blog/math-project-ideas
Chechan, B., Ampadu, E., & Pears, A. (2023). Effect of using Desmos on high school students’ understanding and learning of functions. Eurasia Journal of Mathematics, Science and Technology Education, 19(10), Article em2331. https://doi.org/10.29333/ejmste/13540
Chen S. Y., Lai Y. H., Lin Y. S. (2020b). Research on head-mounted virtual reality and computational thinkingexperiments to improve the learning effect of AIoT maker course: Case of earthquake relief scenes. Frontiers in Psychology, 11, 1164. https://doi.org/10.3389/fpsyg.2020.01164
Chorney, S. (2021). Classroom practice and craft knowledge in teaching mathematics using Desmos: Challenges and strategies. International Journal of Mathematical Education in Science and Technology, 53(12), 3203–3227. https://doi.org/10.1080/0020739X.2021.1931974
Clarke, D., & Roche, A. (2018). Using contextualized tasks to engage students in meaningful and worthwhile mathematics learning. Journal of Mathematical Behavior, 51, 95-108. https://doi.org/10.1016/j.jmathb.2017.11.006
Dohn, N. B., Kafai, Y., Mørch, A., & Ragni, M. (2022). Survey: Artificial intelligence, computational thinking and learning. KI - Künstliche Intelligenz, 36(1), 5-16. https://doi.org/10.1007/s13218-021-00751-5
Durksen, T., Way, J., Bobis, J., Anderson, J., Skilling, K., & Martin, A. (2017). Motivation and engagement in mathematics: A qualitative framework for teacher–student interactions. Mathematics Education Research Journal, 29(2), 34–48. https://doi.org/10.1007/s13394-017-0199-1
FACTS – A Nelnet Company. (n.d.). Confidence Counts! How schools and edtech are shaping bold students. FACTS. Retrieved [July 2, 2025], from https://factsmgt.com/blog/how-schools-and-edtech-are-shaping-bold-students/
Falk, E. (2022, September 15). How to engage students with computational thinking practices in math. Digital Promise. https://digitalpromise.org/2022/09/15/how-to-engage-students-with-computational-thinking-practices-in-math/
Ferrari, P. L. (2003). Abstraction in mathematics. Philosophical Transactions of the Royal Society of London. Series: Biological Sciences, 358(1435), 1225–1230. https://doi.org/10.1098/rstb.2003.1316
FinTech Weekly. (2023 August, 3). Why Mat is Important in Today’s Technology, retrieved from, https://www.fintechweekly.com/magazine/articles/why-math-is-important-in-today-s-technologies
Johnston, M. (2023, November 23). Computer science and computational thinking. Edtechist. https://edtechist.co.uk/computer-science-and-computational-thinking/
Hansen, N. K., & Hadjerrouit, S. (2021). Exploring students’ computational thinking for mathematical problem-solving: A case study. Proceedings of the 18th International Conference on Cognition and Exploratory
Hansson, S. O. (2020). Technology and mathematics. Philosophy & Technology, 33(1), 117–139. https://doi.org/10.1007/s13347-019-00348-9
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/10.1016/j.compedu.2022.104657
Hsu, T.-C., & Chen, M.-S. (2025). Effects of students using different learning approaches for learning computational thinking and AI applications. Education and Information Technologies, 30, 7549–7571. https://doi.org/10.1007/s10639-024-13116-w
Huang X., Qiao C. (2022). Enhancing computational thinking skills through artificial intelligence education at a STEAM high school. Science & Education, 33, 383–403. https://doi.org/10.1007/s11191-022-00392-6
Hurt, T., Greenwald, E., Allan, S., Orona, C., Ryoo, K., & Wilkerson, M. (2023). The computational thinking for science (CT-S) framework: Operationalizing CT-S for K–12 science education researchers and educators. International Journal of STEM Education, 10(1), Article 1. https://doi.org/10.1186/s40594-022-00391-7
Gadanidis, G. (2016). Artificial intelligence, computational thinking, and mathematics education. In Proceedings of ICICTE 2016 (pp. [insert page numbers if known]). Western University, Canada. Retrieved from, http://www.icicte.org/Papers_ICICTE2016/3.1%20013_Gadanidis-edit%20.pdf
GeeksForGeeks. (2024, April 2). Math algorithms. https://www.geeksforgeeks.org/math-algorithms/
Guevara‑Reyes, J., Vinueza‑Morales, M., Ruano‑Lara, E., & Vidal‑Silva, C. (2025). Implementation of personalized frameworks in computational thinking development: Implications for teaching in software engineering. Frontiers in Education, 10, Article 1584040. https://doi.org/10.3389/feduc.2025.1584040
Langreo, L. (2023, December 8). Digital distractions in class linked to lower academic performance. Education Week. Learning in the Digital Age (CELDA 2021), 79–86. International Association for Development of the Information Society (IADIS). https://files.eric.ed.gov/fulltext/ED621547.pdf
Lee, H. S., & Hollebrands, K. F. (2006). Students’ use of technological features while solving a mathematics problem. The Journal of Mathematical Behavior, 25(3), 252–266. https://doi.org/10.1016/j.jmathb.2006.09.005
Lee, S. (2025, May 17). Guide to function decomposition in pre-calc: Step-by-step methods for breaking down functions. Number Analytics. https://www.numberanalytics.com/blog/function-decomposition-pre-calc-guide
Lenzen, W. (2018). Leibniz and the calculus ratiocinator. Technology and Mathematics: Philosophical and Historical Investigations, 47-78.
