THE FLIPPED AI TFL CLASSROOM FRAMEWORK

Abstract

This paper examines the systemic instructional issues present in low-resource classrooms that may be alleviated by the application of the Flipped AI TFL Classroom Framework. This study builds on Kolegova and Levina’s (2024) findings that AI capitalized on efficiency through an automated component of language plan implementation. In addition, it takes into consideration Dugošija’s (2024) findings that several constraints remain for the instruction of foreign languages. The reasoning for this educational deduction is the 3-Point Strategic Investment Plan to reinvest the time savings that AI-infused learning can produce for classroom targeted improvements. These targeted improvements aim to combat areas such as content equity, learner value, and educator burnout, along with the strengthening of Crompton et al.’s (2024) establishment of capacity building within a strong AI-supported context. If established, it is argued, as a framework, AI will explore and liberate cognitive, emotional, and logistical faculties for educators. In turn, focused attention to structural constraints as opposed to routine tasks in ALL TFL contexts. This representation has justified the reinvestment of AI-improved efficiencies to enhance instruction, learner engagement, as well as sustainability for low resource TFL practices.