Artificial Intelligence and Second Language Learning: An Efficient Approach to Error Remediation

Abstract

While theoretical approaches to error correction vary in the second language acquisition (SLA) literature, most sources agree that such correction is useful and leads to learning. While some point out the relevance of the communicative context in which the correction takes place, others stress the value of consciousness-raising. Trying to reconcile the two approaches, this paper describes an application of artificial intelligence in the second language error remediation process. The software presented is called the Intelligent Tutor. It diagnoses some typical errors in the writing of university students who are learning English as a second language. A quasi-experimental study consisting of a grammaticality judgment pre-test, a treatment in the form of the Intelligent Tutor and a short answer post-test, was carried out with 266 university students in three countries. The findings show that artificial intelligence is an efficient instrument of error remediation, reducing the error rate by an average of 83%. This paper discusses the theoretical underpinnings of the software used, briefly describes the software itself and then presents the study design, its findings and their implications in the wider context of second language learning.

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