LibreTexts. (n.d.). Recognizing patterns.https://math.libretexts.org/Courses/Coalinga_College/Math_for_Educators_(MATH_010A_and_010B_CID120)/05%3A_Problem_Solving/5.06%3A_Recognizing_Patterns
Luckin, R. (2017). Towards artificial intelligence-based assessment systems. Nature Human Behavior, 1(3), Article 0028. https://doi.org/10.1038/s41562-016-0028
Kellman, P. J., Massey, C. M., & Son, J. Y. (2010). Perceptual learning modules in mathematics: Enhancing students’ pattern recognition, structure extraction, and fluency. Topics in Cognitive Science, 2(2), 285–305. https://doi.org/10.1111/j.1756-8765.2009.01090.x
KLEMM, F. (1976). Die Rolle der Mathematik in der Technik des 19. Jahrhunderts Le rôle des mathématiques dans la technique au XIXsiècle. Neunzehntes Jahrhundert. Forschungsunternehmen der Fritz Thyssen Stiftung. Studien zur Naturwissenschaft, Technik und Wirtschaft im Neunzehnten Jahrhundert, 3, 736-755.
Koehler, T., & Sammon, J. (2023, June 16). How generative AI can support research-based math instruction. Edutopia. https://www.edutopia.org/article/using-ai-math-instruction/
Kotsopoulos, D., Floyd, L., Nelson, V., & Makosz, S. (2019). Mathematical or computational thinking? An early year’s perspective. In D. Kotsopoulos (Ed.), Mathematical learning and cognition in early childhood (pp. 79–90). Springer. https://doi.org/10.1007/978-3-030-12895-1_6
Kumar, N. B., & George, M. M. (2024). Artificial intelligence in mathematics education: A comprehensive review. International Journal of Creative Research Thoughts. https://www.ijcrt.org/papers/IJCRT2408054.pdf
KylePearce3. (2017, December 3). Abstraction – Counting and quantity. https://mathisvisual.com/abstraction/
Manohar, H., & Viswanathappa, G. (2025, June 13). Exploring the role of enhancing computational thinking skills in mathematics education: A systematic literature review. International Journal for Creative Research Thoughts (IJCRT). https://ijcrt.org/download
Math Teacher Coach. (n.d.). Composition and decomposition of polygons. https://mathteachercoach.com/composition-and-decomposition-of-polygons/
Mathnasium. (2023, March 20). The importance of math in science and technology. https://www.mathnasium.com/ca/math-centres/cambridge/news/3420230320-the-importance-of-math-in-science-and-technology
Meriden Public Schools. (n.d.). Curriculum and instructional technology. https://www.meridenk12.org/departments/curriculum-and-instructional-technology/
Mitchelmore, M., & White, P. (2004). Abstraction in mathematics and mathematics learning. In M. J. Høines & A. B. Fuglestad (Eds.), Proceedings of the 28th Conference of the International Group for the Psychology of Mathematics Education (Vol. 3, pp. 329–336). Bergen University College. https://www.emis.de/proceedings/PME28/RR/RR031_Mitchelmore.pdf
Munahefi, D. N., Kartono, Waluya, B., & Dwijanto. (2020). Analysis of creative thinking processes based onmetacognitive with project work models. In the International Conference on Science and Education and Technology (ISET 2019) (Vol. 443, pp. 180–185). Atlantis Press. https://doi.org/10.2991/assehr.k.200620.035
Mutambara, D., & Bayaga, A. (2020). Rural-based Science, Technology, Engineering and Mathematics teachers’ and learners’ acceptance of mobile learning. South African Journal of Information Management, 22(1), Article a1200. https://doi.org/10.4102/sajim.v22i1.1200
National Council of Teachers of Mathematics. (n.d.). Artificial intelligence and mathematics teaching. https://www.nctm.org/standards-and-positions/Position-Statements/Artificial-Intelligence-and-Mathematics-Teaching/
Noonoo, S. (2019, May 21). Computational thinking is critical thinking. And it works in any subject. EdSurge. https://www.edsurge.com/news/2019-05-21-computational-thinking-is-critical-thinking-and-it-works-in-any-subject
Nordby, S. K., Bjerke, A. H., & Mifsud, L. (2022). Computational thinking in the primary mathematics classroom: A systematic review. Digital Experiences in Mathematics Education, 8(1), 27–49. https://doi.org/10.1007/s40751-022-00102-5
NoteGPT. (2025). 10 best websites for math homework assistance in 2025. NoteGPT. https://notegpt.io/blog/10-Best-Websites-for-Math-Homework-Assistance-in-2025Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. Basic Books
Pauli, S. (n.d.). Definition of an algorithm. Retrieved fromhttps://math-sites.uncg.edu/sites/pauli/112/HTML/secalgdef.html
Peters, A. K., Capilla, R., Coroamă, V. C., Heldal, R., Lago, P., Leifler, O., et al. (2024). Sustainability in computing education: A systematic literature review. ACM Transactions on Computing Education, 24(1), 1–13. https://doi.org/10.1145/3639060
