Assessing the Psychological Impact of Generative AI on Data Science Education: An Exploratory Study

Abstract

The integration of AI in education, particularly through Large Language Models (LLMs), is accelerating, reshaping pedagogical approaches and student interactions with new learning tools. This study assesses the impact of generative AI on the educational experiences of data science students at the Center for Informatics, University of Paraíba (CI/UFPB), Brazil. Through the validation of five psychometric scales, we analyze the students' acceptance of LLMs, their associated burnout levels, technology anxiety and the prevalence of metacognitive and dysfunctional learning strategies. Results indicate a significant adoption of AI-driven technologies, with a low incidence of technology anxiety, such as fear of job displacement due to AI. However, a significant correlation between burnout and dysfunctional learning strategies was observed, which could likely be attributed to the rigorous academic environment. Additionally, the employment of metacognitive strategies in conjunction with LLMs reflects an advanced learning approach, yet challenges with functional learning strategies persist. This study contributes to the discourse on AI in education, highlighting the need for educational frameworks that support effective AI adoption while addressing the psychological demands on students.

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