Ethical Impacts of AI-Generated Feedback on Students' Performance and Self-Efficacy in Quadratic Equation in Orlu Local Government Area, Imo State. Nigeria.
Abstract This study aimed to examine the ethical impacts of AI-generated feedback on students’ performance and self-efficacy in mathematics education. The study employed a quasi-experimental research design. Three (3) research questions and two (2) hypotheses guided the study. Population constituted 1,342 SSS II students in public senior secondary schools in Orlu Local Government Area of Imo State. With a sample size of 134 students from 2 sampled coeducational schools selected using simple random sampling technique. Students’ questionnaire titled: Students’ Questionnaire on Ethical Implications of AI Generated Feedback on Students’ Performance and Self-Efficacy in Solving Quadratic Equations (SQEIAIGFPSQ) as well as a Quadratic Equation Achievement Test (QEAT) were utilized as instruments for data collection. They were validated by experts in mathematics, measurement and evaluation, and their corrections were effected. Reliability of the instruments were calculated using Pearson product Moment Correlation Coefficient (PPMCC) which yielded reliable indices of 0.79 and 0.76 respectively which indicated a high reliability indices. Mean and standard deviation were used to answer the research questions while t-test was used to test the null hypotheses at 0.05 level of significance. The results showed that students in the experimental group taught using AI-generated feedback outperformed their counterpart in the control group who were taught using conventionally generated feedback. Based on the results, recommendations were made by the researchers among others that students should be encouraged to use AI-generated feedback, but should observe the ethical considerations of working on, and contributing to the generated outcomes.
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2025-06-01
- DOI
- https://doi.org/10.5281/zenodo.17232064
- Primary Topic
- Data Mining Algorithms and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00