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Showing 1 to 15 of 35 results Save | Export
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Fan Zhang; Chenglu Li; Owen Henkel; Wanli Xing; Sami Baral; Neil Heffernan; Hai Li – International Journal of Artificial Intelligence in Education, 2025
In recent years, the pre-training of Large Language Models (LLMs) in the educational domain has garnered significant attention. However, a discernible gap exists in the application of these models to mathematics education. This study aims to bridge this gap by pre-training LLMs on authentic K-12 mathematical dialogue datasets. Our research is…
Descriptors: Artificial Intelligence, Natural Language Processing, Mathematics Education, Elementary Secondary Education
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Ishrat Ahmed; Wenxing Liu; Rod D. Roscoe; Elizabeth Reilley; Danielle S. McNamara – Grantee Submission, 2025
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…
Descriptors: Artificial Intelligence, Natural Language Processing, Computer Mediated Communication, College Students
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Tianyuan Yang; Baofeng Ren; Chenghao Gu; Boxuan Ma; Shin 'ichi Konomi – International Association for Development of the Information Society, 2024
As education increasingly shifts towards a technology-driven model, artificial intelligence systems like ChatGPT are gaining recognition for their potential to enhance educational support. In university education and MOOC environments, students often select courses that align with their specific needs. During this process, access to information…
Descriptors: Concept Formation, Artificial Intelligence, Computer Uses in Education, MOOCs
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Wenhao Wang; Etsuko Kumamoto; Chengjiu Yin – International Educational Data Mining Society, 2024
The e-book system, widely used in learning and teaching, has generated a large amount of log data over time. Researchers analyzing these data have discovered the existence of student's jump back behavior, which is positively correlated with academic achievement. However, they also found that this behavior has the disadvantage of low efficiency. To…
Descriptors: Electronic Books, Natural Language Processing, Artificial Intelligence, Reading
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Jennifer Campbell; Katie Ansell; Tim Stelzer – Physical Review Physics Education Research, 2024
Recent advances in publicly available natural language processors (NLP) may enhance the efficiency of analyzing student short-answer responses in physics education research (PER). We train a state-of-the-art NLP, IBM's Watson, and test its agreement with human coders using two different studies that gathered text responses in which students…
Descriptors: Artificial Intelligence, Physics, Natural Language Processing, Computer Uses in Education
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Michelle Ehrenpreis; John DeLooper – portal: Libraries and the Academy, 2025
In November 2019, the Leonard Lief Library implemented Ivy.ai, a proprietary chatbot on its website. This implementation was the first academic library installation of a vendor-supplied chatbot to be discussed in the professional literature. This chatbot functioned as a new tool that assisted users seeking information from the library website.…
Descriptors: Academic Libraries, Artificial Intelligence, Natural Language Processing, Intelligent Tutoring Systems
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Joy He-Yueya; Noah D. Goodman; Emma Brunskill – International Educational Data Mining Society, 2024
Creating effective educational materials generally requires expensive and time-consuming studies of student learning outcomes. To overcome this barrier, one idea is to build computational models of student learning and use them to optimize instructional materials. However, it is difficult to model the cognitive processes of learning dynamics. We…
Descriptors: Artificial Intelligence, Natural Language Processing, Instructional Materials, Computer Uses in Education
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Wesley Morris; Scott Crossley; Langdon Holmes; Chaohua Ou; Mihai Dascalu; Danielle McNamara – International Journal of Artificial Intelligence in Education, 2025
As intelligent textbooks become more ubiquitous in classrooms and educational settings, the need to make them more interactive arises. An alternative is to ask students to generate knowledge in response to textbook content and provide feedback about the produced knowledge. This study develops Natural Language Processing models to automatically…
Descriptors: Formative Evaluation, Feedback (Response), Textbooks, Artificial Intelligence
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Benjamin Brummernhenrich; Christian L. Paulus; Regina Jucks – British Journal of Educational Technology, 2025
Generative AI systems like chatbots are increasingly being introduced into learning, teaching and assessment scenarios at universities. While previous research suggests that users treat chatbots like humans, computer systems are still often perceived as less trustworthy, potentially impairing their usefulness in learning contexts. How are…
Descriptors: Higher Education, Artificial Intelligence, College Students, Feedback (Response)
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Wan, Qian; Crossley, Scott; Banawan, Michelle; Balyan, Renu; Tian, Yu; McNamara, Danielle; Allen, Laura – International Educational Data Mining Society, 2021
The current study explores the ability to predict argumentative claims in structurally-annotated student essays to gain insights into the role of argumentation structure in the quality of persuasive writing. Our annotation scheme specified six types of argumentative components based on the well-established Toulmin's model of argumentation. We…
Descriptors: Essays, Persuasive Discourse, Automation, Identification
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Blikstein, Paulo; Zheng, Yipu; Zhou, Karen Zhuqian – European Journal of Education, 2022
New ideas and technologies enable new ways of doing as well as new forms of language. The rise of Artificial Intelligence (AI) is no exception. The implications of changing activity and language take on new gravity in certain fields to which AI is applied, such as education (AIEd). Terms like "smart," "intelligence," and…
Descriptors: Artificial Intelligence, Discourse Analysis, Semiotics, Educational Technology
Saira Anwar; Ahmed Ashraf Butt; Muhsin Menekse – Grantee Submission, 2023
This study explored the effectiveness of scaffolding in students' reflection writing process. We compared two sections of an introductory computer programming course (N=188). In Section 1, students did not receive any scaffolding while generating reflections, whereas in Section 2, students were scaffolded during the reflection writing process.…
Descriptors: Scaffolding (Teaching Technique), Writing Instruction, Writing Processes, Writing (Composition)
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Mario de la Puente; Jose Torres; Ana Laura Blanco Troncoso; Yuraima Yuliza Hernández Meza; Jenny Xiomara Marquez Carrascal – Smart Learning Environments, 2024
This study investigated the effectiveness of using ChatGPT, a large language model (LLM), to enhance critical thinking and argumentation skills among undergraduate students studying international relations in a developing nation context. A total of 95 participants were randomly assigned to an experimental group (n = 48) and a control group (n =…
Descriptors: Artificial Intelligence, Computer Uses in Education, Critical Thinking, Persuasive Discourse
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Xiao, Yunkai; Zingle, Gabriel; Jia, Qinjin; Akbar, Shoaib; Song, Yang; Dong, Muyao; Qi, Li; Gehringer, Edward – International Educational Data Mining Society, 2020
Peer assessment adds value when students provide "helpful" feedback to their peers. But, this begs the question of how we determine "helpfulness." One important aspect is whether the review detects problems in the submitted work. To recognize problem detection, researchers have employed NLP and machine-learning text…
Descriptors: Peer Evaluation, Problems, Identification, Natural Language Processing
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Jia, Qinjin; Cui, Jialin; Xiao, Yunkai; Liu, Chengyuan; Rashid, Parvez; Gehringer, Edward – International Educational Data Mining Society, 2021
Peer assessment has been widely applied across diverse academic fields over the last few decades, and has demonstrated its effectiveness. However, the advantages of peer assessment can only be achieved with high-quality peer reviews. Previous studies have found that high-quality review comments usually comprise several features (e.g., contain…
Descriptors: Peer Evaluation, Models, Artificial Intelligence, Evaluation Methods
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