Analysis of Abstraction and Algorithmic Thinking Skills in Solving Realistic Mathematics Problems: Students versus Artificial Intelligence
DOI:
10.29303/jm.v8i2.12333Published:
2026-06-24Downloads
Abstract
Artificial Intelligence (AI) in education not only offers significant potential for facilitating access to information but also presents challenges to students’ independence and thinking processes. Problem-solving is a fundamental cognitive skill that requires abstraction and algorithmic thinking. Preliminary analysis revealed that 22 out of 25 students experienced difficulties in identifying relevant information and designing algorithms to solve realistic mathematical problems. These students made errors in simplifying problems and formulating strategic solution steps despite using AI. This qualitative study aims to describe the errors made by students in the processes of abstraction and algorithmic thinking. In addition, students’ solutions were compared with AI-generated outputs to identify their error characteristics. The study involved three students and two AI models (ChatGPT and Gemini) as subjects. Data were collected through realistic problem-solving tests and interviews. The findings indicate that students remain vulnerable to both conceptual and procedural errors, whereas AI predominantly exhibits conceptual errors in the abstraction process. These findings highlight the need to enhance students’ abstraction and algorithmic thinking skills through more effective instructional approaches. Although AI has the potential to support simulation and analysis, it cannot fully replace students’ thinking abilities; therefore, students’ confidence in solving problems should also be strengthened
Keywords:
Abstraction Algorithmic Thinking Realistic Mathematics Artificial IntelligenceReferences
Anil, R., Borgeaud, S., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., Millican, K., Silver, D., Johnson, M., Antonoglou, I., Schrittwieser, J., Glaese, A., Chen, J., Pitler, E., Lillicrap, T., Lazaridou, A., Firat, O., … Vinyals, O. (2025). Gemini: A Family of Highly Capable Multimodal Models (arXiv:2312.11805). arXiv. https://doi.org/10.48550/arXiv.2312.11805
Assaf, D., Adorni, G., Lutz, E., Negrini, L., Piatti, A., Mondada, F., Mangili, F., & Gambardella, L. M. (2024). The CTSkills App—Measuring Problem Decomposition Skills of Students in Computational Thinking (arXiv:2411.14945). arXiv. https://doi.org/10.48550/arXiv.2411.14945
Bartsch, H., Jorgensen, O., Rosati, D., Hoelscher-Obermaier, J., & Pfau, J. (2023). Self-Consistency of Large Language Models under Ambiguity (arXiv:2310.13439). arXiv. https://doi.org/10.48550/arXiv.2310.13439
Bushuyev, S., Bushuyeva, N., Murzabekova, S., Khusainova, M., & Saidullayev, R. (2024). Transformation of the Education Landscape in an AI Environment. Proceedings of the 12th IPMA Research Conference “Project Management in the Age of Artificial Intelligence,” 57–64. https://doi.org/10.56889/strd5315
Candraningtyas, S. R., & Khusna, H. (2023). Computational thinking ability becomes a predictor of mathematical critical thinking ability. Alifmatika: Jurnal Pendidikan Dan Pembelajaran Matematika, 5(2), 247–263. https://doi.org/10.35316/alifmatika.2023.v5i2.247-263
Dainys, A. (2024). Human Creativity versus Machine Creativity: Will Humans Be Surpassed by AI? Dalam Contemporaneous Issues about Creativity. IntechOpen. https://doi.org/10.5772/intechopen.1007369
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
Dorner, C., Ableitinger, C., & Krammer, G. (2026). Revealing the nature of mathematical procedural knowledge by analysing students’ deficiencies and errors. International Journal of Mathematical Education in Science and Technology, 57(3), 413–434. https://doi.org/10.1080/0020739X.2024.2445666
Fauzi, A. L., Kusumah, Y. S., Nurlaelah, E., & Juandi, D. (2024). Computational Thinking in Mathematics Education: A Systematic Literature Review on its Implementation and Impact on Students’ Learning. Jurnal Kependidikan : Jurnal Hasil Penelitian Dan Kajian Kepustakaan Di Bidang Pendidikan, Pengajaran, Dan Pembelajaran, 10(2), 640–653. https://doi.org/10.33394/jk.v10i2.11140
Gadanidis, G. (2017). Artificial intelligence, computational thinking, and mathematics education. International Journal of Information and Learning Technology, 34, 133–139. https://doi.org/10.1108/IJILT-09-2016-0048
