Designing Diagnosis-Informed Microlearning with AI-Feedback for Stoichiometry Learning

Jaman Fahmi

doi:10.24114/aea07q93

Authors

Jaman Fahmi State University of Medan

DOI:

https://doi.org/10.24114/aea07q93

Keywords:

Stoichiometry; misconception; diagnostic assessment; microlearning; AI-feedback

Abstract

Stoichiometry is conceptually challenging because students may experience misconceptions that are not adequately revealed through conventional achievement tests. This study aimed to design and conduct a limited proof-of-concept of diagnosis-informed microlearning with AI-feedback to support students’ conceptual understanding of stoichiometry. The study involved 68 Grade X students from two senior high schools and five expert validators. Thirty diagnostic items were validated, with 24 meeting the predetermined content-validity criteria, from which 18 items were selected to represent six stoichiometry subtopics. A separate 30-item conceptual understanding test yielded 24 valid items, from which 20 items were selected for limited implementation. The initial diagnostic assessment was administered to 34 students, while the revised learning website was implemented with another 34 students. The diagnostic results showed the highest proportion of conceptual problems in limiting reagents (67.7%). Following the limited implementation, 52.94% of students were classified as having moderate-to-high conceptual understanding. Students’ responses to the AI-feedback website were generally positive, with positive responses exceeding 75% across all indicators. The findings provide initial evidence of the potential of diagnosis-informed AI-feedback for formative stoichiometry learning, while further controlled studies are required to establish its effectiveness.

References

Aini, R. G., Ibnu, S., and Budiasih, E. (2016), Identifikasi miskonsepsi dalam materi stoikiometri pada siswa kelas X di SMAN 1 Malang melalui soal diagnostik three-tier, J-PEK (Jurnal Pembelajaran Kimia), 1(2): 50–56. https://doi.org/10.17977/um026v1i22016p50-56.

Alfiyanti, R., and Sukarmin, S. (2021), The effectiveness of software as learning media to detect and reduce misconception in stoichiometry material, International Journal of Chemistry Education Research, 4(2). https://doi.org/10.20885/ijcer.vol4.iss2.art3.

Alias, N. F., and Abdul Razak, R. (2023), Exploring the pedagogical aspects of microlearning in educational settings: A systematic literature review, Malaysian Journal of Learning and Instruction, 20(2): 267–294. https://doi.org/10.32890/mjli2023.20.2.3.

Arief, M. D., Auliah, A., and Hardin. (2021), Pengembangan E-Magazine reaksi reduksi dan oksidasi sebagai media pembelajaran kimia kelas X SMA/MA, Jurnal Inovasi Pembelajaran Kimia, 3(2): 148–163. https://doi.org/10.24114/jipk.v3i2.28111.

Balasundaram, S., Mathew, J., and Nair, S. (2024), Microlearning and learning performance in higher education: A post-test control group study, Journal of Learning for Development, 11(1): 1–14.

Bauer, E., Richters, C., Pickal, A. J., Klippert, M., Sailer, M., and Stadler, M. (2025), Effects of AI-generated adaptive feedback on statistical skills and interest in statistics: A field experiment in higher education, British Journal of Educational Technology, 56(5): 1735–1757. https://doi.org/10.1111/bjet.13609.

Cai, Z., Gui, Y., Mao, P., Wang, Z., Hao, X., Fan, X., and Tai, R. H. (2023), The effect of feedback on academic achievement in technology-rich learning environments (TREs): A meta-analytic review, Educational Research Review, 39: 100521. https://doi.org/10.1016/j.edurev.2023.100521.

Colliot, T., Krichen, O., Girard, N., Anquetil, É., and Jamet, É. (2024), What makes tablet-based learning effective? A study of the role of real-time adaptive feedback, British Journal of Educational Technology, 55(5): 2278–2295. https://doi.org/10.1111/bjet.13439.

Dai, W., Tsai, Y.-S., Lin, J., Aldino, A., Jin, H., Li, T., Gašević, D., and Chen, G. (2024), Assessing the proficiency of large language models in automatic feedback generation: An evaluation study, Computers and Education: Artificial Intelligence, 7: 100299. https://doi.org/10.1016/j.caeai.2024.100299.

Deeva, G., Bogdanova, D., Serral, E., Snoeck, M., and De Weerdt, J. (2021), A review of automated feedback systems for learners: Classification framework, challenges and opportunities, Computers & Education, 162: 104094. https://doi.org/10.1016/j.compedu.2020.104094.

