Adaptive AI in EFL Higher Education Instructional Contexts: Credibility and Students’ Learning Engagement
As Generative Artificial Intelligence (GenAI) tools collect data from both reliable and unreliable sources across the internet, concerns have been raised about the credibility of the output content learners are exposed to when seeking these tools for assistance. To address this problematic issue, our study tests the development of a pedagogical assistant trained on a personalized course framework; Besides its equipment with content that aligns with classroom lectures, the virtual assistant was given a comprehensive set of instructions guiding its behavior; Restricting its responses to the provided course material, forbidding the provision of information from any external sources as an initial action to battle against information credibility issues, and limiting its interactions to course-related discussions to promote engagement and mitigate distractions. The study explores the perceived credibility of the tested agent alongside the perceived impact on students’ learning engagement. This study is significant in informing the design of credible, curriculum-aligned AI assistants for EFL learning contexts. To achieve the required results, our study adopts DeLone & McLean’s theoretical framework alongside a quantitative research design with a structured questionnaire as a data-gathering tool. The sample of this study consists of N = 63 students of the Higher School of Teachers, Moulay Ismail University. Data were analyzed using the Statistical Package for the Social Sciences (SPSS) version 25. Students exhibited positive perceptions towards the custom agent, which they perceived as an engaging and credible source of information that also aligns with the course content they are exposed to during formal lectures. Our findings also revealed a strong correlation between Perceived Impact on Learning Engagement (PILE) and Perceived Credibility (PC), with r (61) = .780. The study acknowledges some limitations and offers recommendations for future studies.