Thematic Structure of Pedagogical Agent Studies: LDA Analysis for the Period 2020–2025

Turgut, Yigit Emrah ORCID: https://orcid.org/0000-0002-6306-4090, Aktı Aslan, Seda ORCID: https://orcid.org/0000-0001-9345-6194, Kopuz, Tuba ORCID: https://orcid.org/0000-0001-6418-4580, Aslan, Alper ORCID: https://orcid.org/0000-0003-2970-6114, Allison, Jordan ORCID: https://orcid.org/0000-0001-8513-4646 and Özyurt, Özcan ORCID: https://orcid.org/0000-0002-0047-6813 (2026) Thematic Structure of Pedagogical Agent Studies: LDA Analysis for the Period 2020–2025. International Journal of Human–Computer Interaction. pp. 1-20. doi:10.1080/10447318.2026.2655931 (In Press)

Text 16224 Turgut, Y.E. et al (2025) THEMATIC STRUCTURE OF PEDAGOGICAL AGENT STUDIES - LDA ANALYSIS FOR THE PERIOD 2020-2025.pdf - Accepted Version Restricted to Repository staff only until 23 April 2027. (Publisher Embargo). Available under License Creative Commons Attribution Non-commercial 4.0. Download (1MB)

Abstract

This study examines 2,360 pedagogical agent (PA) articles published between 2020 and 2025 to identify thematic structures, trends, and research gaps through bibliometric analysis and LDA-based topic modeling. The optimal model produced 10 themes (c_v = 0.4767). The dominant themes—Learner Interaction with Virtual Assistants in Education, Student-Centred Learning and Individual Guidance, and NLP Foundations for Pedagogical Agents—account for 51.3% of publications. Trend analyses revealed consistent growth in both Artificial Intelligence and Pedagogical Design in Education and NLP Foundations for Pedagogical Agents, while Learner Interaction with Virtual Assistants in Education reached saturation. Interaction network results showed these themes at the conceptual core of the field, whereas AI-Driven Medical Chatbots, Knowledge-Based Learning Models, and Student Experiences in Intelligent Learning Environments emerged as future research areas. Overall, findings highlight a strong thematic shift toward artificial intelligence, natural language processing, and student-centered learning.

Item Type:	Article
Article Type:	Article
Uncontrolled Keywords:	Pedagogical agents; LDA; Latent Dirichlet allocation; Topic modeling; Bibliometric analysis
Subjects:	L Education > LC Special aspects of education Q Science > Q Science (General) Q Science > Q Science (General) > Q336 Artificial intelligence
Divisions:	Schools and Research Institutes > School of Business, Computing and Social Sciences
Depositing User:	Kamila Niekoraniec
Date Deposited:	27 Apr 2026 10:10
Last Modified:	04 May 2026 09:30
URI:	https://eprints.glos.ac.uk/id/eprint/16224

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