Artificial Intelligence as a Science Teacher Assistant: An Analysis of Machine Learning Utilization in Diagnosing Student Misconceptions: A Review
DOI:
10.29303/jppipa.v11i12.13089Published:
2025-12-25Downloads
Abstract
Diagnosing these misconceptions in a crowded classroom context is very difficult, time-consuming, and subjective when using conventional methods, which often leads to ineffective teaching interventions. To address the urgent need for accurate and objective diagnosis, this article proposes and analyzes the role of Artificial Intelligence (AI), specifically Machine Learning (ML) technologies such as natural language processing (NLP). ML models can analyze student response data (essays) quickly and consistently, acting as science teacher assistants to strengthen diagnostic capabilities. This study uses a systematic literature review method to analyze and synthesize existing research findings regarding Artificial Intelligence as a Science Teacher's Assistant: An Analysis of the Utilization of Machine Learning in Diagnosing Student Misconceptions. This research aims to analyze and explain Artificial Intelligence as a Science Teacher's Assistant: An Analysis of the Utilization of Machine Learning in Diagnosing Student Misconceptions. The brief objectives of this study are as follows: to analyze the utilization of Machine Learning (ML) models in objectively diagnosing, categorizing, and predicting students' misconceptions in science. The findings of this review study indicate that student misconceptions are a persistent barrier to learning, and conventional (manual, paper-based) diagnostic methods have proven inefficient and subjective for crowded classrooms. This validates the urgent need for technological solutions.
Keywords:
Artificial intelligence Machine learning MisconceptionReferences
Adytia Putri, A., Priatna, N., & Kusnandi, K. (2023). Analysis of Student Errors in Solving Mathematics Problems Based on Newman Procedure and Providing Scaffolding. Numerical: Jurnal Matematika Dan Pendidikan Matematika, 7(2), 321–332. https://doi.org/10.25217/numerical.v7i2.3993 DOI: https://doi.org/10.25217/numerical.v7i2.3993
Affengruber, L., Van Der Maten, M. M., Spiero, I., Nussbaumer-Streit, B., Mahmić-Kaknjo, M., Ellen, M. E., Goossen, K., … Spijker, R. (2024). An exploration of available methods and tools to improve the efficiency of systematic review production: A scoping review. BMC Medical Research Methodology, 24(1), 210. https://doi.org/10.1186/s12874-024-02320-4 DOI: https://doi.org/10.1186/s12874-024-02320-4
Albalawi, R., Yeap, T. H., & Benyoucef, M. (2020). Using Topic Modeling Methods for Short-Text Data: A Comparative Analysis. Frontiers in Artificial Intelligence, 3, 42. https://doi.org/10.3389/frai.2020.00042 DOI: https://doi.org/10.3389/frai.2020.00042
Bashir, A. S., Inusa, B. A., & Mahmud, A. I. (2025). Influence Of Artificial Intelligence On Leadership Communication And Emotional Intelligence In Public Colleges Of Education In Plateau State, Nigeria. Journal Of Digital Learning And Distance Education, 4(5), 1664–1674. https://doi.org/10.56778/jdlde.v4i5.579 DOI: https://doi.org/10.56778/jdlde.v4i5.579
Brown, B., Friesen, S., Beck, J., & Roberts, V. (2020). Supporting New Teachers as Designers of Learning. Education Sciences, 10(8), 207. https://doi.org/10.3390/educsci10080207 DOI: https://doi.org/10.3390/educsci10080207
Chen, Y.-C., Fan, K.-K., & Fang, K.-T. (2021). Effect of Flipped Teaching on Cognitive Load Level with Mobile Devices: The Case of a Graphic Design Course. Sustainability, 13(13), 7092. https://doi.org/10.3390/su13137092 DOI: https://doi.org/10.3390/su13137092
