Transformasi Asesmen Matematika pada Era Kecerdasan Buatan: Sebuah Systematic Literature Review tentang Praktik Asesmen, Umpan Balik, Learning Analytics, dan Arah Penelitian Masa Depan
DOI:
10.29303/jm.v8i3.12725Published:
2026-09-30Abstract
The rapid advancement of Artificial Intelligence (AI) has significantly transformed mathematics assessment by enabling adaptive assessment, automated feedback, learning analytics, and data-driven instructional decision-making. This study aimed to synthesize current evidence regarding AI-based mathematics assessment, feedback practices, learning analytics, and future research directions. A Systematic Literature Review (SLR) was conducted following the PRISMA 2020 guidelines using the Scopus database. The search covered publications from 2015 to 2026 with the query related to mathematics education, generative AI, artificial intelligence, assessment, and feedback. After applying inclusion and exclusion criteria, 33 articles were selected for analysis. The findings indicate that AI has shifted mathematics assessment from traditional summative evaluation toward adaptive, formative, and personalized assessment. AI-generated feedback and learning analytics improve students' problem-solving skills, conceptual understanding, and teachers' instructional decisions. Nevertheless, issues concerning validity, ethical use, data privacy, and teachers' AI literacy remain major challenges. Future studies should develop ethical, explainable, and human-centered AI assessment frameworks that strengthen mathematics learning while preserving pedagogical quality.
Keywords:
Artificial Intelligence Asesmen Matematika Learning Analytics Umpan Balik Systematic Literature ReviewReferences
Abosaooda, H. N., Ariffin, S. B., & Alyasiri, O. M. (2026). A survey on leveraging artificial intelligence tools for enhancing advanced mathematical education and problem-solving. IAES International Journal of Artificial Intelligence, 15(1), 76–85. https://doi.org/10.11591/ijai.v15.i1.pp76-85
Chan, K. W., Ali, F., Park, J., Sham, K. S. B., Tan, E. Y. T., Chong, F. W. C., Qian, K., & Sze, G. K. (2025). Automatic item generation in various STEM subjects using large language model prompting. Computers and Education: Artificial Intelligence, 8. https://doi.org/10.1016/j.caeai.2024.100344
Divjak, B., Svetec, B., & Horvat, D. (2026). Generative AI in mathematics education: Analysing student performance and perceptions over three academic years. International Journal of Technology Enhanced Learning, 18(1), 16–39. https://doi.org/10.1504/IJTEL.2026.150520
Forbus, K. D., Garnier, B., Tikoff, B., Marko, W., Usher, M., & McLure, M. (2020). Sketch worksheets in science, technology, engineering, and mathematics classrooms: Two deployments. AI Magazine, 41(1), 19–32. https://doi.org/10.1609/aimag.v41i1.5189
Hu, M., Guo, R., Li, H., & Li, C. (2026). The alignment of knowledge focus during teaching preparation and AI-powered learning-by-teaching and their effects on mathematics learning outcomes in the context of high-stakes state assessments. Computers and Education, 251. https://doi.org/10.1016/j.compedu.2026.105642
Huda, N., Anwar, K., & Kurniawan, W. (2025). ChatGPT scaffolding in supporting metacognition for limit concepts in guided inquiry mathematics learning. Journal of Information Technology Education: Research, 24. https://doi.org/10.28945/5645
Karaca, H. (2026). AI as an evaluation instrument: Examining handwritten solutions in the context of mathematical representations. RIED-Revista Iberoamericana de Educación a Distancia, 29(2), 1–24. https://doi.org/10.5944/ried.47084
Kassanew, B., Weng, C., Astatke, M., & Kassanew, Y. (2026). The impact of AI-supported Think-Pair-Share instruction on students’ problem-solving skills and motivation in learning quadratic equations: A social constructivist perspective. Journal of Computer Assisted Learning, 42(4). https://doi.org/10.1002/jcal.70271
Khurramov, A. J., Axmedshaeva, M. A., Mukhitdinova, F. A., Xudayberdiyeva, G. A., Almosova, S. S., Makhamatov, M. M., & Khayitov, S. R. (2025). Artificial intelligence in education: Analysis and assessment of legal knowledge using AI tools. Qubahan Academic Journal, 5(3), 264–293. https://doi.org/10.48161/qaj.v5n3a2022
