Language Assessment Using Word Family-Based Automated Item Generation: Evaluating Item Quality Using Teacher Ratings


The integration of Artificial Intelligence (AI) technologies has initiated a new era in language assessment practices, revolutionizing the field with its innovative approaches. This study introduces an advanced Automated Item Generation (AIG) system that utilizes word families as a foundation to automatically generate test items. The primary objective of this research is to investigate the effectiveness of the AIG system in producing high-quality questions through a comprehensive evaluation that combines both quantitative and qualitative measures. The AIG system is developed using cutting-edge machine learning and deep learning techniques, enabling it to enhance and facilitate the language assessment process by generating a substantial number of items. To assess the quality of the generated questions, a group of 30 experienced English teachers participated in the evaluation process. The participants assessed the quality of multiple-choice and fill-in-the-blank questions generated by the AIG system using a 4-point scale. To supplement the quantitative analysis, interviews were conducted to capture the perspectives of the teachers concerning the integration of AIG in language assessment. The findings demonstrate highly promising outcomes in terms of question quality, validating the efficacy of employing word families as a linguistic basis for generating test items. By shedding light on the advantages and effectiveness of utilizing word families as a fundamental lexical unit for AIG, this study contributes to the field of automated item generation in language assessment.

Author Information
S. Susan Marandi, Alzahra University, Iran
Shaghayegh Hosseini, Alzahra University, Iran

Paper Information
Conference: WorldCALL2023
Stream: AI Tools and Techniques

This paper is part of the WorldCALL2023 Conference Proceedings (View)
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To cite this article:
Marandi S., & Hosseini S. (2024) Language Assessment Using Word Family-Based Automated Item Generation: Evaluating Item Quality Using Teacher Ratings ISSN: 2759-1182 – WorldCALL2023: Conference Proceedings
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Posted by James Alexander Gordon