Pfeiffer, A., Bezzina, S., Dingli, A., Wernbacher, T., Denk, N., & Fleischhacker, M. (2021, March 8–9). Adaptive learning and assessment: From the teachers’ perspective. In INTED2021 Proceedings (pp. 375–379). IATED. https://doi.org/10.21125/inted.2021.0103
Pllana, D., Baez, R., Sanchez, H., & Sandeep, N. (2024). Technology Inspires Mathematical Creativity in High School. Futurity Education, 4(3), 309–333. https://doi.org/10.57125/FED.2024.09.25.18
Pllana, D. (2024). The Presence of Mathematical Tools in STEM: A Mixed Method Study (Order No. 32116382). (3222014595). https://draweb.njcu.edu/login?url=https://www.proquest.com/dissertations-theses/presence-mathematical-tools-stem-mixed-method/docview/3222014595/se-2
Pllana, Duli, "Informal Learning - Watching Educational YouTube Videos" (2022). UBT International Conference. 300. https://knowledgecenter.ubt-uni.net/conference/2022/all-events/300
Pllana, D. (2021). Explore a real cartoon example. International Journal of Inspired Education, Science and Technology (IJIEST), 3(1), 8–20. Music Academy “Studio Musica”. http://www.stsir.org/Journals/IJIEST/publications/Vol_3_1_Papers/IJIEST_Paper_2.pdf
Rocha, H. (2020). Graphical representation of functions using technology: A window to teacher knowledge. Teaching Mathematics and Its Applications: An International Journal of the IMA, 39(2), 105–126. https://doi.org/10.1093/teamat/hrz011
Román-González, M., Pérez-González, J., & Jiménez-Fernández, C. (2017). Which cognitive abilities underlie computational thinking? Criterion validity of the Computational Thinking Test. Computers in Human Behavior, 72, 678–691. https://doi.org/10.1016/j.chb.2016.08.047
Russell, D. (2018, December 21). Algorithms in mathematics and beyond: Are we living in the age of algorithms? ThoughtCo. https://www.thoughtco.com/definition-of-algorithm-2312354
Sintema, E. J., & Mosimege, M. (2023). High school students’ beliefs about mathematical problem solving: A cluster analysis. Mathematics Teaching Research Journal, 15(1), 68–87. https://files.eric.ed.gov/fulltext/EJ1391467.pdf
Sidoti, O., Park, E., & Gottfried, J. (2025, January 15). About a quarter of U.S. teens have used ChatGPT for schoolwork–double the share in 2023. Pew Research Center. Retrieved from https://www.pewresearch.org/short-reads/2025/01/15/about-a-quarter-of-us-teens-have-used-chatgpt-for-schoolwork-double-the-share-in-2023/
SplashLearn. (n.d.). What is an algorithm in math? Retrieved from https://www.splashlearn.com/math-vocabulary/algebra/algorithm
Structured Parallel Programming. (2012). Geometric decomposition. In ScienceDirect Topics: Computer Science. https://www.sciencedirect.com/topics/computer-science/geometric-decomposition
Stylianides, A. L. (2007). Proof and proving in school mathematics. Journal for Research in Mathematics Education, 38(3), 289–321. https://pubs.nctm.org/view/journals/jrme/38/3/article-p289.xml
University of Cincinnati CECH Library. (n.d.). AI tools for education. Retrieved June 28, 2025, fromhttps://guides.libraries.uc.edu/ai-education/msa
U.S. Department of Education. (2023). Artificial intelligence and the future of teaching and learning: Insights and recommendations (M. A. Cardona, R. J. Rodríguez, & K. Ishmael, Eds.). Office of Educational Technology. https://www.ed.gov/sites/ed/files/documents/ai-report/ai-report.pdf
Tsortanidou, X., Daradoumis, T., & Barberá, E. (2021). A K-6 computational thinking curricular framework:pedagogical implications for teaching practice. Interactive Learning Environments, 1-21. https://doi.org/10.1080/10494820.2021.1986725
Wagan, A. A., Khan, A. A., Chen, Y.-L., Chen, P. L., Yang, J., & Lagfari, A. A. (2023). Experience: A novel and secure framework (B-AIQoE). Sustainability, 15(6), 5362. https://doi.org/10.3390/su15065362
Wang, F., Ni, X., Zhang, M., & Zhang, J. (2024). Educational digital inequality: A meta-analysis of the relationship between digital device use and academic performance in adolescents. Computers & Education, 213, 105003. https://doi.org/10.1016/j.compedu.2024.105003
Weng, X., Ye, H., & Ng, O.-L. (2024). Integrating artificial intelligence and computational thinking in educational contexts: A systematic review of instructional design and student learning outcomes. Journal of Educational Computing Research, 62(6). https://doi.org/10.1177/07356331241248686
Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society A, 366(1881), 3717-3725.
