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Sonsoles Lopez-Pernas; Kamila Misiejuk; Rogers Kaliisa; Mohammed Saqr – IEEE Transactions on Learning Technologies, 2025
Despite the growing use of large language models (LLMs) in educational contexts, there is no evidence on how these can be operationalized by students to generate custom datasets suitable for teaching and learning. Moreover, in the context of network science, little is known about whether LLMs can replicate real-life network properties. This study…
Descriptors: Students, Artificial Intelligence, Man Machine Systems, Interaction
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Kadir Karakaya – Asian Journal of Distance Education, 2025
This article explores human-AI interaction with large language models or conversational agents in complex information tasks with a focus on prompt engineering strategies. The paper reviews the current literature on the use of artificial intelligence (AI) for complex information tasks that are often nonlinear and entail interpretation,…
Descriptors: Artificial Intelligence, Technology Uses in Education, Man Machine Systems, Interaction
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Pablo Flores Romero; Kin Nok Nicholas Fung; Guang Rong; Benjamin Ultan Cowley – npj Science of Learning, 2025
Large Language Models (LLMs) present a radically new paradigm for the study of "information foraging behavior." We study how LLM technology is used for pedagogical content creation by a sample of 25 participants in a doctoral-level Artificial Intelligence (AI) in Education course, and the role of computational-thinking skills in shaping…
Descriptors: Man Machine Systems, Artificial Intelligence, Natural Language Processing, Interaction
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Gang Zhao; Lijun Yang; Biling Hu; Jing Wang – Journal of Educational Computing Research, 2025
Human-computer collaboration is an effective way to learn programming courses. However, most existing human-computer collaborative programming learning is supported by traditional computers with a relatively low level of personalized interaction, which greatly limits the efficiency of students' efficiency of programming learning and development of…
Descriptors: Artificial Intelligence, Man Machine Systems, Programming, Learning Strategies
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Nguyen, Ha; Lopez, John; Homer, Bruce; Ali, Alisha; Ahn, June – Information and Learning Sciences, 2023
Purpose: In the USA, 22-40% of youth who have been accepted to college do not enroll. Researchers call this phenomenon summer melt, which disproportionately affects students from disadvantaged backgrounds. A major challenge is providing enough mentorship with the limited number of available college counselors. The purpose of this study is to…
Descriptors: Design, Artificial Intelligence, Man Machine Systems, Interaction
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Mohan Yang; Shiyan Jiang; Belle Li; Kristin Herman; Tian Luo; Shanan Chappell Moots; Nolan Lovett – British Journal of Educational Technology, 2025
Generative artificial intelligence brings opportunities and unique challenges to nontraditional higher education students, stemming, in part, from the experience of the digital divide. Providing access and practice is critical to bridge this divide and equip students with needed digital competencies. This mixed-methods study investigated how…
Descriptors: Nontraditional Students, Artificial Intelligence, Technology Uses in Education, Man Machine Systems
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Hung, Wei-Chen; Smith, Thomas J.; Smith, M. Cecil – British Journal of Educational Technology, 2015
Technology provides the means to create useful learning and practice environments for learners. Well-designed cognitive tutor systems, for example, can provide appropriate learning environments that feature cognitive supports (ie, scaffolding) for students to increase their procedural knowledge. The purpose of this study was to conduct a series of…
Descriptors: Intelligent Tutoring Systems, Usability, Research Methodology, Man Machine Systems
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MacArthur, Charles A. – Exceptional Children, 1988
The paper discusses: features of word processors and their impact on the writing process and the social context for writing; research on word processors in schools and the potential instructional role of extensions to word processors, such as spelling and style checkers, synthesized speech output, computer networks, and interactive prompting…
Descriptors: Computer Assisted Instruction, Computer Networks, Computer Uses in Education, Elementary Secondary Education