Estrategias de evaluación del inglés en plataforma virtual: consideraciones del uso de inteligencia artificial en el rendimiento académico del estudiantado - Alfaro Salas, Hazel Viviana (Revista Educación, 2025)
The aim of this research was to examine how assessment strategies can enhance English learning on virtual platforms while mitigating the potential negative effects of students' reliance on AI tools for answering questions. A quantitative, descriptive-correlational, and experimental methodology was employed, wherein assessment strategies were adjusted to evaluate their impact on students' performance. The sample comprised 33 university students (23 women and 10 men) enrolled in an intermediate English course. Written and oral comprehension assessment strategies were applied, alongside a questionnaire addressing the use of ChatGPT as a learning aid. Data analysis was conducted using mean contrast tests (Student’s t-test) and Pearson’s correlation. Results indicated higher scores on the written exam based on reading comprehension and selection tasks, which decreased when selection tasks were replaced by word-dragging activities within images. Additionally, written exam scores were higher than oral exam scores, suggesting that students may leverage AI tools to complete written assessments. The observed differences between average scores across assessment strategies highlight the need for educators to consider students’ use of AI tools such as ChatGPT in the evaluation process. Significant differences were identified between written and oral exam results, as well as between the two assessment strategies, with a notable correlation between ChatGPT use and performance on selection-based written exams. These findings underscore the risk of misinterpreting students’ actual progress in English learning and emphasize the importance of promoting a more pedagogically sound use of AI tools to serve as knowledge mediators rather than merely instruments to pass courses.