Title: Innovations and Discoveries in the Multidisciplinary Research
Chief Editor: Dr. Padmavathi S. M.
Associate Editor: Dr. Poonam Sachin Kadlag
Co-Editor: Dr. Shailaja A Akkur
ISBN: 978-81-69857-36-9
Chapter: 8
DOI: https://doi.org/10.59646/785/8
Authors: P. Esther Ramola, P. Nirmal, and S. Jeyamalathi
Abstract
The use of artificial intelligence in language learning has made possible an extremely individualistic learning experience that changes based on the personal traits of individual learners. Nonetheless, the efficiency of these systems does not strictly depend only on the level of technological complexity, but also on the ability of learners to control themselves. This paper discusses self-regulation in AI-personalized language learning procedures using a well-designed survey method. The research design used was a quantitative research design with the use of a Likert-scale questionnaire that captured the perceptions, behaviours and patterns of engagement of the learners with self-regulated learning. The information was gathered among the language learners with a prior background of exposure to AI-based platforms, with evaluation on the dimensions of goal setting, strategic planning, self-monitoring, and reflective evaluation. The patterns and relationships between self-regulation and perceived learning effectiveness were determined using descriptive and inferential statistics. The results show that learners who exhibited high degrees of self-regulatory behaviours reported more engagement, better learning consistency as well as perceived outcomes in AI-mediated settings. Conversely, the learners who had low self-regulation showed lesser interaction and low levels of satisfaction. These findings imply that self-regulation is an essential mediating variable to make AI-personalized systems as pedagogic as possible. The paper highlights that the need to incorporate the metacognitive strategy training into AI-based language learning platforms is essential to facilitate learner autonomy and continued interest. The implication is that it applies to educators, instructional designers and developers who want to maximize adaptive learning conditions to the needs of different learner populations.
Keywords: Self-regulation, AI-based language learning, personalized learning, learner autonomy, Likert-scale survey, metacognitive strategies, adaptive learning systems