Collaborative Methodology by Integrating Students, AI and Teacher to Teach English Literature for Active and Reflective Learning

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.