Feedforwarding diagnostic language assessment: Artificial intelligence- (AI-) driven weakness identification and contextualised feedback for second language speaking

Abstract

The current study proposes a new approach to weakness identification in diagnostic language assessment (DLA) for speaking skills. We also propose to design actionable and contextualised diagnostic feedback through the systematic integration of feedback and remedial learning activities. Focusing on lexical use in second language speaking, the current study developed and validated our DLA programme in terms of actual learning gains, using an experimental design. A total of 59 beginner-to-intermediate-level Japanese learners of English were randomly assigned to control or experimental groups. While both groups engaged in task repetition with a conversational artificial intelligence (AI) agent on six occasions, only the experimental group received the diagnostic feedback on lexical use including the paraphrased utterances of their original utterance. The results showed that the control group (task repetition only) demonstrated significant improvement during the task repetition sessions but failed to transfer and retain the learning gains. In contrast, despite the lack of practice effects, the experimental group (task repetition with diagnostic feedback) outperformed the control group at the posttest with a near-medium effect size. A qualitative investigation into learners’ perceptions further confirmed that the proposed contextualised diagnostic feedback succeeded in heightening their awareness of weaknesses.