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Yu Song – Routledge, Taylor & Francis Group, 2024
This book demonstrates how artificial intelligence (AI) can be used to uncover the patterns of classroom dialogue and increase the productiveness of dialogue. In this book, the author uses a range of data mining techniques to explore the productive features and sequential patterns of classroom dialogue. She analyses how the Large Language Model…
Descriptors: Classroom Communication, Artificial Intelligence, Technology Uses in Education, Dialogs (Language)
Peer reviewedClayton Cohn; Surya Rayala; Caitlin Snyder; Joyce Horn Fonteles; Shruti Jain; Naveeduddin Mohammed; Umesh Timalsina; Sarah K. Burriss; Ashwin T. S.; Namrata Srivastava; Menton Deweese; Angela Eeds; Gautam Biswas – Grantee Submission, 2025
Collaborative dialogue offers rich insights into students' learning and critical thinking. This is essential for adapting pedagogical agents to students' learning and problem-solving skills in STEM+C settings. While large language models (LLMs) facilitate dynamic pedagogical interactions, potential hallucinations can undermine confidence, trust,…
Descriptors: STEM Education, Computer Science Education, Artificial Intelligence, Natural Language Processing
Fiachra Long – Studies in Philosophy and Education, 2025
Conversation of a particular sort holds the key to learning. I argue here that peer to peer conversation promotes two features that are essential to progressive learning, namely 'contestation' and 'communication.' Traditional learning is principally concerned with whether students have reached a standard of knowledge and skill prescribed by some…
Descriptors: Artificial Intelligence, Natural Language Processing, Intelligent Tutoring Systems, Peer Relationship
Videep Venkatesha; Abhijnan Nath; Ibrahim Khebour; Avyakta Chelle; Mariah Bradford; Jingxuan Tu; James Pustejovsky; Nathaniel Blanchard; Nikhil Krishnaswamy – International Educational Data Mining Society, 2024
In the realm of collaborative learning, extracting the beliefs shared within a group is paramount, especially when navigating complex tasks. Inherent in this problem is the fact that in naturalistic collaborative discourse, the same propositions may be expressed in radically different ways. This difficulty is exacerbated when speech overlaps and…
Descriptors: Cooperative Learning, Dialogs (Language), Language Usage, Artificial Intelligence
Jionghao Lin; Wei Tan; Lan Du; Wray Buntine; David Lang; Dragan Gasevic; Guanliang Chen – IEEE Transactions on Learning Technologies, 2024
Automating the classification of instructional strategies from a large-scale online tutorial dialogue corpus is indispensable to the design of dialogue-based intelligent tutoring systems. Despite many existing studies employing supervised machine learning (ML) models to automate the classification process, they concluded that building a…
Descriptors: Classification, Dialogs (Language), Teaching Methods, Computer Assisted Instruction
Muhammad Mooneeb Ali; Ahmed M. Alaa; Wael Alharbi; Issa Al Qurashi – International Journal of Technology in Education, 2025
Machine and prompt-based Artificial Intelligence (AI) learning has made significant evolution profusely. In education, it has revitalized researchers and educators to scout out subsequent advantages for optimizing learning results. Chiefly, Generative AI has exhibited substantial potential as a tool for language augmentation. This study aims to…
Descriptors: Foreign Countries, Grade 10, Artificial Intelligence, Natural Language Processing
Eunhye Shin – Journal of Computer Assisted Learning, 2025
Background: Analysing classroom dialogue is a widely used approach for understanding students' learning, often requiring team-based collaborative research. This presents a challenge for single researchers due to the labour-intensive nature of the process. Emerging advancements in large language models (LLMs) such as ChatGPT, enhance qualitative…
Descriptors: Artificial Intelligence, Technology Uses in Education, Science Education, Coding
Feiwen Xiao; Ellen Wenting Zou; Jiaju Lin; Zhaohui Li; Dandan Yang – British Journal of Educational Technology, 2025
Large language model (LLM)-based conversational agents (CAs), with their advanced generative capabilities and human-like conversational interfaces, can serve as reading partners for children during dialogic reading and have shown promise in enhancing children's comprehension and conversational skills. However, there is limited research on the…
Descriptors: Childrens Literature, Electronic Books, Artificial Intelligence, Natural Language Processing
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)
Gloria Ashiya Katuka – ProQuest LLC, 2024
Dialogue act (DA) classification plays an important role in understanding, interpreting and modeling dialogue. Dialogue acts (DAs) represent the intended meaning of an utterance, which is associated with the illocutionary force (or the speaker's intention), such as greetings, questions, requests, statements, and agreements. In natural language…
Descriptors: Dialogs (Language), Classification, Intention, Natural Language Processing
Deliang Wang; Yaqian Zheng; Gaowei Chen – Educational Technology & Society, 2024
This study investigates the potential of ChatGPT, a cutting-edge large language model in generative artificial intelligence (AI), to support the teaching of dialogic pedagogy to preservice teachers. A workshop was conducted with 29 preservice teachers, wherein ChatGPT and another prominent AI model, Bert, were sequentially integrated to facilitate…
Descriptors: Artificial Intelligence, Preservice Teachers, Models, Teaching Methods
Mazumder, Sahisnu – ProQuest LLC, 2021
Dialogue systems, commonly called as Chatbots, have gained escalating popularity in recent times due to their wide-spread applications in carrying out chit-chat conversations with users and accomplishing tasks as personal assistants. These systems are typically trained from manually-labeled data and/or written with handcrafted rules and often, use…
Descriptors: Computer Mediated Communication, Computer Software, Dialogs (Language), Information Seeking
Afzal, Shazia; Dempsey, Bryan; D'Helon, Cassius; Mukhi, Nirmal; Pribic, Milena; Sickler, Aaron; Strong, Peggy; Vanchiswar, Mira; Wilde, Lorin – Childhood Education, 2019
As artificially intelligent systems make their foray into the day-to-day educational experiences of students, we need to pay careful attention to the relationship between the system and the student. In this article, the authors discuss designing the personality of a virtual tutoring system called IBM Watson Tutor. The AI personality is key to the…
Descriptors: Intelligent Tutoring Systems, Artificial Intelligence, Instructional Design, Learner Engagement
Graesser, Arthur C. – Grantee Submission, 2016
AutoTutor helps students learn by holding a conversation in natural language. AutoTutor is adaptive to the learners' actions, verbal contributions, and in some systems their emotions. Many of AutoTutor's conversation patterns simulate human tutoring, but other patterns implement ideal pedagogies that open the door to computer tutors eclipsing…
Descriptors: Intelligent Tutoring Systems, Artificial Intelligence, Communication Strategies, Dialogs (Language)
Knight, Simon; Littleton, Karen – Journal of Learning Analytics, 2015
This paper provides a novel, conceptually driven stance on the state of the contemporary analytic challenges faced in the treatment of dialogue as a form of data across on- and offline sites of learning. In prior research, preliminary steps have been taken to detect occurrences of such dialogue using automated analysis techniques. Such advances…
Descriptors: Dialogs (Language), Data Collection, Data Analysis, Artificial Intelligence
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