Chittleborough, G. D., & Treagust, D. F. (2009). Why Models are Advantageous to Learning Science. Educación Química, 20(1), 12–17. https://doi.org/10.1016/S0187-893X(18)30003-X DOI: https://doi.org/10.1016/S0187-893X(18)30003-X
Darling-Hammond, L., Schachner, A. C. W., Wojcikiewicz, S. K., & Flook, L. (2024). Educating teachers to enact the science of learning and development. Applied Developmental Science, 28(1), 1–21. https://doi.org/10.1080/10888691.2022.2130506 DOI: https://doi.org/10.1080/10888691.2022.2130506
Dawes, M., Sterrett, B. I., Brooks, D. S., Lee, D. L., Hamm, J. V., & Farmer, T. W. (2024). Enhancing Teachers’ Capacity to Manage Classroom Behavior as a Means to Reduce Burnout: Directed Consultation, Supported Professionalism, and the BASE Model. Journal of Emotional and Behavioral Disorders, 32(2), 110–123. https://doi.org/10.1177/10634266241235154 DOI: https://doi.org/10.1177/10634266241235154
El Azzouzy, O., Chanyour, T., & Andaloussi, S. J. (2025). Transformer-based models for sentiment analysis of YouTube video comments. Scientific African, 29, e02836. https://doi.org/10.1016/j.sciaf.2025.e02836 DOI: https://doi.org/10.1016/j.sciaf.2025.e02836
El-Bouzaidi, Y. E. I., & Abdoun, O. (2023). Advances in artificial intelligence for accurate and timely diagnosis of COVID-19: A comprehensive review of medical imaging analysis. Scientific African, 22, e01961. https://doi.org/10.1016/j.sciaf.2023.e01961 DOI: https://doi.org/10.1016/j.sciaf.2023.e01961
Filiz, O., Kaya, M. H., & Adiguzel, T. (2025). Teachers and AI: Understanding the factors influencing AI integration in K-12 education. Education and Information Technologies, 30(13), 17931–17967. https://doi.org/10.1007/s10639-025-13463-2 DOI: https://doi.org/10.1007/s10639-025-13463-2
Franz, D. J., Richter, T., Lenhard, W., Marx, P., Stein, R., & Ratz, C. (2023). The Influence of Diagnostic Labels on the Evaluation of Students: A Multilevel Meta-Analysis. Educational Psychology Review, 35(1), 17. https://doi.org/10.1007/s10648-023-09716-6 DOI: https://doi.org/10.1007/s10648-023-09716-6
Gross, S., Hankeln, C., Rösike, K.-A., & Prediger, S. (2025). How do Expert and Novice Teachers Monitor and Enhance Student Understanding? Qualitative Comparisons Informing the Design of a Digital Formative Assessment Platform. Technology, Knowledge and Learning, 30(2), 991–1020. https://doi.org/10.1007/s10758-024-09755-0 DOI: https://doi.org/10.1007/s10758-024-09755-0
Hadi Mogavi, R., Deng, C., Juho Kim, J., Zhou, P., D. Kwon, Y., Hosny Saleh Metwally, A., Tlili, A., Bassanelli, S., Bucchiarone, A., Gujar, S., Nacke, L. E., & Hui, P. (2024). ChatGPT in education: A blessing or a curse? A qualitative study exploring early adopters’ utilization and perceptions. Computers in Human Behavior: Artificial Humans, 2(1), 100027. https://doi.org/10.1016/j.chbah.2023.100027 DOI: https://doi.org/10.1016/j.chbah.2023.100027
Hamilton, L., Elliott, D., Quick, A., Smith, S., & Choplin, V. (2023). Exploring the Use of AI in Qualitative Analysis: A Comparative Study of Guaranteed Income Data. International Journal of Qualitative Methods, 22, 16094069231201504. https://doi.org/10.1177/16094069231201504 DOI: https://doi.org/10.1177/16094069231201504
Kampatzis, A., Sidiropoulos, A., Diamantaras, K., & Ougiaroglou, S. (2024). Sentiment Dimensions and Intentions in Scientific Analysis: Multilevel Classification in Text and Citations. Electronics, 13(9), 1753. https://doi.org/10.3390/electronics13091753 DOI: https://doi.org/10.3390/electronics13091753
