An Investigation into AI-Assisted Aspect-Based and Integrated English Teaching Approaches Using ADLL and CPCT Theories

Abstract

This study investigated the comparative effectiveness of AI-assisted aspect-based and integrated English teaching approaches, grounded in Aspectual Diversification in Language Learning Theory (ADLL) and Curriculum Practice Convergence Theory (CPCT). It aimed to determine their effects on students’ proficiency, engagement, and curriculum-practice alignment. A quasi-experimental design was employed. The population comprised 400 SS2 students from two secondary schools in Akoko South East, Ondo State, Nigeria, with a sample of 120 students selected using stratified random sampling. A panel of three expert language educators and curriculum measurement specialists was constituted from Landmark College to ensure construct and content validity. The ELAT essay and writing sections were scored using a standardized, analytical 100-point rubric spanning four core criteria (25 points each): Grammar and Expression, Content Development, Organization of Ideas, and Critical Writing. Data were collected through pre-tests, post-tests. AI-generated analytics, classroom observations, and questionnaires, were analyzed using descriptive and inferential statistics. Findings revealed that AI-assisted aspect-based instruction significantly improved discrete language skills, particularly grammar and vocabulary, while AI-assisted integrated instruction enhanced overall communicative competence and contextual language use. The study concluded that a hybrid AI-assisted instructional model offers the most effective approach to English language teaching.