Development and Validation of an AI-Based Model for Identifying Senior Secondary School Students’ Errors in Chemistry Calculations in Lokoja Metropolis, Kogi State, Nigeria
DOI:
https://doi.org/10.33003/fjs-2026-1020-6105Keywords:
Chemistry Calculations, Chemerror-AI, Error Classification, Conceptual Errors, Procedural Errors, Computational ErrorsAbstract
This study evaluated the development and validation of an AI-based model for identifying senior secondary school students’ errors in chemistry calculations in Lokoja Metropolis, Kogi State, Nigeria. The study adopted a Research and Development (R&D) design. The study population comprised all 24,152 SS1 and SS2 students, from which a sample of 269 students was selected. The instruments used for data collection were the researcher-developed Chemistry AI Test (CALT) and Identification of Students’ Errors in Chemistry Calculations (ISECC) questionnaire. The instruments were validated by two chemistry teachers and one Measurement and Evaluation expert. Reliability was established through pilot testing, and Kuder–Richardson Formula 20 and Cronbach’s alpha yielded coefficients of 0.79 and 0.81, respectively. Data were analysed using descriptive statistics, while students’ working steps were analysed using the ChemError-AI model to classify errors into mathematical/arithmetic, conceptual, formula, unit, interpretation, and substitution categories. Findings showed that the ChemError-AI model correctly classified 58 out of 64 unseen test cases, representing 90.6% accuracy. The findings indicate that the model can provide preliminary information about the types of errors students make in chemistry calculations. It was concluded that students experience difficulties at different stages of chemistry problem solving and that these difficulties extend beyond mathematical operations. It was recommended, among other measures, that teachers should receive practical training on how to interpret AI-generated classifications and combine AI-supported information with their professional judgement before making instructional decisions.
References
Abdullah, A., Azmi, J., & Ardiansyah, A. (2021). Analisis pola kesalahan jawaban siswa pada materi hitungan kimia. Orbital: Journal Pendidikan Kimia, 5(1), 13–27. https://doi.org/10.19109/ojpk.v5i1.7237
Afolabi, S. S., & Anaekwe, M. C. (2025). Identification and remediation of difficulties in writing and balancing chemical equations as perceived by chemistry students in Kaduna State. UniJos Journal of Contemporary Studies in Education, 1(2). 102-106
Agogo, P. O., & Maduawesi, C. C. (2021). Artificial intelligence-driven diagnostic approaches for identifying learners' error patterns in mathematics education.
Agbarakwe, H. A., & Chibueze, O. O. (2024). Leveraging artificial intelligence for enhanced assessment and feedback mechanisms in Nigeria higher education system. International Journal of Research and Innovation in Social Science, 8(9), 142–151. https://doi.org/10.47772/IJRISS.2024.809012
Ahiakwo, M. J., & Isiguzo, C. Q. (2015). Students’ conceptions and misconceptions in chemical kinetics in Port Harcourt Metropolis of Nigeria. African Journal of Chemical Education, 5(2), 112–130.
Al-Balushi, S. M., & Al-Abdali, N. S. (2019). Using technology-supported learning to enhance inquiry and address students' learning difficulties in science.
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, 977–997. https://doi.org/10.1007/s11165-024-10176-3
Ayoade, A. A. (2019). Assessment practices and the identification of students' misconceptions and learning difficulties.
Basuki, K. H. (2020). Analisis Kesalahan Mahasiswa dalam menyelesaikan soal stoikiometri. Journal Ilmiah Wahana Pendidikan, 6(4), 641–651. https://doi.org/10.5281/zenodo.4298027
Cooper, M. M., Stieff, M., & others. (2023). Comparing the performance of college chemistry students with ChatGPT for calculations involving acids and bases. Journal of Chemical Education, 100(10), 3934–3944. https://doi.org/10.1021/acs.jchemed.3c00500
Federal Republic of Nigeria. (2019). National policy on education (6th Ed.). NERDC Press.
Holmes, W., Wieman, C., & Bonn, C. (2023). Artificial intelligence in education: Opportunities and challenges for teaching, learning and assessment.
Jacobsen, M., & McKenney, S. (2024). Research and development in education: Designing, developing, testing and refining educational innovations.
