Reflections of Artificial Intelligence Use on Student Achievement and Attitudes in Biology Classes

This study investigated the topic of enzymes using current artificial intelligence tools such as ChatGPT, Gemini Pro, and Microsoft Copilot and examined the effects of this approach on student achievement and attitudes. A quasiexperimental design was used, with pretest and posttest applications to both experimental and control groups. The sample consisted of 45 students (29 girls, 16 boys) in the ninth grade (16-17 years old) of a high school affiliated with the Ministry of National Education (MoNE). Data were collected using the Enzyme Academic Achievement Test and the Biology Course Attitude Scale. The SPSS statistical program was used to analyze the data. After determining that the data showed a normal distribution, parametric statistical tests were used to examine whether there were significant differences between the experimental and control groups. Independent samples t tests were used for comparisons between groups, and dependent samples t tests were used to evaluate the change in pre- and post-application scores for each group. The study results showed that students in the experimental group had higher academic achievement than those in the control group, and their posttest scores increased significantly compared to their pretest scores. The effect size coefficient for the difference between the groups' posttest achievement scores was calculated as Cohen’s d = 1.26, indicating that the implemented method had a large effect on academic achievement; furthermore, the experimental group's attitudes toward biology improved significantly.