<![CDATA[The utilization of artificial intelligence in learning is growing, one of which is through the use of a Generative Pre-trained Transformer (GPT) as a learning tool. This study aims to analyze the relationship between student motivation, response quality perspective, and frequency of GPT use in chemistry learning. This study uses a quantitative approach with multiple regression methods to predict the frequency of GPT use based on two predictor variables, namely motivation and response quality evaluation. Data were collected through a survey of 58 students from chemistry and chemistry education study programs at the undergraduate, master's, and doctoral levels. Correlation analysis results showed a significant positive relationship between motivation and response quality evaluation (r(56) = .59, p < .001) and between response quality evaluation and frequency of GPT use (r(56) = .49, p < .001). However, the relationship between motivation and frequency of GPT use was weaker (r(56) = .32, p = .015). Regression analysis showed that evaluation of response quality significantly predicted the frequency of GPT use (β = .57, p < .001), whereas motivation had a smaller effect (β = .21, p < .01). The R² value of .25 indicated that 25% of the variability in the frequency of GPT use could be explained by both predictor variables. This finding suggests that although motivation has a role in the use of GPT, college students are more likely to use it when they rate the response quality as high. The implications of the results of this study can be the basis for developing strategies to increase the utilization of GPT in chemistry learning as well as academic policies related to the use of AI in education.]]>
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