Utilizing an AI-CEFR checker: What is the efficacy for utilizing CEFR results for first year university students?

Abstract

We investigated a text analyzer tool that provided Common European Framework of Reference for Languages (CEFR) level results for supporting language activities in two communication classes. In one class, students self-evaluated their language in formative assessment (FA) tasks and in the end, completed a survey of their experiences, which we evaluated. Students demonstrated insights into the thought process behind their written language and their motivation to evaluate their written English. They did this with the aim of improving their own language use and developing their language to achieve CEFR-J (Japanese version) based objectives. In class two, we applied the analyzer to video presentation transcripts for determining summative assessment (SA). Findings showed future potential for using this tool for grading and designing resources for CEFR-J activities and level-specificity. We concluded that Artificial Intelligence (AI)-linked CEFR-J based activities and task-based learning showed potential for further AI-language research.

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