Katona, J., & Gyonyoru, K. I. K. (2025). Integrating AI-based adaptive learning into the flipped classroom model to enhance engagement and learning outcomes. Computers and Education: Artificial Intelligence, 8, 100392. https://doi.org/10.1016/j.caeai.2025.100392 DOI: https://doi.org/10.1016/j.caeai.2025.100392
Kumar, Y., Marchena, J., Awlla, A. H., Li, J. J., & Abdalla, H. B. (2024). The AI-Powered Evolution of Big Data. Applied Sciences, 14(22), 10176. https://doi.org/10.3390/app142210176 DOI: https://doi.org/10.3390/app142210176
Lameras, P., & Arnab, S. (2021). Power to the Teachers: An Exploratory Review on Artificial Intelligence in Education. Information, 13(1), 14. https://doi.org/10.3390/info13010014 DOI: https://doi.org/10.3390/info13010014
Madanchian, M., & Taherdoost, H. (2025). The impact of artificial intelligence on research efficiency. Results in Engineering, 26, 104743. https://doi.org/10.1016/j.rineng.2025.104743 DOI: https://doi.org/10.1016/j.rineng.2025.104743
Manorat, P., Tuarob, S., & Pongpaichet, S. (2025). Artificial intelligence in computer programming education: A systematic literature review. Computers and Education: Artificial Intelligence, 8, 100403. https://doi.org/10.1016/j.caeai.2025.100403 DOI: https://doi.org/10.1016/j.caeai.2025.100403
Mariani, M. M., Machado, I., Magrelli, V., & Dwivedi, Y. K. (2023). Artificial intelligence in innovation research: A systematic review, conceptual framework, and future research directions. Technovation, 122, 102623. https://doi.org/10.1016/j.technovation.2022.102623 DOI: https://doi.org/10.1016/j.technovation.2022.102623
Meletiou-Mavrotheris, M., Bakogianni, D., Danidou, Y., Paparistodemou, E., & Kofteros, A. (2025). Investigating Student Teacher Engagement with Data-Driven AI and Ethical Reasoning in a Graduate-Level Education Course. Education Sciences, 15(9), 1179. https://doi.org/10.3390/educsci15091179 DOI: https://doi.org/10.3390/educsci15091179
Morris, D. L. (2025). Rethinking Science Education Practices: Shifting from Investigation-Centric to Comprehensive Inquiry-Based Instruction. Education Sciences, 15(1), 73. https://doi.org/10.3390/educsci15010073 DOI: https://doi.org/10.3390/educsci15010073
Mulyani, H., Istiaq, M. A., Shauki, E. R., Kurniati, F., & Arlinda, H. (2025). Transforming education: Exploring the influence of generative AI on teaching performance. Cogent Education, 12(1), 2448066. https://doi.org/10.1080/2331186X.2024.2448066 DOI: https://doi.org/10.1080/2331186X.2024.2448066
Musullulu, H. (2025). Evaluating attention deficit and hyperactivity disorder (ADHD): A review of current methods and issues. Frontiers in Psychology, 16, 1466088. https://doi.org/10.3389/fpsyg.2025.1466088 DOI: https://doi.org/10.3389/fpsyg.2025.1466088
Nunez-Oviedo, M. C., & Clement, J. J. (2019). Large Scale Scientific Modeling Practices That Can Organize Science Instruction at the Unit and Lesson Levels. Frontiers in Education, 4, 68. https://doi.org/10.3389/feduc.2019.00068 DOI: https://doi.org/10.3389/feduc.2019.00068
Oise, G. P., Ejenarhome Otega Prosper, Augustine Osazee Airhiavbere, & Agwam Gladys Ifeoma. (2025). Student Success Prediction in Digital Learning Environments. Journal Of Digital Learning And Distance Education, 4(6), 1697–1707. https://doi.org/10.56778/jdlde.v4i6.592 DOI: https://doi.org/10.56778/jdlde.v4i6.592
Olde Bekkink, M., Donders, A. R. T. R., Kooloos, J. G., De Waal, R. M. W., & Ruiter, D. J. (2016). Uncovering students’ misconceptions by assessment of their written questions. BMC Medical Education, 16(1), 221. https://doi.org/10.1186/s12909-016-0739-5 DOI: https://doi.org/10.1186/s12909-016-0739-5
