The development of explanations for scientific phenomena is crucial in science assessment. However, the scoring of students' written explanations is a challenging and resource-intensive process. Large language models (LLMs) have demonstrated the potential to address these challenges, particularly when the explanations are written in English, an…

The purpose of this research is to provide a first-person account of the dynamics and needed adaptations in an academic's transition from being a doctoral student to a doctoral supervisor. By employing autoethnography, this study explores the inner experiences, challenges, and insights encountered in the process of developing a supervisory…

Like many student writing centers, the American University of Sharjah Writing Center is seeing a rise in student reliance upon generative AI (GenAI) tools, which are artificial intelligence systems capable of generating human-like text. Peer tutors frequently seek guidance on how to approach student papers involving GenAI tools such as ChatGPT,…

This study investigated the effects of linear distance, agreement attraction, and working memory (WM) on the processing of English third-person singular subject-verb agreement by second language (L2) learners whose first language (L1) is Thai. Grounded in the Linear Distance Hypothesis (Gibson, 1998, 2000), the study hypothesized that linear…

An outgrowth of work submitted for Howard University's Generative Artificial Intelligence (AI) Writing Faculty Task Force, this essay examines what is at stake for students of color with the use of AI as well as the potential opportunities it presents. The research is tailored to explore how the mission of Historically Black Colleges and…

Large language models (LLMs) are increasingly being utilized to develop tools and services in various domains, including education. However, due to the nature of the training data, these models are susceptible to inherent social or cognitive biases, which can influence their outputs. Furthermore, their handling of critical topics, such as privacy…

This study investigates the impact and role of an instructional chatbot, ARUChatbot, in a distance education setting. Using a sequential explanatory mixed-methods design, the research involved 130 students from Ardahan University's Basic Information Technologies course. Participants were selected through purposive sampling. Quantitative data were…

Artificial Intelligence (AI) has the potential to revolutionize education as it develops, offering dynamic, individualized, and effective learning experiences that might change teaching practices. However, there is still inconsistency and limitations in the integration and use of AI in Tanzanian universities. Therefore, the study delt to…

Explicable AI in education (XAIED) has been proposed as a way to improve trust and ethical practice in algorithmic education. Based on a critical review of the literature, this paper argues that XAI should be understood as part of a wider socio-technical turn in AI. The socio-technical perspective indicates that explicability is a relative term.…

To learn language, children must map variable input to categories such as phones and words. How do children process variation and distinguish between variable pronunciations ("shoup" for "soup") versus new words? The unique sensory experience of children with cochlear implants, who learn speech through their device's degraded…

The emerging generative artificial intelligence (AI) chatbots, such as Chat Generative Pre-Trained Transformer (ChatGPT), have recently taken different disciplines by surprise. Very few scholarly papers show the collaborative effort by researchers on the impact of generative AI in higher education (HE) and its implication on HE disciplines and…

Generative AIs such as Google Bard are known to be equipped with techniques and grammatical principles of human language based on a large corpus of text and code that allow them to generate natural-sounding language, and also identify and correct grammatical errors in human-written texts. Still, they are not perfect language generators, and this…

In everyday life, humans rely on working memory (WM) processes to make sense of relationships between linguistic elements that are not linearly adjacent. For example, to understand the sentence "The dog that the cat chased is cute," we encode the referent "the dog" into WM, maintain and retrieve it after reading the verb…

Previous research has found mixed results for a link between executive function and perspective-taking. One proposed reason for this is that perspective-taking during comprehension tasks may not be internally reliable. This dissertation presents two large individual differences experiments with multiple perspective-taking during comprehension and…

Rastle et al. (2004) reported that true (e.g., walker) and pseudo (e.g., corner) multi-morphemic words prime their stem words more than form controls do (e.g., brothel priming BROTH) in a masked priming lexical decision task. This data pattern has led a number of models to propose that both of the former word types are "decomposed" into…