Assessing the Influence of AI-Generated Responses on Academic Achievement: An Ethical Perspective and Self-Efficacy of Delta State Secondary School Students, Nigeria
DOI:
https://doi.org/10.70232/jrmste.v3i4.72Keywords:
AI-Generated Response, Ethical Perspective, Students’ Achievement, Quadratic Equations, Self-EfficacyAbstract
This study examined the influence of AI-generated responses on students’ mathematics achievement and self-efficacy. Using a quasi-experimental design with two control groups and two experimental groups, the study employed three research questions and three hypotheses. 1,369 Senior Secondary School I (SSS I) students from public schools in Ughelli North Local Government Area of Delta State made up the population. A sample of 115 students, consisting of two intact classes chosen by simple random sampling, was drawn from two randomly selected coeducational schools. School A served as the experimental group with 62 students (43 males and 19 females), while School B served as the control group with 53 students. Data were gathered using two instruments: the “Students’ Questionnaire on Ethical Impacts of AI-Generated Responses on Students’ Achievement in Solving Quadratic Equations” (SQEIAIGRASQ) and the Quadratic Equation Achievement Test (QEAT). Both instruments were validated by mathematics teachers and measurement and evaluation experts, and the required corrections were made. Reliability testing showed a high reliability index. The mean and standard deviation were used to answer the research questions, while independent-samples t-tests were employed to test the null hypotheses at a 0.05 level of significance. The findings revealed that students exposed to AI-generated responses achieved higher performance than those taught through conventional teaching methods. Furthermore, AI-generated responses positively influenced the self-efficacy of students in solving mathematics problems. The practical implication of these findings is that teachers should integrate AI-generated responses into instruction while providing their own input, and students should also be encouraged to engage with these tools responsibly. Based on the findings, it was recommended that students be encouraged to use AI-generated responses while observing ethical guidelines in their use and contribution to AI-supported learning outcomes.
References
Agbadobi, P. U. (2023). Mathematics and gender dichotomy using item statistics. European Modern Studies Journal, 7(2), 99-109. https://doi.org/10.59573/emsj.7(2).2023.09
Amalu, M. N. (2016). Gender differences in attitude towards mathematics in Nigerian secondary schools. Sophia: An African Journal of Philosophy, 17(1), 191-197. Retrieved from https://www.researchgate.net/publication/331865161_Gender_differences_in_attitude_towards_mathematics_in_Nigerian_secondary_schools
Anokye Poku, D. & Ampadu, E. (2020). Gender differences in attitudes and achievement in Mathematics among Ghanaian JHS students. International Journal of Education, 12(3), 84-95. https://doi.org/10.5296/ije.v12i3.17136
Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. In J.A. Larusson & B. White (Eds.), Learning analytics (pp. 61-75). Springer. https://doi.org/10.1007/978-1-4614-3305-7_4
Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215. https://doi.org/10.1037/0033-295X.84.2.191
Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman.
Binns, R. (2022). Fairness in machine learning: Lessons from political philosophy. In Proceedings of the 2022 Conference on Fairness, Accountability, and Transparency (pp. 38-53). https://proceedings.mlr.press/v81/binns18a/binns18a.pdf
Bond, M., Buntins, K., Bedenlier, S. (2020). Mapping research in student engagement and educational technology in higher education: a systematic evidence map. International Journal of Educational Technology in Higher Education, 17, Article 2. https://doi.org/10.1186/s41239-019-0176-8
Bulut, O., Beiting-Parrish, M., Casabianca, J. M., Slater, S. C., Jiao, H., Song, D., Ormerod, C. M., Fabiyi, D. G., Ivan, R., Walsh, C., Rios, O., Wilson, J., Yildirim-Erbasli, S. N., Wongvorachan, T., Liu, J. X., Tan, B., & Morilova, P. (2024). The rise of artificial intelligence in educational measurement: Opportunities and ethical challenges. Chinese/English Journal of Educational Measurement and Evaluation, 5(3), Article 3. https://doi.org/10.59863/MIQL7785
Chaudhry, M. A., Cukurova, M., & Luckin, R. (2022). A transparency index framework for AI in education. arXiv. https://doi.org/10.48550/arXiv.2206.03220
Diri, E. A. & Oladayo, C. E. (2025). Ethical impacts of AI-generated feedback on students’ performance and self-efficacy in quadratic equation in Orlu Local Government Area, Imo State. Nigeria. FUO-Journal of Educational Research, 73-81. https://doi.org/10.5281/zenodo.17232065
