The present study examined the neural correlates of emotion effects evoked by emotion-label and emotion-laden nouns in Chinese-English bilinguals' two languages through the emotion categorization tasks. At the perceptual processing stage, only L2 emotion-label and emotion-laden nouns induced amplified N100 than neutral nouns. At the semantic…

Over the past decade, social scientists have adapted computational methods for qualitative text analysis, with the hope that they can match the accuracy and reliability of hand coding. The emergence of GPT and open-source generative large language models (LLMs) has transformed this process by shifting from programming to engaging with models using…

This paper presents a thematic review of the anti-plagiarism instruction of content specialists as reported in a range of articles published in the decade of 2014-2023. A total of 28 articles were identified through systematic searching and a ChatGPT-assisted selection process based on a set of inclusion criteria. Specifically, we aimed to include…

The emergence of ChatGPT, and other generative AI (GenAI) tools, has elicited dystopian and utopian proclamations concerning their potential impact on education. This paper suggests that responses to GenAI are based on often-implicit perceptions of naturalness and artificiality. To examine the depiction and function of these concepts, Gideon…

Can metaphorical reasoning involving embodied experience--such as color perception--be learned from the statistics of language alone? Recent work finds that colorblind individuals robustly understand and reason abstractly about color, implying that color associations in everyday language might contribute to the metaphorical understanding of color.…

The aim of this paper is to provide tools to teachers for monitoring student work and understanding practices in order to help student and possibly adapt exercises in the future. In the context of an online programming learning platform, we propose to study the attempts (i.e., submitted programs) of the students for each exercise by using…

Enemy items are two test items that should not be presented to a candidate on the same test. Identifying enemies is essential for personnel assessment, as they weaken the measurement precision and validity of a test. In this research, we examined the effectiveness of lexical and semantic natural language processing techniques for identifying enemy…

Background: People with neurological conditions such as Parkinson's Disease are at risk of speech and voice difficulties that impact volume, clarity of speech and intelligibility. Voice-assisted technology (VAT), such as Alexa, poorly recognises speech difficulties, and this often prompts people to change their speech to enable interaction. Aims:…

Machine learning-based automatic scoring faces challenges with imbalanced student responses across scoring categories. To address this, we introduce a novel text data augmentation framework that leverages GPT-4, a generative large language model specifically tailored for imbalanced datasets in automatic scoring. Our experimental dataset consisted…

This case study examines the development of a proof-of-concept (PoC) generative artificial intelligence (genAI) model inspired by OpenAI's ChatGPT®, implemented within the Office of Research Administration (ORA) at Emory University. Generative artificial intelligence (genAI) refers to AI models capable of producing human-like text. Specifically,…

Cognitive-informed reading education research utilises models that are underpinned by the notion that reading is a mental process of word recognition multiplied by language comprehension. Examples of these models include the Simple View of Reading, the Cognitive Foundations Framework, the Reading Rope and the Active Model of Reading. These models…

Generative AI like ChatGPT is transforming education and research rapidly. This study focuses on ethical considerations surrounding ChatGPT in academic research through a comprehensive bibliometric analysis of 245 research articles published between 2019-2024, collected from the Scopus database. The study uncovers a substantial surge in…

Background: Analyzing student short-answer written justifications to conceptually challenging questions has proven helpful to understand student thinking and improve conceptual understanding. However, qualitative analyses are limited by the burden of analyzing large amounts of text. Purpose: We apply dense and sparse Large Language Models (LLMs)…

This study explored the role of artificial intelligence (AI)-powered conversational agents in human-computer interaction, particularly in the post-coronavirus (COVID-19) era, where digital technologies are central to healthcare, customer service, and education sectors. The research investigated the disruption of context continuity when users…

Data extraction is a crucial, yet labor-intensive and error-prone part of evidence synthesis. To date, efforts to harness machine learning for enhancing efficiency of the data extraction process have fallen short of achieving sufficient accuracy and usability. With the release of large language models (LLMs), new possibilities have emerged to…