Many individuals with autism experience challenges using language in social contexts (i.e., pragmatic language). Characterizing and understanding pragmatic variability is important to inform intervention strategies and the etiology of communication challenges in autism; however, current manual coding-based methods are often time and labor…

Purpose: The Language ENvironment Analysis (LENA) technology uses automated speech processing (ASP) algorithms to estimate counts such as total adult words and child vocalizations, which helps understand children's early language environment. This ASP has been validated in North American English and other languages in predominantly monolingual…

Recent developments in the use of large-language models have led to substantial improvements in the accuracy of content-based automated scoring of free-text responses. The reported accuracy levels suggest that automated systems could have widespread applicability in assessment. However, before they are used in operational testing, other aspects of…

Knowing functions and functional thinking have recently moved from just knowledge for older students to incorporating younger students, and functional thinking has been identified as one of the core competencies for algebra. Although it is significant for mathematical understanding, there is no unified view of functional thinking and how different…

Researchers have increasingly utilized artificial intelligence to automatically analyze classroom dialogue, aiming to provide timely feedback to teachers due to its educational significance. However, traditional machine learning and deep learning models face challenges, such as limited performance and lack of generalizability, across various…

Advances in computational language models increasingly enable adaptive support for self-regulated learning (SRL) in digital learning environments (DLEs; eg, via automated feedback). However, the accuracy of those models is a common concern for educational stakeholders (eg, policymakers, researchers, teachers and learners themselves). We compared…

Critical thinking and anxiety influenced the translation competence of translators. This study sought to examine the interactions between critical thinking, attitude, and anxiety influenced the translation competence of translators. This study adopted an empirical approach to collect data from 145 student translators from many colleges in Saudi…

Background: This study investigates the use of large language models to create adaptive, contextually relevant survey questions, aiming to enhance data quality in educational research without limiting scalability. Purpose: We provide step-by-step methods to develop a dynamic survey instrument, driven by artificial intelligence (AI), and introduce…

In this article, we investigate a systematic workflow that supports the learning engineering process of formulating the starting question for a conversational module based on existing learning materials, specifying the input that transformer-based language models need to function as classifiers, and specifying the adaptive dialogue structure,…

Recent developments in computerized scoring via semantic distance have provided automated assessments of verbal creativity. Here, we extend past work, applying computational linguistic approaches to characterize salient features of creative text. We hypothesize that, in addition to semantic diversity, the degree to which a story includes…

Systematic reviews are vital for guiding practice, research and policy, although they are often slow and labour-intensive. Large language models (LLMs) could speed up and automate systematic reviews, but their performance in such tasks has yet to be comprehensively evaluated against humans, and no study has tested Generative Pre-Trained…

The capabilities of large language models (LLMs) in language comprehension, conversational interaction, and content generation have led to their widespread adoption across various educational stages and contexts. Given the fundamental role of education, concerns are rising about whether LLMs can serve as competent teachers. To address the…

Since its arrival in late 2022, ChatGPT has occupied the minds of academics, administrators and students. Reactions to the emergence of Large Language Models (LLMs) have varied but significant anxieties about their impact on assessment have arisen. To address these concerns, this article serves three purposes; firstly, it seeks to gauge the…

Purpose: This study examines the effectiveness of automatic speech recognition (ASR) for individuals with speech disorders, addressing the gap in performance between read and conversational ASR. We analyze the factors influencing this disparity and the effect of speech mode--specific training on ASR accuracy. Method: Recordings of read and…

Exam evaluations are essential to assessing students' knowledge and progress in a subject or course. To meet learning objectives and assess student performance, questions must be themed. Automatic Question Generation (AQG) is our novel approach to this problem. A comprehensive process for autonomously generating Bahasa Indonesia text questions is…