Kieser, J., et al. (2024). ChatGPT as an instructor’s assistant for generating and scoring exams. Journal of Chemical Education, 101(9), 3780–3788. https://doi.org/10.1021/acs.jchemed.4c00231
Kingsley, C. J. O., Uthman, M., Bebeji, I. A., & Udeh, P. C. (2025). Fault diagnosis system using CNN-LSTM networks. UNIABUJA Journal of Engineering and Technology, 2(2), 224–232.
Krenn, B., et al. (2020). Artificial intelligence, adaptive learning and intelligent assessment in STEM education.
Nwani, O. E. (2019). Chemistry education and its relevance to national development.
Olubunmi, O. A., & Aarinola, A. A. (2022). Challenges affecting effective science and Chemistry teaching in Nigerian secondary schools.
Oladejo, M. A., et al. (2022). Concept difficulty in secondary school chemistry: An intra-play of gender, school location and school type. Journal of Technology and Science Education.12 (1), 20-24
Reinhold, P., et al. (2005). Evaluating students’ conceptual understanding of balanced equations and stoichiometric ratios using a particulate drawing. Journal of Chemical Education.
Rusek, M., et al. (2026). Challenges in chemistry calculations at university entry: Evidence from a multi-institutional study. Journal of Chemical Education, 103(4), 1826–1834. https://doi.org/10.1021/acs.jchemed.6c00022
Rusek, M., Vojíř, K., Bártová, I., Klečková, M., Sirotek, V., & Štrofová, J. (2022). To what extent
do freshmen university chemistry students master chemistry calculations? Acta Chimica Slovenica, 69(2), 371–377. https://doi.org/10.17344/ACSI.2021.7250
Roy, P., Poet, H., Staunton, R., Aston, K., & Thomas, D. (2024). ChatGPT in lesson preparation: A teacher choices trial. Education Endowment Foundation.
OECD. (2024). Education policy outlook 2024: Reshaping teaching and learning from ABCs to AI. OECD Publishing. https://doi.org/10.1787/dd5140e4-en
Organisation for Economic Co-operation and Development. (2021). OECD digital education outlook 2021: Pushing the frontiers with artificial intelligence, blockchain and robots. OECD Publishing.
Sanusi, T. (2025). Artificial intelligence integration in STEM education: Personalized learning and assessment.
Samaila, K., Abdulfattah, K., Babatunde, O. M., & Akindele, N. A. (2024). Teachers’ awareness and readiness to use AI assessment methods in Kwara State, Nigeria. International Journal of Innovative Technology Integration in Education, 7(2).
Scott, F. J. (2012). Is mathematics to blame? An investigation into high school students’ difficulty in performing calculations in chemistry. Chemistry Education Research and Practice, 13, 330–336. https://doi.org/10.1039/C2RP00001F
UNESCO. (2023). Guidance for generative AI in education and research. UNESCO.
Upahi, J. E., & Olorundare, A. S. (2012). Difficulties faced by Nigerian senior school chemistry students in solving stoichiometric problems. Journal of Education and Practice, 3(12). 31-35
Tadese, Z. B., Nimani, T. D., Mare, K. U., Gubena, F., Wali, I. G., & Sani, J. (2025). Exploring machine learning algorithms for predicting fertility preferences among reproductive age women in Nigeria. Frontiers in Digital Health, 6, Article 1495382. https://doi.org/10.3389/fdgth.2024.1495382
Taskin, V., & Bernholt, S. (2014). Students’ understanding of chemical formulae: A review of empirical research. International Journal of Science Education, 36(1), 157–185. https://doi.org/10.1080/09500693.2012.744492
Weegar, R., & Idestam-Almquist, P. (2024). Reducing workload in short answer grading using machine learning. International Journal of Artificial Intelligence in Education, 34, 247–273. https://doi.org/10.1007/s40593-022-00322-1
Yang, Y., Huang, F., & Liang, Z. (2024). Effects of reduction on teacher’s teaching load in the lesson development of AI-led instruction: A pilot study. Journal of East China Normal University (Educational Sciences), 42(2), 46–62. https://doi.org/10.16382/j.cnki.1000-5560.2024.02.004
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