EMPOWERING ELEMENTARY SCHOOL TEACHERS THROUGH MATHEMATICAL PROBLEM POSING WITH AI AS A REFLECTIVE FEEDBACK TOOL: A COMMUNITY SERVICE PROGRAM IN DEPOK CITY
DOI:
https://doi.org/10.51878/community.v6i2.16346Keywords:
community service; mathematical problem posing; artificial intelligence; reflective feedback; mathematical reasoning; teacher empowermentAbstract
Program pengabdian kepada masyarakat ini menangani tantangan dalam desain instruksional di kalangan guru sekolah dasar di Kota Depok, di mana kemampuan untuk menyusun soal aritmetika yang bersifat komputasional belum mencakup perancangan tugas yang secara matematis autentik dan menuntut kemampuan kognitif tinggi. Inisiatif ini bertujuan untuk mendampingi 18 guru sekolah dasar dalam merancang, mengevaluasi, dan menyempurnakan tugas-tugas matematika melalui siklus penyusunan soal secara partisipatif, dengan memanfaatkan kecerdasan buatan (AI) sebagai mitra untuk memberikan umpan balik reflektif. Evaluasi deskriptif kualitatif dilakukan terhadap perubahan pada artefak tugas, yang mencakup empat soal kelompok awal, revisi kelompok secara kolaboratif, serta sembilan produk individu yang didokumentasikan secara lengkap. Artefak awal yang menjadi tolok ukur terbatas pada soal komputasi satu langkah yang rutin, dengan narasi dangkal atau bahkan tanpa konteks sama sekali. Setelah melalui proses penilaian kolaboratif dengan bantuan AI serta penyempurnaan secara individu, sebagian besar tugas individu yang didokumentasikan berkembang menjadi soal yang melibatkan langkah majemuk atau penalaran analitis dan evaluatif, yang ditempatkan dalam konteks realistis-generik maupun konteks lokal yang spesifik. Akan tetapi, hasil ini mencerminkan peningkatan kualitatif pada artefak tugas yang didokumentasikan, bukan peningkatan kompetensi guru yang terverifikasi secara langsung; hal ini dikarenakan tidak adanya tes awal dan akhir (pre-test dan post-test), pengujian reliabilitas antarpenilai (inter-rater reliability) secara independen, maupun uji coba penerapan langsung di dalam kelas.
ABSTRACT
This community service program addressed an instructional design challenge among elementary school teachers in Depok City, where the ability to construct computable arithmetic problems did not routinely extend to designing mathematically authentic and cognitively demanding tasks. The initiative aimed to assist 18 elementary school teachers in designing, evaluating, and refining mathematical tasks through a participatory problem-posing cycle using artificial intelligence (AI) as a reflective feedback partner. The qualitative descriptive evaluation examined changes across task artifacts comprising four initial group problems, collaborative group revisions, and nine fully documented individual products. Initial baseline artifacts were limited to routine single-step computations situated within superficial narrative wrappers or devoid of context. Following collaborative AI-assisted appraisal and individual refinement, the majority of documented individual tasks shifted toward multi-step or analytical and evaluative reasoning embedded in realistic-generic or situated local contexts. However, these outcomes reflect qualitative improvements in documented task artifacts rather than a directly verified gain in teacher competence, given the absence of pre- and post-tests, independent inter-rater reliability testing, or direct classroom implementation trials.
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References
Brunetto, D., & Iacono, U. D. (2026). Modeling cycle and GenAI as resources for mathematics teachers’ professional development. Education Sciences, 16(4), Article 504. https://doi.org/10.3390/educsci16040504
Cai, J. (2022). What research says about teaching mathematics through problem posing. Éducation et Didactique, 16(3), 47–68. https://doi.org/10.4000/educationdidactique.10642
Chavarría-Arroyo, G., & Albanese, V. (2023). Contextualized mathematical problems: Perspective of teachers about problem posing. Education Sciences, 13(1), Article 6. https://doi.org/10.3390/educsci13010006
Chui, K. S. (2026a). Using large language models as formative assessors evaluating students’ mathematical modelling competence with ChatGPT [Data set]. Zenodo. https://doi.org/10.5281/zenodo.18961670
Chui, K. S. (2026b). Using large language models as formative assessors evaluating students’ mathematical modelling competence with ChatGPT [Data set]. Zenodo. https://doi.org/10.5281/zenodo.18961669
Darling-Hammond, L., Hyler, M. E., & Gardner, M. (2017). Effective teacher professional development. Learning Policy Institute. https://doi.org/10.54300/122.311
Glegola, A.-K., Jentsch, A., Ross, N., König, J., & Kaiser, G. (2026). The influence of teachers’ dispositions on their use of mathematical problem-solving tasks: A Qualitative Comparative Analysis based on teachers’ pedagogical content knowledge and beliefs. Journal of Mathematics Teacher Education. https://doi.org/10.1007/s10857-026-09761-2
