Assessing the Influence of AI-Supported Learning Methods on Instructional Quality in Higher Education

Abstract

Ensuring high instructional quality in higher education remains a significant challenge, particularly in language instruction for Year One undergraduate students transitioning from secondary to tertiary learning environments. In English–French programmes, learners often face difficulties related to limited individualised feedback, varying language proficiency levels, and reduced engagement, which can hinder effective learning. With the growing integration of Artificial Intelligence (AI)-supported learning methods in higher education, there is a need for empirical evidence on their contribution to instructional quality, especially in early undergraduate French language courses. This study employed a quantitative descriptive–correlational research design to investigate the impact of AI-supported learning methods on perceived instructional quality among 43 Year One undergraduate students enrolled in English–French (French modules) programmes. Data were collected using a structured 5-point Likert-scale questionnaire and analysed using descriptive statistics, Pearson correlation, and simple linear regression analysis. The findings revealed a strong and statistically significant positive relationship between AI-supported learning methods and instructional quality (r = 0.68, p < 0.001). Regression results further indicated that AI-supported learning methods significantly predicted instructional quality (β = 0.64, p < 0.001), accounting for 46% of the variance. These results demonstrate that AI-supported learning methods play a substantial role in enhancing teaching clarity, feedback quality, learner engagement, and overall instructional effectiveness in higher education French language instruction. The study concludes that integrating AI technologies can meaningfully improve instructional quality and supports their informed adoption in tertiary language education.