Exploring the impact of artificial intelligence in chemistry teaching: A systematic review of empirical research
DOI:
https://doi.org/10.30862/accej.v8i2.1083Keywords:
Artificial intelligence, chemistry teaching, empirical research, literature reviewAbstract
Chemistry education frequently struggles to foster a comprehensive understanding, often because it focuses too narrowly on macroscopic, submicroscopic, or symbolic representations. While Artificial Intelligence offers considerable potential to enhance learning, research specifically examining its impact on chemistry teaching remains scarce. This study aimed to identify, evaluate, and synthesize empirical literature on the effects of AI in chemistry teaching. This study was conducted in accordance with the PRISMA three sequential stages: 1) a comprehensive literature search in scientific databases utilizing keywords such as Artificial Intelligence, Chemistry Teaching, and Empirical Study/Research; 2) a selection process based on the inclusion and exclusion criteria; and 3) systematic data extraction. The literature review incorporated 13 empirical research articles published in Scopus- and Sinta-indexed journals. Findings consistently indicate that AI integration significantly impacts learner performance and instructional effectiveness by facilitating just-in-time, automated, and individualized feedback. Specific AI applications identified include generative tools for conceptual problem-solving, the utilization of ChatGPT/Bing Chat, gamified learning approaches, and AI assistants within remote laboratory settings. Nevertheless, this study highlights the inherent limitations of AI in addressing complex chemical content, alongside prevalent student concerns about AI accuracy, plagiarism, data privacy, and the potential for over-reliance on this technology.
References
Aldwinarta, F. H., Nurdiana, R. & Sulistina, O. (2024). Media pembelajaran berbasis ai chatbot pada materi termokimia di SMA apakah dibutuhkan? Jurnal Inovasi Pendidikan Kimia, 18(1), 1–6. https://doi.org/10.15294/jipk.v18i1.49044
Almasri, F. (2024). Exploring the impact of artificial intelligence in teaching and learning of science: A systematic review of empirical research. Research in Science Education, 54(5), 977–997. https://doi.org/10.1007/s11165-024-10176-3
Balaquiao, E. C. (2024). Optimizing students’ performance through artificial intelligence (ai) technology: A gamified approach to smart learning environment. Journal of Pedagogy and Education Science, 3(02), 104–114. https://doi.org/10.56741/jpes.v3i02.515
Basimin, M. Q., Habiddin, H. & Joharmawan, R. (2023). Higher order thinking skills and visual representations of chemical concepts: A literature review. Hydrogen: Jurnal Kependidikan Kimia, 11(6), 1043. https://doi.org/10.33394/hjkk.v11i6.10173
Blonder, R., Rap, S. & Mamlok-Naaman, R. (2023). Frontiers of research in chemistry education for the benefit of chemistry teachers. Chemistry Teacher International, 5(2), 107–111. https://doi.org/10.1515/cti-2023-0041
Chi, M., Zheng, C. & He, P. (2024). Assessing high school students’ chemical thinking using an essential questions-perspective framework. Chemistry Education Research and Practice, 25(4), 1143–1158. https://doi.org/10.1039/d4rp00106k
Chiu, W. K. (2021). Pedagogy of emerging technologies in chemical education during the era of digitalization and artificial intelligence: a systematic review. Education Sciences, 11(11), 709. https://doi.org/10.3390/educsci11110709
Daher, W., Diab, H. & Rayan, A. (2023). Artificial intelligence generative tools and conceptual knowledge in problem solving in chemistry. Information (Switzerland), 14(7), 409. https://doi.org/10.3390/info14070409
De la Ossa, L. A. M., Garzón, N. N., Pérez, C., Puildo, B. E. & Hoyos, J. H. (2024). Evaluation of artificial intelligence as a source of motivation in the teaching of chemistry: A study with tenth grade students at the adventist school of ibagué. Evolutionary Studies in Imaginative Culture, 2193, 2193–2208. https://doi.org/10.70082/esiculture.vi.1721
Eitemüller, C., Trauten, F., Striewe, M. & Walpuski, M. (2023). Digitalization of multistep chemistry exercises with automated formative feedback. Journal of Science Education and Technology, 32(3), 453–467. https://doi.org/10.1007/s10956-023-10043-2
Fan, Z., Yan, Z. & Wen, S. (2023). Deep learning and artificial intelligence in sustainability: A review of SDGs, renewable energy, and environmental health. Sustainability (Switzerland), 15(18), 13493. https://doi.org/10.3390/su151813493
Halimah, N., Degeng, M. D. K. & Soepriyanto, Y. (2024). Peranan tingkat penerimaan learning management system terhadap kemampuan self regulated learning. JKTP: Jurnal Kajian Teknologi Pendidikan, 7(3), 118. https://doi.org/10.17977/um038v7i32024p118
Hasmarani, S., . R. & . R. (2019). Analisis kemampuan peserta didik dalam menyelesaikan soal asam basa pada tingkat keterampilan berpikir kritis dan berpikir kreatif di kelas XI IPA SMA/MA di kec. bulukumpa. In Chemistry Education Review (CER) (Vol. 2, Issue 2). https://doi.org/10.26858/cer.v2i2.8954