Eitemüller, C., Trauten, F., Striewe, M., and Walpuski, M. (2023), Digitalization of multistep chemistry exercises with automated formative feedback, Journal of Science Education and Technology, 32: 453–467. https://doi.org/10.1007/s10956-023-10043-2.

Ekawisudawati, E., Wijaya, M., and Danial, M. (2021), Analisis miskonsepsi peserta didik pada materi asam basa menggunakan instrumen three-tier diagnostic test, Chemistry Education Review, 5(1): 62–72. https://doi.org/10.26858/cer.v5i1.13315.

Fitriani, A. R., Rahmawati, A., and Lathifa, U. (2022), Phenomenology of conceptual understanding ability, representation, and student algorithm on stoichiometry materials, Journal of Educational Chemistry, 4(1): 9–18. https://doi.org/10.21580/jec.2022.4.1.9744.

Gani, A. (2025), Analisis miskonsepsi siswa pada materi stoikiometri menggunakan tes diagnostik three-tier, BINTANG: Jurnal Pendidikan dan Sains, 7(1): 95–111.

Hasniyah, F., and Muchtar, Z. (2021), Pengembangan uji instrumen tiga tingkat dengan CRI untuk mendeteksi miskonsepsi dalam pembelajaran reaksi redoks, Jurnal Inovasi Pembelajaran Kimia, 3(2): 123–135. https://doi.org/10.24114/jipk.v3i2.26503.

Huang, Y., Hansen, M., Richter, E., Kleickmann, T., Scheiter, K., and Richter, D. (2025), Enhancing preservice teachers’ noticing via adaptive feedback in a virtual reality classroom, Learning and Instruction, 95: 102053. https://doi.org/10.1016/j.learninstruc.2024.102053.

Karimatunnisa’, K., and Aloysius, H. P. (2023), Identifikasi miskonsepsi konsep stoikiometri pada sumber belajar kimia SMA, Jurnal Riset Pembelajaran Kimia, 8(2). https://doi.org/10.21831/jrpk.v8i2.20632.

Kinder, A., Briese, F. J., Jacobs, M., Dern, N., Glodny, N., Jacobs, S., and Lessmann, S. (2025), Effects of adaptive feedback generated by a large language model: A case study in teacher education, Computers and Education: Artificial Intelligence, 8: 100349. https://doi.org/10.1016/j.caeai.2024.100349.

Krause, A., Polarz, S., Hoppe, A., Ewerth, R., and Nehring, A. (2026), Towards defining, assessing and modelling competency levels in stoichiometry, Chemistry Education Research and Practice, 27: 351–367. https://doi.org/10.1039/D5RP00077G.

Kurniawan, C., and Jahro, I. S. (2021), Pengembangan handout titrasi asam-basa berbasis Android terintegrasi model Discovery Learning dan soal-soal HOTS, Jurnal Inovasi Pembelajaran Kimia, 3(2): 136–147. https://doi.org/10.24114/jipk.v3i2.28207.

Lawshe, C. H. (1975), A quantitative approach to content validity, Personnel Psychology, 28(4): 563–575. https://doi.org/10.1111/j.1744-6570.1975.tb01393.x.

Lestari, E. A., Harjito, H., Susilaningsih, E., and Wijayati, N. (2021), Analisis miskonsepsi menggunakan tes diagnosa three-tier multiple choice pada materi stoikiometri, Jurnal Inovasi Pendidikan Kimia, 15(2): 2824–2830. https://doi.org/10.15294/jipk.v15i2.29171.

Liao, M., Zhu, K., and Wang, G. (2024), Can human-machine feedback in a smart learning environment enhance learners’ learning performance? A meta-analysis, Frontiers in Psychology, 14: 1288503. https://doi.org/10.3389/fpsyg.2023.1288503.

Maier, U., and Klotz, C. (2022), Personalized feedback in digital learning environments: Classification framework and literature review, Computers and Education: Artificial Intelligence, 3: 100080. https://doi.org/10.1016/j.caeai.2022.100080.

Mejeh, M., Sarbach, L., and Hascher, T. (2024), Effects of adaptive feedback through a digital tool: A mixed-methods study on the course of self-regulated learning, Education and Information Technologies, 29: 1–43. https://doi.org/10.1007/s10639-024-12510-8.

Meyer, J., Jansen, T., Schiller, R., Liebenow, L. W., Steinbach, M., Horbach, A., and Fleckenstein, J. (2024), Using LLMs to bring evidence-based feedback into the classroom: AI-generated feedback increases secondary students’ text revision, motivation, and positive emotions, Computers and Education: Artificial Intelligence, 6: 100199. https://doi.org/10.1016/j.caeai.2023.100199.