Pennisi, F., Pinto, A., Ricciardi, G. E., Signorelli, C., & Gianfredi, V. (2025). The Role of Artificial Intelligence and Machine Learning Models in Antimicrobial Stewardship in Public Health: A Narrative Review. Antibiotics, 14(2), 134. https://doi.org/10.3390/antibiotics14020134 DOI: https://doi.org/10.3390/antibiotics14020134
Romero, J. D., Feijoo-Garcia, M. A., Nanda, G., Newell, B., & Magana, A. J. (2024). Evaluating the Performance of Topic Modeling Techniques with Human Validation to Support Qualitative Analysis. Big Data and Cognitive Computing, 8(10), 132. https://doi.org/10.3390/bdcc8100132 DOI: https://doi.org/10.3390/bdcc8100132
Rost, M., & Knuuttila, T. (2022). Models as Epistemic Artifacts for Scientific Reasoning in Science Education Research. Education Sciences, 12(4), 276. https://doi.org/10.3390/educsci12040276 DOI: https://doi.org/10.3390/educsci12040276
Schmidt, D. A., Alboloushi, B., Thomas, A., & Magalhaes, R. (2025). Integrating artificial intelligence in higher education: Perceptions, challenges, and strategies for academic innovation. Computers and Education Open, 9, 100274. https://doi.org/10.1016/j.caeo.2025.100274 DOI: https://doi.org/10.1016/j.caeo.2025.100274
Sorsa, A., Jerene, D., Negash, S., & Habtamu, A. (2020). Use of Xpert Contributes to Accurate Diagnosis, Timely Initiation, and Rational Use of Anti-TB Treatment Among Childhood Tuberculosis Cases in South Central Ethiopia. Pediatric Health, Medicine and Therapeutics, Volume 11, 153–160. https://doi.org/10.2147/PHMT.S244154 DOI: https://doi.org/10.2147/PHMT.S244154
Sozio, G., Agostinho, S., Tindall-Ford, S., & Paas, F. (2024). Enhancing Teaching Strategies through Cognitive Load Theory: Process vs. Product Worked Examples. Education Sciences, 14(8), 813. https://doi.org/10.3390/educsci14080813 DOI: https://doi.org/10.3390/educsci14080813
Strielkowski, W., Grebennikova, V., Lisovskiy, A., Rakhimova, G., & Vasileva, T. (2025). AI ‐driven adaptive learning for sustainable educational transformation. Sustainable Development, 33(2), 1921–1947. https://doi.org/10.1002/sd.3221 DOI: https://doi.org/10.1002/sd.3221
Surur, F. M., Mamo, A. A., Gebresilassie, B. G., Mekonen, K. A., Golda, A., Behera, R. K., & Kumar, K. (2025). Unlocking the power of machine learning in big data: A scoping survey. Data Science and Management, S2666764925000104. https://doi.org/10.1016/j.dsm.2025.02.004 DOI: https://doi.org/10.1016/j.dsm.2025.02.004
Tamascelli, N., Campari, A., Parhizkar, T., & Paltrinieri, N. (2024). Artificial Intelligence for safety and reliability: A descriptive, bibliometric and interpretative review on machine learning. Journal of Loss Prevention in the Process Industries, 90, 105343. https://doi.org/10.1016/j.jlp.2024.105343 DOI: https://doi.org/10.1016/j.jlp.2024.105343
Towler, L., Bondaronek, P., Papakonstantinou, T., Amlôt, R., Chadborn, T., Ainsworth, B., & Yardley, L. (2023). Applying machine-learning to rapidly analyze large qualitative text datasets to inform the COVID-19 pandemic response: Comparing human and machine-assisted topic analysis techniques. Frontiers in Public Health, 11, 1268223. https://doi.org/10.3389/fpubh.2023.1268223 DOI: https://doi.org/10.3389/fpubh.2023.1268223
Tu, X., He, Z., Huang, Y., Zhang, Z.-H., Yang, M., & Zhao, J. (2024). An overview of large AI models and their applications. Visual Intelligence, 2(1), 34. https://doi.org/10.1007/s44267-024-00065-8 DOI: https://doi.org/10.1007/s44267-024-00065-8
Tufail, S., Riggs, H., Tariq, M., & Sarwat, A. I. (2023). Advancements and Challenges in Machine Learning: A Comprehensive Review of Models, Libraries, Applications, and Algorithms. Electronics, 12(8), 1789. https://doi.org/10.3390/electronics12081789 DOI: https://doi.org/10.3390/electronics12081789