Hat?saru, V. (2020). Exploring evidence of mathematical tasks and representations in the drawings of middle school students. International Electronic Journal of Mathematics Education, 15(3), Article em0593. https://doi.org/10.29333/iejme/8482
He, P., Li, Z., Wang, Z., Xiong, J., & Li, T. (2026). Judging the judges: Human validation of multi-LLM evaluation for high-quality K-12 science instructional materials. arXiv. https://doi.org/10.48550/arxiv.2602.13243
Henderson, M., Bearman, M., Chung, J., Fawns, T., Buckingham Shum, S., Matthews, K. E., & de Mello Heredia, J. (2025). Comparing generative AI and teacher feedback: Student perceptions of usefulness and trustworthiness. Assessment & Evaluation in Higher Education, 51(5), 863–878. https://doi.org/10.1080/02602938.2025.2502582
Hwang, S., Xu, R., Yao, Y., & Cai, J. (2024). Learning to teach through problem posing: A teacher’s journey in a networked teacher–researcher partnership. The Journal of Mathematical Behavior, 73, Article 101120. https://doi.org/10.1016/j.jmathb.2023.101120
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., ... Kuhn, J. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, Article 102274. https://doi.org/10.1016/j.lindif.2023.102274
Krushinskaia, K., Elen, J., & Raes, A. (2025). Pre-service teachers’ agency during their interactions with generative AI while designing for learning – a process view on Intelligent-TPACK. Computers and Education Open, 10, Article 100325. https://doi.org/10.1016/j.caeo.2025.100325
Lee, J., Smith, D., Woodhead, S., & Lan, A. (2024). Math multiple choice question generation via human-large language model collaboration. arXiv. https://doi.org/10.48550/arxiv.2405.00864
Lee, Y. (2024). The effects of functional moves in teacher questioning on students’ contextualization of mathematical word problem solving. Journal of Mathematics Teacher Education, 28(1), 73–101. https://doi.org/10.1007/s10857-023-09616-0
Liu, Y., & Shang, J. (2025). Charting diverse pathways to AI empowerment among teachers: A multi-case study in Chinese K-12 education. International Journal of Chinese Education, 14(3), 1–18. https://doi.org/10.1177/2212585x251408661
Lucy, L., August, T., Wang, R. E., Soldaini, L., Allison, C., & Lo, K. (2024). Mathfish: Evaluating language model math reasoning via grounding in educational curricula. arXiv. https://doi.org/10.48550/arxiv.2408.04226
Miao, F., & Cukurova, M. (2024). AI competency framework for teachers. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000391104
Miao, F., & Holmes, W. (2023). Guidance for generative AI in education and research. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000386693
Modén, M. U., Tallvid, M., Lundin, J., & Lindström, B. (2021). Intelligent tutoring systems: Why teachers abandoned a technology aimed at automating teaching processes. Proceedings of the 54th Hawaii International Conference on System Sciences (HICSS), 186–195. https://doi.org/10.24251/hicss.2021.186
Parrish, C. W., & Bryd, K. O. (2022). Cognitively demanding tasks: Supporting students and teachers during engagement and implementation. International Electronic Journal of Mathematics Education, 17(1), Article em0668. https://doi.org/10.29333/iejme/11475
Rezat, S. (2026). Students’ mathematical feedback processes using a digital curriculum resource: A qualitative perspective. ZDM – Mathematics Education. https://doi.org/10.1007/s11858-026-01821-4
Spreitzer, C., Straser, O., Zehetmeier, S., & Maaß, K. (2024). Mathematical modelling abilities of artificial intelligence tools: The case of ChatGPT. Education Sciences, 14(7), Article 698. https://doi.org/10.3390/educsci14070698
Tan, S. C., Tan, Y. Y., Teo, C. L., & Yuan, G. (2025). Teachers’ professional agency in learning with AI: A case study of a generative AI-based knowledge building learning companion for teachers. British Journal of Educational Technology. https://doi.org/10.1111/bjet.70013
Thomas, C. A., Berry, R. Q., & Sebastian, R. (2024). Examining the elements of culturally relevant pedagogy captured and missed in a measure of high-quality mathematics instruction. ZDM – Mathematics Education, 56(5), 953–964. https://doi.org/10.1007/s11858-024-01595-7
Trevisani, F. de M., & Maltempi, V. (2026). Generative AI and pedagogical authorship in mathematics education. Dalam WTM-Verlag eBooks (hlm. 159–164). https://doi.org/10.37626/ga9783959873680.0.27
Turvey, K., & Pachler, N. (2025). A topological exploration of convergence/divergence of human-mediated and algorithmically mediated pedagogy. British Journal of Educational Technology. https://doi.org/10.1111/bjet.70007
Zhang, L., Stylianides, G. J., & Stylianides, A. J. (2024). Enhancing mathematical problem posing competence: A meta-analysis of intervention studies. International Journal of STEM Education, 11(1), Article 48. https://doi.org/10.1186/s40594-024-00507-1
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