Adoption of Large Language Models in Programming Education: Student Views, Concerns, and Perceived Use in Mexican Higher Education
Academic Article in Scopus
Large language models (LLMs) have witnessed rapid adoption across academic and industrial domains due to their advanced capabilities, including code generation, natural language processing, and interactive support. Their integration into educational settings, particularly in programming has introduced both substantial benefits and emerging ethical challenges. The current study investigates students¿ perceptions of LLM usage at two different public and private universities in Mexico, focusing on how ease of use influences their perceived effectiveness, confidence, future intention, and frequency of use in programming-related tasks. A quantitative, survey-based approach was adopted, with data collected from undergraduate and postgraduate students actively. The findings revealed that the ease of use plays a significant role in shaping students¿ attitudes and behaviors. Path analysis using Partial Least Squares Structural Equation Modeling (PLS-SEM) confirmed strong relationships between usability and perceived outcomes, suggesting that intuitive tool design can enhance confidence and increase regular usage. However, the study also highlights ethical concerns among students, including overreliance and potential academic dishonesty. These insights underscore the need for practical guidelines and institutional policies to ensure responsible and balanced integration of LLMs in academic environments. This research offers important implications for educators, developers, and policymakers working to align technological innovation with ethical learning practices.
