AI-ENHANCED SOCIAL–EMOTIONAL LEARNING IN EFL EDUCATION: A HOLISTIC APPROACH TO FOSTERING STUDENT ENGAGEMENT AND PROMOTING EMOTIONAL WELL-BEING

Abstract

Background:  The study investigates the evolving role of artificial intelligence (AI) in transforming social emotional learning (SEL) models to support English as a Foreign Language (EFL) learners with Pakistani educational landscape. The rise of digital technology in the educational sphere leads to the fact that AI not only redesigns the way students learn about the linguistic domain but also the way they learn in terms of social and emotional aspects of their learning. The systems of AI are currently being developed to meet the affective needs of students and thus adding some value towards emotional resilience, social flexibility and overall well-being in the learning contexts.

Objectives: The present research seeks to explore the effect of AI-boosted SEL on the two most important outcomes of student development, which are their emotional well-being and academic engagement. It investigates the benefits of using AI tools in regulation of emotions, motivation, sustained attention, and perseverance among the learners of EFL. With the help of a study of the interaction between AI-based emotional support systems and student engagement.

Methods: The sample size consisted of 1000 undergraduate and postgraduate EFL learners, which were selected in line with various universities within all the provinces of Pakistan. The study employed the convenience sampling method, which allowed a representative sample of the student body to be ensured despite being diverse and in some instances, difficult to reach. Their learning experience was presented with a wide range of AI-enhanced learning environments, such as personalized learning adapted by the platform, and emotionally aware tutoring systems. Structural equation modeling which is essential in terms of robust assessment of the nature of the variables among key constructs used SPSS to analyze quantitative data based on both descriptive statistics and regression models, and AMOS to do the structural equation modeling.

Results: The findings indicated a positive relationship with significant statistical values between AI-integrated SEL interventions and improved outcomes of a student. Students who used AI-facilitated platforms noted that they had a better emotional balance, more focus and increased academic motivation. Such systems allowed the emotional adaptations of feedback, situation-based learning routes, and personal provision systems, which resulted in increased academic involvement and preparedness and emotions.

Conclusion: The study supports the transformative power of AI-enhanced SEL models that can result in an emotionally intelligent, student-centered learning setting with the EFL students. AI tools are also a more comprehensive and reactive pedagogical model that can provide emotional and cognitive support as a part of language training. The psyche has significant implication to the policy makers in the education sector and curriculum developers, who should encourage the use of AI-PSI emotional intelligence strategies in educational curriculum as a starting point in the training and teaching of modern languages.