Gasaymeh, A., & AlMohtadi, R. (2024). College of education students’ perceptions of their computational thinking proficiency. Frontiers in Education, 9, 1478666. https://doi.org/10.3389/feduc.2024.1478666
Hashana, A. M. J., Brundha, P., Ahamed Ayoobkhan, M. U., & S, F. (2023). Deep Learning in ChatGPT - A Survey. 2023 7th International Conference on Trends in Electronics and Informatics (ICOEI), 1001–1005. https://doi.org/10.1109/ICOEI56765.2023.10125852
Hollister, D. L., Gonzalez, A., & Hollister, J. (2017). Contextual Reasoning in Human Cognition and the Implications for Artificial Intelligence Systems. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-319-57837-8_48
Huang, X., & Quao, C. (2024). Enhancing Computational Thinking Skills Through Artificial Intelligence Education at a STEAM High School. Diambil 7 Juni 2026, dari https://www.researchgate.net/publication/364306092_Enhancing_Computational_Thinking_Skills_Through_Artificial_Intelligence_Education_at_a_STEAM_High_School
Kania, N., & Arifin, Z. (2019). Analisis Kesulitan Calon Guru Sekolah Dasar Dalam Menyelesaikan Soal Pemecahan Masalah Matematis Berdasarkan Prosedur Newman (Brwgs_v1). SocArXiv. https://doi.org/10.31235/osf.io/brwgs
Koerunnisa, K., Rianto, R., Novita, S. D., Nurasih, Z., Abdullah, A., Aliah, A., Fallo, H., & Rasilah, R. (2025). The Influence of a Realistic Mathematical Approach on Student Learning in Elementary Schools. Journal of Mathematics Instruction, Social Research and Opinion, 4(1), 105–114. https://doi.org/10.58421/misro.v4i1.282
Liang, S., Zhang, W., Zhong, T., & Liu, T. (2026). Mathematics and Machine Creativity: A Survey on Bridging Mathematics with AI (arXiv:2412.16543). arXiv. https://doi.org/10.48550/arXiv.2412.16543
Lisa, L., Simamora, E., Mulyono, M., Napitupulu, E. E., & Panjaitan, A. (2024). Newman’s Error Analysis (NEA) in Solving Computational Thinking Problems on Indefinite Integral Material. Tarbawi : Jurnal Ilmu Pendidikan, 20(2), 208–220. https://doi.org/10.32939/tarbawi.v20i2.3822
Lubis, D. D. (2023). Using Digital Learning Media to Increase Student Creativity in Solving Mathematics Problems. EDUCTUM: Journal Research, 2(5), 152–154. https://doi.org/10.56495/ejr.v2i5.462
Luo, B., Lau, R., Li, C., & Si, Y. W. (2021). A critical review of state‐of‐the‐art chatbot designs and applications. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 12. https://doi.org/10.1002/widm.1434
Mane, P. S. (2024). Integrating Artificial Intelligence Techniques with Computational Mathematics for Solving Complex Problems. Panamerican Mathematical Journal, 34(2), 66–77. https://doi.org/10.52783/pmj.v34.i2.923
Manisha Rajesh Gupta. (2024). ChatGPT-A Generative Pre-Trained Transformer. International Journal of Advanced Research in Science, Communication and Technology, 590–595. https://doi.org/10.48175/IJARSCT-15087
Monalisa, M. (2023). Analisis Berpikir Komputasional Siswa SMP pada Kurikulum Merdeka Mata Pelajaran Informatika. DIAJAR: Jurnal Pendidikan Dan Pembelajaran, 2(3), 298–304. https://doi.org/10.54259/diajar.v2i3.1596
Mustafa, A. N. (2024). The future of mathematics education: Adaptive learning technologies and artificial intelligence. International Journal of Science and Research Archive, 12(1), 2594–2599. https://doi.org/10.30574/ijsra.2024.12.1.1134
Oh, S. (2025). Evaluating Mathematical Problem-Solving Abilities of Generative AI Models: Performance Analysis of o1-preview and gpt-4o Using the Korean College Scholastic Ability Test. IEEE Access, 13, 1227–1235. https://doi.org/10.1109/ACCESS.2024.3523703
Prabawanto, S., Herman, T., Melani, R., Samosir, C., & Mefiana, S. (2024). Students’ Difficulties in Understanding Problems and Making Mathematical Models in the Contextual Problem Solving Process (hlm. 796–810). https://doi.org/10.2991/978-94-6463-554-6_66
Pratibha, & Chandra, A. (2024). A Review of use of Artificial Intelligence in Teaching and Learning of Mathematics. IJSAT - International Journal on Science and Technology, 15(4). https://doi.org/10.71097/IJSAT.v15.i4.1190
Putra, Z., Witri, G., Dahnilsyah, Gunawan, Y., Sumadinata, H., & Putri, A. (2024). Prospective Elementary Teachers’ Computational Thinking Skills: A Preliminary Study (hlm. 4). https://doi.org/10.1109/IDICAIEI61867.2024.10842752
Qudwatullathifah, R. N., Ismuwardani, Z., Guntur, M., Musyarrofah, S., & Ningsih, N. I. S. (2023). EFEKTIVITAS PLATFORM PEMBELAJARAN MATEMATIKA BERBASIS DIGITAL UNTUK MENINGKATKAN KEMAMPUAN PEMECAHAN MASALAH SISWA SEKOLAH DASAR. Prima Magistra: Jurnal Ilmiah Kependidikan, 4(4), 590–599. https://doi.org/10.37478/jpm.v4i4.3225
