Even with significant progress in educational technology and artificial intelligence (AI), most institutions of higher education still rely extensively on teacher-centered learning in English literature classes, which restricts students to opportunities of engagement, critical thinking, and reflective learning. The proposed research is a literature review that hypothesizes and analyses a collaborative form of learning that effectively incorporates students, teachers, and AI in order to optimize reflective and skills-based learning. The mixed-method design allowed the collection of the quantitative data (through the use of a 30-item Likert-scale questionnaire distributed to 151 undergraduate students) and the qualitative information (114 open-ended responses). Patterns of student satisfaction and engagement were analyzed by using supervised machine-learning models such as SVM, Logistic Regression, Decision Tree, and Random Forest. The findings suggest that there is a high level of mean scores of teaching practices, skill development, collaboration, and reflection which are identified by positive sentiment polarity of the qualitative responses. The predictive accuracy of machine-learning models was also quite high (as high as 92%), which proves the reliability of the trends in learner satisfaction. The results indicate that an AI-assisted collaborative model is effective to enhance engagement, reflective learning, and development of analytical skills in literature classes and can be used as a pedagogical framework to scale to higher education.
Jawaid et al. (Thu,) studied this question.