An increasing number of language testing companies are developing and deploying deep learning-based automated essay scoring systems (AES) to replace traditional approaches that rely on handcrafted feature extraction. However, there is hesitation to accept neural network approaches to automated essay scoring because the features are automatically…

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…

With the rapid development of generative artificial intelligence (AI) frameworks (e.g., the generative pre-trained transformer [GPT]), a growing number of researchers have started to explore its potential as an automated essay scoring (AES) system. While previous studies have investigated the alignment between human ratings and GPT ratings, few…

As access to smartphones and emerging technologies has become ubiquitous in our daily lives and in language learning, technology-mediated social interaction has become common in teaching and assessing L2 speaking. The changing ecology of L2 spoken interaction provides language educators and testers with opportunities for renewed test design and…

Construct validity and building validity arguments are some of the main challenges facing the language assessment community. The notion of construct validity and validity arguments arose from research in psychological assessment and developed into the gold standard of validation/validity research in language assessment. At a theoretical level,…

This article reports the results of using a three-layer back propagation artificial neural network to predict item difficulty in a reading comprehension test. Three classes of variables were examined: text structure, propositional analysis, and cognitive demand. Results demonstrate that the networks can consistently predict item difficulty. (JL)