Qurbonova, B., & Yusupova, A. (2024). The Deep Investigation of the Part of AI Integ in The Field of Education: A Technical Review. https://www.researchgate.net/publication/383016425_The_Deep_Investigation_of_the_Part_of_AI_Integ_in_The_Field_of_Education_A_Technical_Review
Rizki, L. M., & Kusumah, Y. S. (2025). Unveiling the Needs: Developing AI-Based Chatbots in Blended Learning to Enhance Computational Thinking and Self-Regulated Learning in Mathematics Education. Diambil 7 Juni 2026, dari https://www.researchgate.net/publication/394725084_Unveiling_the_Needs_Developing_AI-Based_Chatbots_in_Blended_Learning_to_Enhance_Computational_Thinking_and_Self-Regulated_Learning_in_Mathematics_Education
Rojas, E., & Benakli, N. (2020). Mathematical Literacy and Critical Thinking. Dalam J. C. But (Ed.), Teaching College-Level Disciplinary Literacy: Strategies and Practices in STEM and Professional Studies (hlm. 197–226). Springer International Publishing. https://doi.org/10.1007/978-3-030-39804-0_8
Rosydiana, E. A., Sudjimat, D. A., & Utama, C. (2023). The Effect of Digital Learning Media Using Scratch Game Based Learning on Student Problem Solving Skills. Jurnal Penelitian Pendidikan IPA, 9(11), 10010–10015. https://doi.org/10.29303/jppipa.v9i11.4876
Setiawan, H., Handican, R., & Rurisman, R. (2023). Revolutionizing Math Education: Unleashing the Potential of Web-based Learning Media for Enhanced Mathematical Problem Solving Skills. JDIME: Journal of Development and Innovation in Mathematics Education, 1(2), 01–11. https://doi.org/10.32939/jdime.v1i2.2978
Shodikin, A., Ekawati, R., Purnomo, H., & Abdullah, A. H. (2024). Failure in Constructing the Mathematical Model in Real-World Problems. TEM Journal, 3335–3345. https://doi.org/10.18421/TEM134-68
Singh, A. (2023). A Review on Objective-Driven Artificial Intelligence (arXiv:2308.10135). arXiv. https://doi.org/10.48550/arXiv.2308.10135
Singh, B., Kaunert, C., Singh, B., & Kaunert, C. (1M, Januari 1). Computational Thinking for Innovative Solutions and Problem-Solving Techniques: Transforming Conventional Education to Futuristic Interdisciplinary Higher Education [Chapter]. IGI Global Scientific Publishing. (computational-thinking-for-innovative-solutions-and-problem-solving-techniques). Https://Services.Igi-Global.Com/Resolvedoi/Resolve.Aspx?Doi=10.4018/979-8-3693-1974-1.Ch004. https://doi.org/10.4018/979-8-3693-1974-1.ch004
Sumartono, Sumartono, W. A. P., & Rafsanjani, W. A. H. (2024). Transforming Education: The Impact of Artificial Intelligence on Learning and Pedagogical Practices. Proceeding of International Seminar Enrichment of Career by Knowledge of Language and Literature, 12(1), 75–84. https://doi.org/10.25139/eckll.v12i1.9605
Susanti, R. D., & Taufik, M. (2021). Analysis of Student Computational Thinking in Solving Social Statistics Problems: Analysis of Student Computational Thinking in Solving Social Statistics Problems. SJME (Supremum Journal of Mathematics Education), 5(1). https://doi.org/10.35706/sjme.v5i1.4376
Swasto, W., Suanto, E., & Saragih, S. (2024). Analysis of Computational Thinking Ability of High School Students in Solving Statistics problems. PRISMA, 13(2), 269–277. https://doi.org/10.35194/jp.v13i2.4373
Wang, H., & Star, J. R. (2023). Investigating algorithm-oriented flexibility and structure-informed flexibility in mathematics learning. Asian Journal for Mathematics Education, 2(1), 16–41. https://doi.org/10.1177/27527263231163593
Wang, S., & Liu, S. (2024). Exploration of the Cultivation Path of Computational Thinking in Linear Algebra Curriculum. Curriculum and Teaching Methodology, 7(4). https://doi.org/10.23977/curtm.2024.070409
Wu, T., Silitonga, L. M., Dharmawan, B., & Murti, A. T. (2024). Empowering Students to Thrive: The Role of CT and Self-Efficacy in Building Academic Resilience—Ting-Ting Wu, Lusia Maryani Silitonga, Budi Dharmawan, Astrid Tiara Murti, 2024. Diambil 7 Juni 2026, dari https://journals.sagepub.com/doi/10.1177/07356331231225468
Zaman, B. U. (2024). Formalizing Meaning and Knowledge for AGI Development. https://doi.org/10.36227/techrxiv.171779125.54959094/v1
Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: A systematic review. Smart Learning Environments, 11(1), 28. https://doi.org/10.1186/s40561-024-00316-7
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