ERIC Number: EJ1458149
Record Type: Journal
Publication Date: 2025-Feb
Pages: 15
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-0119-5646
EISSN: EISSN-2243-7908
Available Date: N/A
Advancing the In-Class Dialogic Quality: Developing an Artificial Intelligence-Supported Framework for Classroom Dialogue Analysis
Xian Li; Guangxin Han; Bei Fang; Juhou He
Asia-Pacific Education Researcher, v34 n1 p495-509 2025
The development of artificial intelligence (AI) significantly improves the effectiveness of classroom dialogue systems, but their integration into the learning environment remains challenging. To address this gap, this research presents a framework for automatic intelligent dialogue analysis, intending to promote high-quality classroom dialogue and facilitate teaching and learning. The proposed framework includes two main components: a dialogue-oriented interactive classroom and an artificial intelligence-powered analysis system. We present a synthesis of essential principles that ought to be adhered to in the dialogue-oriented interactive classroom, as viewed through the lens of three key domains: the environment, the community and the teaching-learning. The AI system will analyse the dialogues generated from the interactive classroom. The utilization of feedback obtained from the AI system assists educators who adjust their pedagogical strategies, consequently improving the quality of classroom dialogues. Elevated-quality dialogues will reciprocally boost the performance of the AI system, engendering a sustainable improvement for the entire framework. Moreover, we also propose "Guide of AI", a union of classroom participants and experts, which serves as the bridge between the classroom and technology to guide the operation of AI system. For the validation of the framework, we conduct an empirical study that mainly investigates the effectiveness of processed essential principles and AI systems. We select 6 pre-service teachers who are randomly divided into three groups. Three groups have different levels of involvement in AI system and each teacher gives three lessons. We record and analyse all teaching dialogue records and also use questionnaires to obtain teachers' attitudes. The results show that timely feedback from AI system can promote the improvement of dialogue quality, which demonstrates the effectiveness of AI dialogue analysis system. In addition, the proposed essential principles also show a constructive impact.
Descriptors: Artificial Intelligence, Classroom Communication, Discourse Analysis, Dialogs (Language), Discussion (Teaching Technique), Feedback (Response), Technology Uses in Education, Preservice Teachers, Student Attitudes
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Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A
Author Affiliations: N/A