Wing, J. (2011). Research notebook: Computational thinking—What and why. The Link Magazine, 6, 20–23. Retrieved from, https://openlab.bmcc.cuny.edu/edu-211-b18l-spring-2024-j-longley/wp-content/uploads/sites/3085/2023/06/CT-What-And-Why-copy.pdf
Wolfram, S. (2023, March 23). ChatGPT gets its “Wolfram superpowers”! Stephen Wolfram Writings. https://writings.stephenwolfram.com/2023/03/chatgpt-gets-its-wolfram-superpowers/
Wong, K. W., Ma, X., Dillenbourg, P., & Huan, J. (2020). Broadening artificial intelligence education in K–12: Where to start? ACM Inroads, 11(1), 20–29. https://doi.org/10.1145/3381884
Wu, W., & Yang, K. (2022). The relationships between computational and mathematical thinking: A review study on tasks. Cogent Education, 9(1), 1–19. https://doi.org/10.1080/23386X.2022.2098929
Yalalov, D. (2023, February 6). Lang Chain: How to combine ChatGPT and Wolfram Alpha to get more accurate and detailed answers. Opinion Technology. https://mpost.io/langchain-how-to-combine-chatgpt-and-wolfram-alfa-to-get-more-accurate-and-detailed-answers/
Ye, H., Liang, B., Ng, O., & Chai, C. (2023). Integration of computational thinking in K–12 mathematics education: Asystematic review on CT-based mathematics instruction and student learning. International Journal of STEM Education, 10(1), Article 3, 1–26. https://doi.org/10.1186/s40594-023-00396-0
Yeni, S., Nijenhuis-Voogt, J., Saeli, M., Barendsen, E., & Hermans, F. (2024). Computational thinking integrated inschool subjects – A cross-case analysis of students’ experiences. International Journal of Child-Computer Interaction, 38, 100696. https://doi.org/10.1016/j.ijcci.2024.100696
Zainudin, R., Zulnaidi, H., Mafarja, N., & Mohamed Kamali, M. Z. (2025). Development and validation of My Online Teaching with GeoGebra (MyOT_G+) kit on mathematical reasoning among pre-university students. Contemporary Educational Technology, 17(2), ep569. https://doi.org/10.30935/cedtech/15989
Zimmer, S. (2022). Algorithm (mathematics) [Unpublished manuscript]. Google Docs. https://docs.google.com/document/d/1TBZc9Sc8savOTRGrKu7GmcNSgegHEh1qht6WNlXyu74/edit
Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., Liu, J.-B., Yuan, J., & Li, Y. (2021). A review of artificial intelligence (AI) in education from 2010 to 2020. Education Research International, 2021, Article 8812542. https://doi.org/10.1155/2021/8812542
Downloads
Published
Issue
Section
License
Copyright (c) 2026 International Journal of Education in Mathematics, Science and Technology
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Articles may be used for research, teaching, and private study purposes. Authors alone are responsible for the contents of their articles. The journal owns the copyright of the articles. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of the research material.
The author(s) of a manuscript agree that if the manuscript is accepted for publication in the journal, the published article will be copyrighted using a Creative Commons “Attribution 4.0 International” license. This license allows others to freely copy, distribute, and display the copyrighted work, and derivative works based upon it, under certain specified conditions.
Authors are responsible for obtaining written permission to include any images or artwork for which they do not hold copyright in their articles, or to adapt any such images or artwork for inclusion in their articles. The copyright holder must be made explicitly aware that the image(s) or artwork will be made freely available online as part of the article under a Creative Commons “Attribution 4.0 International” license.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