Kuo, B.-C., Bai, Z.-E., & Lin, C.-H. (2026). Developing an AI learning companion for mathematics problem solving in elementary schools. Computers and Education, 240. https://doi.org/10.1016/j.compedu.2025.105463
Lim, K. Y. T., Le, N. T. M., & Chanoudam, S. (2026). Automating spatial visualisation of handwritten vector equations using large vision models in pre-tertiary mathematics. Multimodal Technologies and Interaction, 10(6). https://doi.org/10.3390/mti10060068
Liu, J., Keane, T., Sun, D., Yang, Y., & Yang, Y. (2025). Design and evaluation of ChatGPT-MWPS: An AI-enhanced learning system for improving primary students’ mathematical word problem solving. Smart Learning Environments, 12(1). https://doi.org/10.1186/s40561-025-00419-9
Liu, J., Sun, D., Sun, J., Wang, J., & Yu, P. L. H. (2025). Designing a generative AI enabled learning environment for mathematics word problem solving in primary schools: Learning performance, attitudes and interaction. Computers and Education: Artificial Intelligence, 9. https://doi.org/10.1016/j.caeai.2025.100438
López-Pernas, S., Misiejuk, K., & Saqr, M. (2026). How AI-generated feedback hinders or helps learning: A heterogeneous TNA study of learning dynamics. Journal of Computer Assisted Learning, 42(4). https://doi.org/10.1002/jcal.70285
Nguyen, T. Q. H., Tran, T. L., Trinh, Q. H., & Ngo, T. D. (2026). Using technology to support children with dyscalculia in pre-primary and primary school: Insights, challenges and opportunities. Disability and Rehabilitation: Assistive Technology, 21(4), 1300–1322. https://doi.org/10.1080/17483107.2026.2658104
Oh, S. (2025). Integration of MATH41 and generative AI in pre-service mathematics teacher education: An empirical study on lesson design competency. IEEE Access, 13, 128959–128973. https://doi.org/10.1109/ACCESS.2025.3586593
Ramprakash, B., Surya Devi, B., Nithyakala, G., Bhumika, K., & Avanthika, S. (2024). Comparing traditional instructional methods to ChatGPT: A comprehensive analysis. Journal of Engineering Education Transformations, 37(Special Issue 2), 612–620. https://doi.org/10.16920/jeet/2024/v37is2/24095
Rücker, C. R., & Becker-Genschow, S. (2025). Enhancing enthusiasm for STEM education with AI: Domain-specific chatbot as personalized learning assistant. Computers and Education Open, 9. https://doi.org/10.1016/j.caeo.2025.100315
Rutatola, E. P., Stroeken, K., & Belpaeme, T. (2025). “Habari, Colleague!”: A qualitative exploration of the perceptions of primary school mathematics teachers in Tanzania regarding the use of social robots. Applied Sciences, 15(15). https://doi.org/10.3390/app15158483
Stamenkova, R. (2025). Large language models: A tool for solving mathematical problems in high school. Annual of Sofia University "St. Kliment Ohridski" Faculty of Mathematics and Informatics, 112, 165–183. https://doi.org/10.60063/gsu.fmi.112.165-183
Tai, P., & Hieu, B. T. (2018). Interaction networks: Data-driven models for mathematics education in intelligent tutoring systems. International Journal of Mechanical Engineering and Technology, 9, 1173–1184.
Thuy, P. T. D., Van, N. T., Trang, N. H., Thuong, N. M., Hien, V. T. T., & Chung, V. Q. (2025). Designing an AI-supported formative assessment model for pre-service mathematics teacher self-study in Vietnam. International Journal of Interactive Mobile Technologies, 19(22), 50–68. https://doi.org/10.3991/ijim.v19i22.57723
Torres-Peña, R. C., Peña-González, D., Chacuto-López, E., Ariza, E. A., & Vergara, D. (2024). Updating calculus teaching with AI: A classroom experience. Education Sciences, 14(9). https://doi.org/10.3390/educsci14091019
Walkington, C. (2025). The implications of generative artificial intelligence for mathematics education. School Science and Mathematics. https://doi.org/10.1111/ssm.18356
Wu, S., Xu, J., & Feng, S. (2026). Opportunities and challenges of generative AI in STEAM education: A thematic co-occurrence network analysis of primary and secondary school leaders’ perspectives. Technology, Knowledge and Learning. https://doi.org/10.1007/s10758-026-09976-5
Zhu, F., Liang, Q., Mao, Z., & Wang,Y. (2026). Improving instructional design proficiency of master’s students in mathematics education through intelligent educational technologies. Computers and Education: Artificial Intelligence, 10. https://doi.org/10.1016/j.caeai.2026.100597
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