Beyond Clickers: An AI-Powered Student Response System for Capturing Mathematical Reasoning in Indonesian Mathematics Classrooms

Authors

  • Fiki Rahmita UIN Kiai Ageng Muhammad Besari Ponorogo, Indonesia
  • Amanah Khairiyah Kementerian Keuangan RI, Indonesia

DOI:

https://doi.org/10.33394/jp.v13i4.22758

Keywords:

Artificial intelligence, Formative assessment, Mathematical reasoning, Student response systems

Abstract

This study aims to examine the design and pedagogical logic of the Si•Xiang•Hui AI Interactive Classroom, an AI-powered student response system developed at Zhejiang Normal University, China, and to explore its potential implications for Indonesian mathematics education. The study employed a qualitative descriptive-analytic case study design. The case involved a hands-on demonstration of the system during a seminar for education administrators from Southeast Asia held at Zhejiang Normal University in September 2026. The system developer served as the key informant, while 34 adult participants, including the two authors, participated as participant observers. Data were collected through participant observation, an audio recording of the session, the developer’s presentation slides, the delegation’s activity report, and relevant policy documents. The data were analyzed using reflexive thematic analysis with a deductive orientation informed by the ICAP framework, formative assessment, and knowledge building, with triangulation conducted across data sources. The findings indicate that the system extends beyond conventional clickers in four ways: it pairs answer choices with spoken explanations; enables every student to pose questions that can be addressed through human–AI collaboration; assigns higher scores to explanations based on their quality (up to ten points, compared with one point for an answer choice); and provides analytics intended to classify responses according to Bloom’s Taxonomy and SOLO taxonomy levels. Two areas for further development were identified: enabling students to revise their recorded arguments and making teacher verification of AI-generated answers and scores an explicit step in the assessment process. The findings suggest that the pedagogical logic underlying the system, rather than the technology itself, can be adapted to Indonesian mathematics classrooms using low-cost tools, provided that teachers receive adequate preparation, scoring criteria incorporate mathematical correctness, and student data are appropriately protected.

References

Almarashdi, H. S., Jarrah, A. M., Abu Khurma, O., & Gningue, S. M. (2024). Unveiling the potential: A systematic review of ChatGPT in transforming mathematics teaching and learning. Eurasia Journal of Mathematics, Science and Technology Education, 20(12), em2555. https://doi.org/10.29333/ejmste/15739

Awang, L. A., Yusop, F. D., & Danaee, M. (2025). Current practices and future direction of artificial intelligence in mathematics education: A systematic review. International Electronic Journal of Mathematics Education, 20(2), em0823. https://doi.org/10.29333/iejme/16006

Bai, S., Hew, K. F., & Huang, B. (2020). Does gamification improve student learning outcome? Evidence from a meta-analysis and synthesis of qualitative data in educational contexts. Educational Research Review, 30, 100322. https://doi.org/10.1016/j.edurev.2020.100322

Biggs, J. B., & Collis, K. F. (1982). Evaluating the quality of learning: The SOLO taxonomy (Structure of the Observed Learning Outcome). Academic Press.

Black, P., & Wiliam, D. (2018). Classroom assessment and pedagogy. Assessment in Education: Principles, Policy & Practice, 25(6), 551–575. https://doi.org/10.1080/0969594X.2018.1441807

Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589–597. https://doi.org/10.1080/2159676X.2019.1628806

Carter, N., Bryant-Lukosius, D., DiCenso, A., Blythe, J., & Neville, A. J. (2014). The use of triangulation in qualitative research. Oncology Nursing Forum, 41(5), 545–547. https://doi.org/10.1188/14.ONF.545-547

Chen, B., & Hong, H.-Y. (2016). Schools as knowledge-building organizations: Thirty years of design research. Educational Psychologist, 51(2), 266–288. https://doi.org/10.1080/00461520.2016.1175306

Chi, M. T. H., Adams, J., Bogusch, E. B., Bruchok, C., Kang, S., Lancaster, M., Levy, R., Li, N., McEldoon, K. L., Stump, G. S., Wylie, R., Xu, D., & Yaghmourian, D. L. (2018). Translating the ICAP theory of cognitive engagement into practice. Cognitive Science, 42(6), 1777–1832. https://doi.org/10.1111/cogs.12626

Holstein, K., McLaren, B. M., & Aleven, V. (2019). Co-designing a real-time classroom orchestration tool to support teacher–AI complementarity. Journal of Learning Analytics, 6(2), 27–52. https://doi.org/10.18608/jla.2019.62.3

Järvelä, S., Nguyen, A., & Hadwin, A. F. (2023). Human and artificial intelligence collaboration for socially shared regulation in learning. British Journal of Educational Technology, 54(5), 1057–1076. https://doi.org/10.1111/bjet.13325

Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274

Knox, J. (2020). Artificial intelligence and education in China. Learning, Media and Technology, 45(3), 298–311. https://doi.org/10.1080/17439884.2020.1754236

Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. SAGE Publications.

Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B.-P. T. (2023). Ethical principles for artificial intelligence in education. Education and Information Technologies, 28(4), 4221–4241. https://doi.org/10.1007/s10639-022-11316-w

OECD. (2023). PISA 2022 results (Volume I): The state of learning and equity in education. OECD Publishing. https://doi.org/10.1787/53f23881-en

Opesemowo, O. A. G., & Adewuyi, H. O. (2024). A systematic review of artificial intelligence in mathematics education: The emergence of 4IR. Eurasia Journal of Mathematics, Science and Technology Education, 20(7), em2478. https://doi.org/10.29333/ejmste/14762

Republic of Indonesia. (2022). Undang-Undang Republik Indonesia Nomor 27 Tahun 2022 tentang Pelindungan Data Pribadi [Law of the Republic of Indonesia Number 27 of 2022 on Personal Data Protection]. https://peraturan.bpk.go.id/Details/229798

Rittle-Johnson, B., Loehr, A. M., & Durkin, K. (2017). Promoting self-explanation to improve mathematics learning: A meta-analysis and instructional design principles. ZDM, 49(4), 599–611. https://doi.org/10.1007/s11858-017-0834-z

Roell, M., Viarouge, A., Houdé, O., & Borst, G. (2017). Inhibitory control and decimal number comparison in school-aged children. PLOS ONE, 12(11), e0188276. https://doi.org/10.1371/journal.pone.0188276

Schell, J. A., & Butler, A. C. (2018). Insights from the science of learning can inform evidence-based implementation of peer instruction. Frontiers in Education, 3, 33. https://doi.org/10.3389/feduc.2018.00033

Selwyn, N. (2019). What’s the problem with learning analytics? Journal of Learning Analytics, 6(3), 11–19. https://doi.org/10.18608/jla.2019.63.3

Serrada-Sotil, J., Huertas Martínez, J. A., & Granado-Peinado, M. (2025). Do audience response systems truly enhance learning and motivation in higher education? A systematic review. Humanities and Social Sciences Communications, 12(1), 1767. https://doi.org/10.1057/s41599-025-06042-w

Sutrisno AB, J., Pratama, E. Y., Putra R, A., Nasution, S. H., & Lestyanto, L. M. (2025). Students’ mathematical justification abilities in analyzing ChatGPT’s answers. Infinity Journal, 14(2), 445–460. https://doi.org/10.22460/infinity.v14i2.p445-460

Turmuzi, M., Azmi, S., & Kertiyani, N. M. I. (2026). ChatGPT in school mathematics education: A systematic review of opportunities, challenges, and pedagogical implications. Teaching and Teacher Education, 170, 105286. https://doi.org/10.1016/j.tate.2025.105286

Wardat, Y., Tashtoush, M. A., AlAli, R., & Jarrah, A. M. (2023). ChatGPT: A revolutionary tool for teaching and learning mathematics. Eurasia Journal of Mathematics, Science and Technology Education, 19(7), em2286. https://doi.org/10.29333/ejmste/13272

Wood, R., & Shirazi, S. (2020). A systematic review of audience response systems for teaching and learning in higher education: The student experience. Computers & Education, 153, 103896. https://doi.org/10.1016/j.compedu.2020.103896

Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed.). SAGE Publications.

Zawacki-Richter, O., Marín, V. I., 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), 39. https://doi.org/10.1186/s41239-019-0171-0

Zhai, X., Yin, Y., Pellegrino, J. W., Haudek, K. C., & Shi, L. (2020). Applying machine learning in science assessment: A systematic review. Studies in Science Education, 56(1), 111–151. https://doi.org/10.1080/03057267.2020.1735757

Zhang, L. (2026, September 20). AI-empowered optimization and innovation in classroom teaching: A new model of “Teacher–AI–Student” human–machine collaborative teaching and learning [Conference presentation]. Seminar for Education Administrators from Southeast Asia, Zhejiang Normal University, Jinhua, China.

Published

2026-10-09

How to Cite

Rahmita, F., & Khairiyah, A. (2026). Beyond Clickers: An AI-Powered Student Response System for Capturing Mathematical Reasoning in Indonesian Mathematics Classrooms. Jurnal Paedagogy, 13(4). https://doi.org/10.33394/jp.v13i4.22758

Citation Check