Diri, E. A., & Ogiga, E. (2025). Dice games on students’ academic achievement in concept of probability in Bayelsa State, Nigeria. FUO-Journal of Educational Research, 4(2), 99-106. https://doi.org/10.5281/zenodo.14885866
Ehindero, O., Adeleke, M., Oloyede, E., & Ajibade, Y. (2010). Looking at gender disparity in science and mathematics from other angles: Are there differences in logical reasoning and linguistic abilities? African Journal of Educational Studies in Mathematics and Sciences, 7(1), 35–44. https://doi.org/10.4314/ajesms.v7i1.61571
González-Betancor, S. M., López-Puig, A. J., & Cardenal, M. E. (2021). Digital inequality at home. The school as compensatory agent. Computers & Education, 168, 104195. https://doi.org/10.1016/j.compedu.2021.104195
Hao, J., von Davier, A. A., Yaneva, V., Lottridge, S., von Davier, M., & Harris, D. J. (2024). Transforming assessment: The impacts and implications of large language models and generative AI. Educational Measurement: Issues and Practice, 43(2), 16−29. https://doi.org/10.1111/emip.12602
Henkel, O., Horne-Robinson, H., Kozhakhmetova, N., & Lee, A. (2024). Effective and scalable math support: evidence on the impact of an AI-tutor on math achievement in Ghana. ArXiv. https://arxiv.org/abs/2402.09809
Huang, L., Zheng, J., Lajoie, S. P. (2024). Examining university teachers’ self-regulation in using a learning analytics dashboard for online collaboration. Education and Information Technologies, 29, 8523–8547. https://doi.org/10.1007/s10639-023-12131-7
Joshua, M. R., Elizabeth, H. S., Melissa, K. K., Aaron, R., Omiya, S. (2022). Big data, big changes? The technologies and sources of data used in science classrooms. British Journal of Educational Technology, 53(5), 1179-1201. https://doi.org/10.1111/bjet.13245T
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2021). Intelligence unleashed: An argument for AI in education. Pearson Education.
OECD (2020). Education at a glance 2020: OECD indicators. OECD Publishing. https://doi.org/10.1787/69096873-en
Ogoke, C. J., Anyanwu, A., Okolie, A., Onuoha, E., & Vivian, D.-O. (2025). Application of artificial intelligence in the teaching and learning of mathematics: Implication for achievement and retention. Eureka: Journal of Educational Research, 3(2), 136–143. https://doi.org/10.56773/ejer.v3i2.62
Oladayo, C. E. (2025). Effects of instructional materials on students’ academic performance in mathematics in Emohua Local Government Area, Rivers State. Nigeria. FUO-Journal of Science Education and Multidisciplinary Research, 1(1), 239-249. https://doi.org/10.5281/zenodo.17633952
Perkins, M., Furze, L., Roe, J., & MacVaugh, J (2024). The Artificial Intelligence Assessment Scale (AIAS): A framework for ethical integration of generative AI in educational assessment. Journal of University Teaching and Learning Practice, 21(6). https://doi.org/10.53761/q3azde36
Ryan, R. M., & Deci, E. L. (2017). Self-determination theory: Basic psychological needs in motivation, development, and wellness. Guilford Press. https://doi.org/10.1521/978.14625/28806
Schiff, D. (2021). Out of the laboratory and into the classroom: The future of artificial intelligence in education. AI & Society, 36(1), 331-348. https://doi.org/10.1007/s00146-020-01033-8
Schunk, D. H., & Zimmerman, B. J. (2012). Motivation and self-regulated learning: Theory, research, and applications. Routledge. https://doi.org/10.4324/9780203831076
Stewart, J. (2021). Calculus: Early transcendentals (9th ed.). Cengage Learning.
Street, K. E. S., Malmberg, L. E., & Schukajlow, S. (2024). Students’ mathematics self-efficacy: A scoping review. ZDM – Mathematics Education, 56, 265–280. https://doi.org/10.1007/s11858-024-01548-0
Thomas, G. B., Weir, M. D., Hass, J., & Giordano, F. R. (2020). Thomas’ calculus (14th ed.). Pearson.
Udonsa, A. E. (2015). Gender disparity in mathematics performance among secondary school students in Adamawa State, Nigeria. Journal of Sociology, Psychology and Anthropology in Practice, 7(1), 49–56. Retrieved from https://icidr.org.ng/index.php/Jspap/article/view/1643
UNESCO (2024). Global education monitoring report 2024: Technology in education. UNESCO Publishing. Retrieved from https://www.unesco.org/gem-report/en/publication/tecnologia-en-la-educacion-una-herramienta-nuestra-medida-2024
Yang, Y., Maeda, Y., & Gentry, M. (2024). The relationship between mathematics self-efficacy and mathematics achievement: Multilevel analysis with NAEP 2019. Large-scale Assessments in Education, 12, Article 16. https://doi.org/10.1186/s40536-024-00204-z
Yin, Z., Chinta, S. V., Wang, Z., Gonzalez, M., & Zhang, W. (2024). FairAIED: Navigating fairness, bias, and ethics in educational AI applications. ArXiv. https://arxiv.org/abs/2407.18745
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