Iyamuremye, A., Niyonzima, F. N., Mukiza, J., Twagilimana, I., Nyirahabimana, P., Nsengimana, T., Habiyaremye, J. D., Habimana, O. & Nsabayezu, E. (2024). Utilization of artificial intelligence and machine learning in chemistry education: a critical review. Discover Education, 3(1), 00197–5. https://doi.org/10.1007/s44217-024-00197-5
Jere, S. & Mpeta, M. (2025). Integrating generative artificial intelligence chatbots into chemistry teaching: Impact of affective factors on engagement and conceptual understanding. Eurasia Journal of Mathematics, Science and Technology Education, 21(10). https://doi.org/10.29333/ejmste/17077
Kohnke, S. & Zaugg, T. (2025). Artificial intelligence: An untapped opportunity for equity and access in STEM education. Education Sciences, 15(1). https://doi.org/10.3390/educsci15010068
Lizano-Sánchez, F., Idoyaga, I. J., Orduna, P., Rodriguez-Gil, L. & Arguedas-Matarrita, C. (2025). Students’ interactions with an artificial intelligence assistant in a remote chemistry laboratory. Frontiers in Education, 10. https://doi.org/10.3389/feduc.2025.1712743
P dos Santos, R. (2023). Enhancing chemistry learning with ChatGPT, bing chat, bard, and claude as agents-to-think-with: A comparative case study. In SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4629567
Pedersen, A. Y. & Caviglia, F. (2018). Researcher or fellow citizen? Looking for a role model in the humanities. Proceedings of International Conference of the Learning Sciences, ICLS , 2(2018-June), 945–948.
Permatasari, M. B., Rahayu, S. & Dasna, I. W. (2022). Chemistry learning using multiple representations: A systematic literature review. Journal of Science Learning, 5(2), 334–341. https://doi.org/10.17509/jsl.v5i2.42656
Qureshi, M. D. A., Ayaz, S., Amjad, F., Ramzan, M. F., Ikram, M., & Hussain, I. (2024). Innovating chemical education: Leveraging artificial intelligence and effective teaching strategies to enhance public engagement in environmental and organic chemistry. Indus Journal of Social Sciences, 2(2), 253-271. https://doi.org/10.59075/ijss.v2i2.189
Reyes, R. L. (2025). Integrating real-world problems into chemistry curricula: Enhancing relevance and student engagement. Forum for Education Studies, 3(2), 2177. https://doi.org/10.59400/fes2177
Sari, N. I. & Ahmad, F. (2024). Studi komparatif: Model pembelajaran kooperatif tipe jigsaw dan number head together dalam pembelajaran ipa pada siswa sekolah menegah pertama. Jurnal Studi Guru Dan Pembelajaran, 7(2), 850–861. https://doi.org/10.30605/jsgp.7.2.2024.4396
Talanquer, V. (2022). The complexity of reasoning about and with chemical representations. JACS Au, 2(12), 2658–2669. https://doi.org/10.1021/jacsau.2c00498
Tamim, R. M., Borokhovski, E., Bernard, R. M., Schmid, R. F., Abrami, P. C. & Pickup, D. I. (2021). A study of meta-analyses reporting quality in the large and expanding literature of educational technology. Australasian Journal of Educational Technology, 37(4), 100–115. https://doi.org/10.14742/ajet.6322
Tonyali, B., Ropohl, M. & Schwanewedel, J. (2023). What makes representations good representations for science education? A teacher-oriented summary of significant findings and a practical guideline for the transfer into teaching. Chemistry Teacher International, 5(4), 413–425. https://doi.org/10.1515/cti-2022-0019
Zawacki-Richter, O., Kerres, M., Bedenlier, S., Bond, M. & Buntins, K. (2019). Systematic reviews in educational research: Methodology, perspectives and application. Systematic Reviews in Educational Research: Methodology, Perspectives and Application, 64(3), 1–161. https://doi.org/10.1007/978-3-658-27602-7
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Nur Indah Sari, Fandi Ahmad
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
License and Copyright Agreement
In submitting the manuscript to the journal, the authors certify that:
- They are authorized by their co-authors to enter into these arrangements.
- The work described has not been formally published before, except in the form of an abstract or as part of a published lecture, review, thesis, or overlay journal. Please also carefully read Arfak Chem: Chemistry Education Journal Posting Your Article Policy at http://journalfkipunipa.org/index.php/accej/about
- That it is not under consideration for publication elsewhere,
- That its publication has been approved by all the author(s) and by the responsible authorities – tacitly or explicitly – of the institutes where the work has been carried out.
- They secure the right to reproduce any material that has already been published or copyrighted elsewhere.
- They agree to the following license and copyright agreement.
Copyright
Authors who publish with Arfak Chem: Chemistry Education Journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-ND 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