Upu, A., Taneo, P. N. L., & Daniel, F. (2022). Analisis Kesalahan Siswa dalam Menyelesaikan Soal Cerita Berdasarkan Tahapan Newman dan Upaya Pemberian Scaffolding. Edumatica : Jurnal Pendidikan Matematika, 12(01), 52–62. https://doi.org/10.22437/edumatica.v12i01.16593 DOI: https://doi.org/10.22437/edumatica.v12i01.16593
Vaesen, K., & Houkes, W. (2021). A new framework for teaching scientific reasoning to students from application-oriented sciences. European Journal for Philosophy of Science, 11(2), 56. https://doi.org/10.1007/s13194-021-00379-0 DOI: https://doi.org/10.1007/s13194-021-00379-0
Van Der Steen, J., Van Schilt-Mol, T., Van Der Vleuten, C., & Joosten-ten Brinke, D. (2022). Supporting Teachers in Improving Formative Decision-Making: Design Principles for Formative Assessment Plans. Frontiers in Education, 7, 925352. https://doi.org/10.3389/feduc.2022.925352 DOI: https://doi.org/10.3389/feduc.2022.925352
Vieriu, A. M., & Petrea, G. (2025). The Impact of Artificial Intelligence (AI) on Students’ Academic Development. Education Sciences, 15(3), 343. https://doi.org/10.3390/educsci15030343 DOI: https://doi.org/10.3390/educsci15030343
Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications, 252, 124167. https://doi.org/10.1016/j.eswa.2024.124167 DOI: https://doi.org/10.1016/j.eswa.2024.124167
Wiese, L., Will Pinto, H. E., & Magana, A. J. (2024). Undergraduate and graduate students’ conceptual understanding of model classification outcomes under the lens of scientific argumentation. Computer Applications in Engineering Education, 32(4), e22734. https://doi.org/10.1002/cae.22734 DOI: https://doi.org/10.1002/cae.22734
Yang, W., Fu, R., Amin, M. B., & Kang, B. (2025). The Impact of Modern AI in Metadata Management. Human-Centric Intelligent Systems, 5(3), 323–350. https://doi.org/10.1007/s44230-025-00106-5 DOI: https://doi.org/10.1007/s44230-025-00106-5
Yaseen, H., Mohammad, A. S., Ashal, N., Abusaimeh, H., Ali, A., & Sharabati, A.-A. A. (2025). The Impact of Adaptive Learning Technologies, Personalized Feedback, and Interactive AI Tools on Student Engagement: The Moderating Role of Digital Literacy. Sustainability, 17(3), 1133. https://doi.org/10.3390/su17031133 DOI: https://doi.org/10.3390/su17031133
Zachariadis, C. B., & Leligou, H. C. (2024). Harnessing Artificial Intelligence for Automated Diagnosis. Information, 15(6), 311. https://doi.org/10.3390/info15060311 DOI: https://doi.org/10.3390/info15060311
Zhou, Y., Javed, K., & Iveson, J. (2025). Addressing sophisticated misconceptions: An assimilation-based method for teaching accounting expenses. Frontiers in Education, 10, 1567329. https://doi.org/10.3389/feduc.2025.1567329 DOI: https://doi.org/10.3389/feduc.2025.1567329
License
Copyright (c) 2025 Iwan Purnama, Rian Farta Wijaya, Aziddin Harahap, Firman Edi
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with Jurnal Penelitian Pendidikan IPA, 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 4.0 International License (CC-BY License). This license allows authors to use all articles, data sets, graphics, and appendices in data mining applications, search engines, web sites, blogs, and other platforms by providing an appropriate reference. The journal allows the author(s) to hold the copyright without restrictions and will retain publishing rights without restrictions.
- 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 Jurnal Penelitian Pendidikan IPA.
- 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 (See The Effect of Open Access).
