DeepL as Scaffolding in Academic Writing: A Zone of Proximal Development Theoretical Review

Abstrak

This study aimed to investigate the potential of DeepL as a scaffolding tool for English as a Foreign Language (EFL) learners in the context of academic writing. Amidst the ethical debate regarding the use of Artificial Intelligence (AI) in education, this article offers an alternative perspective using Vygotsky's Zone of Proximal Development (ZPD) theoretical lens. The method used was a comparative conceptual analysis and critical literature synthesis of 20 published articles (2021–2025) to map DeepL's functions in reducing students' cognitive load. The theoretical findings indicate that DeepL meets the criteria as an artificial More Knowledgeable Other (MKO) capable of providing micro-scaffolding on lexicogrammatical aspects. Through the proposed "Write-Translate-Compare" integration model, guided use of DeepL allows students to produce texts above their current competence level, which are gradually internalized into independent competence. The conclusion suggests a paradigm shift from total prohibition to critical AI literacy, where the teaching focus shifts from pure text production to managing technological assistance for sustainable learning.