Publication Date
| In 2026 | 0 |
| Since 2025 | 2 |
| Since 2022 (last 5 years) | 4 |
| Since 2017 (last 10 years) | 6 |
| Since 2007 (last 20 years) | 9 |
Descriptor
Source
Author
Publication Type
| Journal Articles | 9 |
| Reports - Research | 8 |
| Reports - Descriptive | 1 |
| Reports - Evaluative | 1 |
| Speeches/Meeting Papers | 1 |
| Tests/Questionnaires | 1 |
Education Level
| Higher Education | 10 |
| Postsecondary Education | 10 |
Audience
Location
| China | 1 |
| Germany | 1 |
| Iowa | 1 |
| New Zealand | 1 |
| Pennsylvania | 1 |
Laws, Policies, & Programs
Assessments and Surveys
| Test of English as a Foreign… | 1 |
What Works Clearinghouse Rating
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
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)
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
Sinclair, Arabella J.; Schneider, Bertrand – International Educational Data Mining Society, 2021
Collaborative dialogue is rich in conscious and subconscious coordination behaviours between participants. This work explores collaborative learner dialogue through theories of alignment, analysing inter-partner movement and language use with respect to our hypotheses: that they interrelate, and that they form predictors of collaboration quality…
Descriptors: Dialogs (Language), Cooperative Learning, Correlation, Predictor Variables
Timpe-Laughlin, Veronika; Sydorenko, Tetyana; Daurio, Phoebe – Computer Assisted Language Learning, 2022
Often, second/foreign (L2) language learners receive little opportunity to interact orally in the target language. Interactive, conversation-based spoken dialog systems (SDSs) that use automated speech recognition and natural language processing have the potential to address this need by engaging learners in meaningful, goal-oriented speaking…
Descriptors: Second Language Learning, Second Language Instruction, Oral Language, Dialogs (Language)
Chukharev-Hudilainen, Evgeny; Ockey, Gary J. – ETS Research Report Series, 2021
This paper describes the development and evaluation of Interaction Competence Elicitor (ICE), a spoken dialog system (SDS) for the delivery of a paired oral discussion task in the context of language assessment. The purpose of ICE is to sustain a topic-specific conversation with a test taker in order to elicit discourse that can be later judged to…
Descriptors: Intercultural Communication, Oral Language, Communicative Competence (Languages), Error Analysis (Language)
Lipschultz, Michael; Litman, Diane; Katz, Sandra; Albacete, Patricia; Jordan, Pamela – Grantee Submission, 2014
Post-problem reflective tutorial dialogues between human tutors and students are examined to predict when the tutor changed the level of abstraction from the student's preceding turn (i.e., used more general terms or more specific terms); such changes correlate with learning. Prior work examined lexical changes in abstraction. In this work, we…
Descriptors: Intelligent Tutoring Systems, Natural Language Processing, Semantics, Abstract Reasoning
Ezen-Can, Aysu; Boyer, Kristy Elizabeth – Journal of Educational Data Mining, 2015
Within the landscape of educational data, textual natural language is an increasingly vast source of learning-centered interactions. In natural language dialogue, student contributions hold important information about knowledge and goals. Automatically modeling the dialogue act of these student utterances is crucial for scaling natural language…
Descriptors: Classification, Dialogs (Language), Computational Linguistics, Information Retrieval
Vlugter, P.; Knott, A.; McDonald, J.; Hall, C. – Computer Assisted Language Learning, 2009
We describe a computer assisted language learning (CALL) system that uses human-machine dialogue as its medium of interaction. The system was developed to help students learn the basics of the Maori language and was designed to accompany the introductory course in Maori running at the University of Otago. The student engages in a task-based…
Descriptors: College Students, Introductory Courses, Malayo Polynesian Languages, Pretests Posttests
Kim, Jung Hee; Freedman, Reva; Glass, Michael; Evens, Martha W. – Discourse Processes: A Multidisciplinary Journal, 2006
We annotated transcripts of human tutoring dialogue for the purpose of constructing a dialogue-based intelligent tutoring system, CIRCSIM-Tutor. The tutors were professors of physiology who were also expert tutors. The students were 1st year medical students who communicated with the tutors using typed communication from separate rooms. The tutors…
Descriptors: Tutors, Tutoring, Physiology, Natural Language Processing

Peer reviewed
Direct link
