Pemanfaatan Teknologi Deep Learning dan AI Adaptif untuk Transformasi Pembelajaran Soal Cerita Matematika bagi Guru Sekolah Dasar

Marthinus Yohanes Ruamba; Diki Kurniawan; Ortis Fredy Waromi; Robertho D Burumi

doi:10.58266/jpmb.v4i4.1332

Authors

Marthinus Yohanes Ruamba Universitas Cenderawasih, Indonesia
Diki Kurniawan Universitas Cenderawasih, Indonesia
Ortis Fredy Waromi Universitas Cenderawasih, Indonesia
Robertho D Burumi Universitas Cenderawasih, Indonesia

DOI:

https://doi.org/10.58266/jpmb.v4i4.1332

Keywords:

AI adaptif, deep learning, guru SD, literasi numerasi, soal cerita matematika

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan meningkatkan kompetensi guru SD Negeri Inpres Kampung Baru, Kota Jayapura, dalam memanfaatkan teknologi Deep Learning dan AI adaptif untuk menyusun, memvisualisasikan, dan mengevaluasi soal cerita matematika secara kontekstual. Kegiatan dilaksanakan pada 27 April sampai 1 Mei 2026 melalui tahapan diskusi awal dengan pihak sekolah, pre-test, penyampaian materi, praktik penggunaan AI, post-test, focus group discussion, refleksi, dan penyampaian hasil kegiatan. Data dikumpulkan melalui pre-test dan post-test, observasi praktik, FGD, angket respon guru, serta dokumentasi luaran. Hasil kegiatan menunjukkan bahwa kemampuan awal guru berada pada kategori sedang dengan rata-rata pre-test 75%. Setelah pelatihan dan pendampingan, rata-rata post-test mencapai 100%, sehingga terdapat peningkatan sebesar 25 poin persentase. Respon guru juga sangat positif; seluruh pernyataan angket memperoleh persentase setuju/sangat setuju sebesar 100%. Guru mampu mengadaptasi soal cerita menjadi lebih dekat dengan konteks lokal Kampung Baru, seperti aktivitas nelayan, Pasar Youtefa, daun sagu, dan ekonomi kampung. Kendala utama yang ditemukan adalah sebagian guru mengalami kesulitan login email dan belum terbiasa menyusun prompt pembelajaran. Luaran kegiatan meliputi modul/e-book praktis, kumpulan soal cerita adaptif berbasis konteks lokal, video dokumentasi, artikel pengabdian, dan peningkatan kompetensi guru. Kegiatan ini menunjukkan bahwa AI dapat menjadi alat bantu pedagogis yang efektif apabila digunakan dengan verifikasi guru, pendampingan teknis, dan penguatan etika penggunaan data siswa.

References

Akgun, S., & Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI and Ethics, 2, 431-440. https://doi.org/10.1007/s43681-021-00096-7

Barnes, M. A. (2020). Addressing the role of working memory in mathematical word-problem solving when designing intervention for struggling learners. ZDM Mathematics Education, 52, 87-96. https://doi.org/10.1007/s11858-019-01070-8

Chen, X., Xie, H., & Hwang, G. J. (2020). A multi-perspective study on artificial intelligence in education: Grants, conferences, journals, software tools, institutions, and researchers. Computers and Education: Artificial Intelligence, 1, 100005. https://doi.org/10.1016/j.caeai.2020.100005

Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20, 22. https://doi.org/10.1186/s41239-023-00392-8

Darling-Hammond, L., Flook, L., Cook-Harvey, C., Barron, B., & Osher, D. (2020). Implications for educational practice of the science of learning and development. Applied Developmental Science, 24(2), 97-140. https://doi.org/10.1080/10888691.2018.1537791

Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 4171-4186. https://doi.org/10.18653/v1/N19-1423

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.

Hwang, G. J., Xie, H., Wah, B. W., & Gasevic, D. (2020). Vision, challenges, roles and research issues of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1, 100001. https://doi.org/10.1016/j.caeai.2020.100001

Kabudi, T., Pappas, I., & Olsen, D. H. (2021). AI-enabled adaptive learning systems: A systematic mapping of the literature. Computers and Education: Artificial Intelligence, 2, 100017. https://doi.org/10.1016/j.caeai.2021.100017

Kasneci, E., Sessler, K., Kuchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Gunnemann, S., Hullermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., Stadler, M., Weller, J., Kuhn, J., & 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

Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi. (2023). Rapor Pendidikan Indonesia 2023. Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi.

Lin, X. (2021). Investigating the unique predictors of word-problem solving using meta-analytic structural equation modeling. Educational Psychology Review, 33, 1097-1124. https://doi.org/10.1007/s10648-020-09554-w

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson.

Mishra, P. (2019). Considering contextual knowledge: The TPACK diagram gets an upgrade. Journal of Digital Learning in Teacher Education, 35(2), 76-78. https://doi.org/10.1080/21532974.2019.1588611

OECD. (2019). PISA 2018 assessment and analytical framework. OECD Publishing. https://doi.org/10.1787/b25efab8-en

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

Powell, S. R., Stevens, E. A., & Berry, K. A. (2019). Effects of a word-problem intervention on word-problem language features for third-grade students with mathematics difficulty. Learning Disabilities: A Multidisciplinary Journal, 24(2), 1-14.

Ramirez, G., Shaw, S. T., & Maloney, E. A. (2018). Math anxiety: Past research, promising interventions, and a new interpretation framework. Educational Psychologist, 53(3), 145-164. https://doi.org/10.1080/00461520.2018.1447384

Ruamba, M. Y., Sukestiyarno, Y. L., Rochmad, R., & Asih, T. S. N. (2025). The impact of visual and multimodal representations in mathematics on cognitive load and problem-solving skills. International Journal of Advanced and Applied Sciences, 12(4), 164-172. https://doi.org/10.21833/ijaas.2025.04.018

Shute, V. J., & Rahimi, S. (2017). Review of computer-based assessment for learning in elementary and secondary education. Journal of Computer Assisted Learning, 33(1), 1-19. https://doi.org/10.1111/jcal.12172

UNESCO. (2019). Beijing consensus on artificial intelligence and education. UNESCO.

UNESCO. (2023). Guidance for generative AI in education and research. UNESCO.

Vessonen, T., Hellstrand, H., Kurkela, M., Aunio, P., & Laine, A. (2025). The effectiveness of mathematical word problem-solving interventions among elementary schoolers: A systematic review and meta-analysis. International Journal of Educational Research, 132, 102642. https://doi.org/10.1016/j.ijer.2025.102642

Zawacki-Richter, O., Marin, 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, 39. https://doi.org/10.1186/s41239-019-0171-0