A Comparison of Automated Corrective Feedback and Traditional Corrective Feedback: A Review Study

Abstract

Corrective feedback (CF) is often used to help language learners identify and correct errors in their spoken or written language. Traditional CF in this paper refers to teacher feedback, peer feedback, and self-feedback. Automated corrective feed-back (ACF) indicates the use of technology, specifically artificial intelligence (AI) systems, to provide feedback to learners on their performance or work. This paper compared ACF and traditional CF through a review based on these four aspects: response time of feedback, potential risks, interpersonal interaction, and personalized learning, aiming to assist teachers in comprehending the use of technical tools and enhancing learners' English proficiency. ACF has the benefits of instant response time, minimal emotional damage, and individualized feedback. Whereas traditional CF has the benefits of real-time interpersonal interaction and no concerns about privacy exposure. It is recommended to combine the two modes of feedback so as to enhance the effectiveness and efficiency of language learning.