Developing English Reading Materials Using Local Content Through Teachy AI: A Systematic Review of Empirical Studies

Abstract

This systematic review aims to examine the development of local content-based English reading instructional materials and the integration of artificial intelligence (AI), specifically Teachy AI, in enhancing English as a foreign language (EFL) learning. Fifteen empirical articles published between 2015 and 2025 were systematically collected through Google Scholar, Scopus, ResearchGate, ERIC, and Springer Open. The criteria included research focused on reading instructional materials, local cultural integration, and the use of AI in learning. The findings reveal three main trends. First, reading instructional materials developed from local content have been shown to improve students' motivation, comprehension, cultural awareness, and contextual engagement. Second, the use of AI-based tools facilitates learning personalization, material adaptation, and innovative teaching practices, resulting in a more interactive and student-centered learning process. Third, a combined approach of local content and AI support has the potential to enrich reading pedagogy in EFL contexts. However, limitations remain, including a lack of research and limited studies on broad-scale implementation across various educational contexts. In conclusion, locally based reading instructional materials with AI support can enhance both effectiveness and cultural relevance in EFL learning. Further studies need to explore its long-term implementation and integration into the national curriculum.