Emotion Recognition in AI-Powered Classrooms Impacts on Student Engagement and Learning Outcomes

This study allowed for the investigation of the probable effects of integrating emotion detection technologies into AI-based classroom instruction on academic learning results, faculty motivation, and student engagement at East Pakistani higher education institutions. The quantitative, correlational, and predictive study design was used and simple random sampling was utilized to enable the collection of data amongst a sample of 250 undergraduate and postgraduate students. Analysis of the data used descriptive stats, Pearson correlation, simple linear regression and independent samples t-test. The results indicated that there is moderately strong, positive, and significant relationship between emotion recognition technologies and student engagement. Regression analysis ensured that emotion-sensitive AI systems were significant in predicting the academic learning of students, which helped in explaining considerable proportion variances. Also, a strong correlation between emotional responsiveness of AI-based classrooms and the level of student motivation was witnessed. These findings altogether demonstrate the significance of incorporating emotion recognition features into AI-augmented learning spaces so as to achieve humanization of learning by involving it in the development of an emotionally responsive learning process that is student-oriented. Alongside this, the paper has also pointed towards the necessity of employing ethically set principles regarding the use of emotional data, culturally aware AI algorithms, as well as educator training on the interpretation of emotional analytics.