Monib, W. K., Qazi, A., and Apong, R. A. (2025), Microlearning beyond boundaries: A systematic review and a novel framework for improving learning outcomes, Heliyon, 11(2): e41413. https://doi.org/10.1016/j.heliyon.2024.e41413.

Mubarokah, F. D., Mulyani, S., and Indriyanti, N. Y. (2018), Identifying students’ misconceptions of acid-base concepts using a three-tier diagnostic test: A case of Indonesia and Thailand, Journal of Turkish Science Education, 15(Special Issue): 51–58. https://doi.org/10.12973/tused.10256a.

Panggabean, F. T. M., and Purba, J. (2021), Pengembangan e-modul terintegrasi media berbasis Adobe Flash CS6 untuk meningkatkan kemampuan pemecahan masalah kimia mahasiswa, Jurnal Inovasi Pembelajaran Kimia, 3(2): 116–122. https://doi.org/10.24114/jipk.v3i2.28108.

Polit, D. F., Beck, C. T., and Owen, S. V. (2007), Is the CVI an acceptable indicator of content validity? Appraisal and recommendations, Research in Nursing & Health, 30(4): 459–467. https://doi.org/10.1002/nur.20199.

Pölloth, B. (2025), High school students experimenting with computational chemistry: Design-based research on and through the “Comp-Chem-Lab”, Journal of Chemical Education, 102(4): 1367–1379. https://doi.org/10.1021/acs.jchemed.4c01136.

Rilanty, N., and Juwitaningsih, T. (2020), Pengembangan media pembelajaran berbasis website untuk meningkatkan hasil belajar siswa pada materi kesetimbangan kimia, Jurnal Inovasi Pembelajaran Kimia, 2(1): 36–40. https://doi.org/10.24114/jipk.v2i1.17844.

Rosa, V., States, N. E., Corrales, A., Nguyen, Y., and Atkinson, M. B. (2022), Relevance and equity: Should stoichiometry be the foundation of introductory chemistry courses?, Chemistry Education Research and Practice, 23: 662–685. https://doi.org/10.1039/D1RP00333J.

Sa’adah, N., Haryani, S., and Mahatmanti, W. (2022), Analysis of chemical misconceptions of stoichiometry materials using Certainty of Response Index, International Journal of Active Learning, 7(1): 94–102.

Sailer, M., Bauer, E., Hofmann, R., Kiesewetter, J., Glas, J., Gurevych, I., and Fischer, F. (2023), Adaptive feedback from artificial neural networks facilitates pre-service teachers’ diagnostic reasoning in simulation-based learning, Learning and Instruction, 83: 101620. https://doi.org/10.1016/j.learninstruc.2022.101620.

Shiddiqi, M. H. A., Arthamena, V. D., Ayyubi, M., Manarisip, A. J., and Aznam, N. (2024), Systematic literature review: Analysis of misconception problems and diagnostic instruments for learning chemistry, Jurnal Penelitian Pendidikan IPA, 10(4): 168–179. https://doi.org/10.29303/jppipa.v10i4.5189.

Silaban, R., and Panggabean, M. V. (2022), Pengembangan media pembelajaran berbasis Android pada materi kesetimbangan kimia, Jurnal Inovasi Pembelajaran Kimia, 4(1): 1–9. https://doi.org/10.24114/jipk.v4i1.24085.

Silaban, R., and Sianturi, P. A. (2021), Pengembangan media pembelajaran berbasis Android pada materi laju reaksi, Jurnal Inovasi Pembelajaran Kimia, 3(2): 191–200. https://doi.org/10.24114/jipk.v3i2.22814.

Soeharto, S., Csapó, B., Sarimanah, E., Dewi, F. I., and Sabri, T. (2019), A review of students’ common misconceptions in science and their diagnostic assessment tools, Jurnal Pendidikan IPA Indonesia, 8(2): 247–266. https://doi.org/10.15294/jpii.v8i2.18649.

Stott, A. E. (2023), Epistemological lessons from inconsistencies in teachers’ errors related to use of the mole ratio in stoichiometry calculations: A cue for professional development, Journal of Chemical Education, 100(7): 2548–2557. https://doi.org/10.1021/acs.jchemed.2c01252.

Tan, X., Cheng, G., and Ling, M. H. A. (2025), Artificial intelligence in teaching and teacher professional development: A systematic review, Computers and Education: Artificial Intelligence, 8: 100355. https://doi.org/10.1016/j.caeai.2024.100355.