Impact of Motivation on Students’ Cognitive Abilities in Using Generative Artificial Intelligence for Learning

Abstract

Generative AI, such as ChatGPT and similar platforms, offers students innovative ways to engage in problem-solving, improve their writing, and develop critical thinking skills. While AI technologies have the potential to revolutionize education, the influence of motivation on their effectiveness remains underexplored. This study explores the influence of motivation on the cognitive abilities of college students using Generative Artificial Intelligence (GenAI) for learning, focusing on students in the Municipality of Bansalan. The research employed a quantitative, descriptive correlational design, utilizing a survey administered via Google Forms to collect data on students' motivation and cognitive abilities related to GenAI use. The participants of this study were college students, and the sample size consisted of 206 respondents. Motivation was assessed through six factors: personal enjoyment, integration with learning identity, personal development goals, self-expectations, external motivators, and uncertainty about AI use. Cognitive abilities were measured in terms of focus and attention, processing and interaction skills, and memory and recall abilities. Stratified random sampling was used to ensure diverse representation from various colleges. Data analysis revealed a weak positive correlation (r = 0.387) between motivation and cognitive abilities, indicating that higher motivation levels tend to enhance cognitive performance. Additionally, linear regression analysis showed that self-expectations and uncertainty about AI use significantly influenced cognitive abilities, explaining 21.80% of the variance. Regarding the level of cognitive abilities in using generative AI for learning, the overall mean score of 3.10 (SD = 0.455) suggested that students perceive their cognitive abilities as neither highly effective nor inadequate, indicating room for improvement in leveraging AI for learning purposes. Regarding variability, constructs like Focus and Attention had low SD values (e.g., 0.530), suggesting consistent responses, whereas items such as PE3 (SD = 0.904) demonstrated higher variability, indicating diverse participant perspectives on enjoyment-related items. The study highlights the importance of motivation in maximizing the cognitive benefits of GenAI, providing insights for educators to optimize AI integration in learning environments.