Deep learning models for text classification have been increasingly used in intelligent tutoring systems and educational writing assistants. However, the scarcity of data in many educational settings, as well as certain imbalances in counts among the annotated labels of educational datasets, limits the generalizability and expressiveness of…

The advent of Large Language Models (LLMs) has fundamentally disrupted our thinking about educational technology. Their ability to engage in natural dialogue, provide contextually relevant responses, and adapt to learner needs has led many to envision them as powerful tools for personalized learning. This emergence raises important questions about…

Language is an essential component of human communication and interaction. Advances in Artificial Intelligence (AI) technology, specifically in Natural Language Processing (NLP) and speech-recognition, have made is possible for conversational agents, also known as chatbots, to converse with language learners in a way that mimics human speech.…

We propose a novel method for automated short answer grading (ASAG) designed for practical use in real-world settings. The method combines LLM embedding similarity with a nonlinear regression function, enabling accurate prediction from a small number of expert-graded responses. In this use case, a grader manually assesses a few responses, while…

Open-text responses provide researchers and educators with rich, nuanced insights that multiple-choice questions cannot capture. When reliably assessed, such responses have the potential to enhance teaching and learning. However, scaling and consistently capturing these nuances remain significant challenges, limiting the widespread use of…

Student modeling is central to many educational technologies as it enables predicting future learning outcomes and designing targeted instructional strategies. However, open-ended learning domains pose challenges for accurately modeling students due to the diverse behaviors and a large space of possible misconceptions. To approach these…

It is becoming apparent that generative AI has significant implications for education. However, previous technologies that have had a large impact, such as calculators, do not provide a suitable model for understanding how generative AI can and will be used in learning. Drawing on research on human-computer interactions, we map out a typology of…

Transformer architectures contribute to managing long-term dependencies for natural language processing, representing one of the most recent changes in the field. These architectures are the basis of the innovative, cutting-edge large language models (LLMs) that have produced a huge buzz in several fields and industrial sectors, among the ones…

This review compares generative artificial intelligence with five representative educational technologies in history and concludes that AI technology can become a knowledge producer and thus can be utilized as educative AI to enhance teaching and learning outcomes. From a historical perspective, each technological breakthrough has affected…

Within the university sector, student recruitment and enrolment are key strategies as institutions strive to attract, retain and engage students. This strategy is underpinned by the provision of services, applications and technologies that facilitate lecturing and support staff. Universities that offer online learning have a particular incentive…

Knowledge tracing models have enabled a range of intelligent tutoring systems to provide feedback to students. However, existing methods for knowledge tracing in learning sciences are predominantly reliant on statistical data and instructor-defined knowledge components, making it challenging to integrate AI-generated educational content with…

Text generation tools, often presented as a form of generative artificial intelligence, have the potential to pose a threat to the integrity of the educational system. They can be misused to afford students marks and qualifications that they do not deserve. The emergence of recent tools, such as ChatGPT, appear to have left the educational…

This study aims to explore the criteria and success factors for the application of Artificial Intelligence Generated Content (AIGC) in higher education, and guide its practice through the construction of a comprehensive system and framework. This study first identifies seven primary criteria, encompassing technical robustness, integration with…

Recent developments in Conversational Agents (CAs) have offered researchers new ways to develop personalized learning support for students that is accessible in various environments. Traditionally, CA learner experiences are accessible through home devices, including Alexa Echo and Google Nest, for tasks such as setting reminders, ordering…

This emerging technology report delves into the role of ChatGPT, an OpenAI conversational AI, in language learning. The initial section introduces ChatGPT's nature and highlights its features, including accessibility, personalization, immersive learning, and instant feedback, which render it a valuable asset for language learners and educators…