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Albornoz-De Luise, Romina Soledad; Arevalillo-Herraez, Miguel; Arnau, David – IEEE Transactions on Learning Technologies, 2023
In this article, we analyze the potential of conversational frameworks to support the adaptation of existing tutoring systems to a natural language form of interaction. We have based our research on a pilot study, in which the open-source machine learning framework Rasa has been used to build a conversational agent that interacts with an existing…
Descriptors: Intelligent Tutoring Systems, Natural Language Processing, Artificial Intelligence, Models
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Hao Zhou; Wenge Rong; Jianfei Zhang; Qing Sun; Yuanxin Ouyang; Zhang Xiong – IEEE Transactions on Learning Technologies, 2025
Knowledge tracing (KT) aims to predict students' future performances based on their former exercises and additional information in educational settings. KT has received significant attention since it facilitates personalized experiences in educational situations. Simultaneously, the autoregressive (AR) modeling on the sequence of former exercises…
Descriptors: Learning Experience, Academic Achievement, Data, Artificial Intelligence
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Andres Neyem; Luis A. Gonzalez; Marcelo Mendoza; Juan Pablo Sandoval Alcocer; Leonardo Centellas; Carlos Paredes – IEEE Transactions on Learning Technologies, 2024
Software assistants have significantly impacted software development for both practitioners and students, particularly in capstone projects. The effectiveness of these tools varies based on their knowledge sources; assistants with localized domain-specific knowledge may have limitations, while tools, such as ChatGPT, using broad datasets, might…
Descriptors: Computer Software, Artificial Intelligence, Intelligent Tutoring Systems, Capstone Experiences
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Emiko Tsutsumi; Yiming Guo; Ryo Kinoshita; Maomi Ueno – IEEE Transactions on Learning Technologies, 2024
Knowledge tracing (KT), the task of tracking the knowledge state of a student over time, has been assessed actively by artificial intelligence researchers. Recent reports have described that Deep-IRT, which combines item response theory (IRT) with a deep learning method, provides superior performance. It can express the abilities of each student…
Descriptors: Item Response Theory, Academic Ability, Intelligent Tutoring Systems, Artificial Intelligence
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Liu, Ming; Rus, Vasile; Liu, Li – IEEE Transactions on Learning Technologies, 2017
Question generation is an emerging research area of artificial intelligence in education. Question authoring tools are important in educational technologies, e.g., intelligent tutoring systems, as well as in dialogue systems. Approaches to generate factual questions, i.e., questions that have concrete answers, mainly make use of the syntactical…
Descriptors: Chinese, Questioning Techniques, Automation, Natural Language Processing
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Kumar, R.; Rose, C. P. – IEEE Transactions on Learning Technologies, 2011
Tutorial Dialog Systems that employ Conversational Agents (CAs) to deliver instructional content to learners in one-on-one tutoring settings have been shown to be effective in multiple learning domains by multiple research groups. Our work focuses on extending this successful learning technology to collaborative learning settings involving two or…
Descriptors: Educational Technology, Computer Software, Computer Software Evaluation, Programming