AI and Classroom Assessment in Pre-service Teacher Education: A Bibliometric Analysis of Research Trends, Thematic Evolution, and Future Directions
TL;DR
The findings suggest that AI-enhanced classroom assessment is not merely a technological issue but a pedagogical and professional concern requiring assessment literacy, AI literacy, ethical judgment, and teacher agency.
Abstract
Assessment preparation in teacher education remains a persistent concern as pre-service teachers are increasingly expected to design valid assessments, generate meaningful feedback, interpret learning evidence, and exercise professional judgment in AI-mediated classrooms. This study presents a bibliometric analysis of research on artificial intelligence-enhanced classroom assessment in pre-service teacher education from 2005 to 2025. Using Scopus-indexed publications, the study analyzed 344 documents from 242 sources to map publication trends, citation patterns, influential authors and institutions, geographic distribution, co-citation structures, keyword co-occurrence networks, and thematic evolution. Results show that the field is young but rapidly expanding, with an annual growth rate of 29.61% and a sharp increase in publications from 2022 onward, reflecting the growing influence of generative AI, ChatGPT, automated feedback, learning analytics, and intelligent learning systems. Co-citation analysis revealed three intellectual foundations: teacher knowledge and research foundations, generative AI-mediated pedagogical practice, formative feedback and automated assessment. Keyword co-occurrence analysis identified four major thematic clusters: AI integration and ethical teacher education, intelligent learning systems and data-driven student learning, AI-supported feedback and personalization, and assessment analytics and evidence-based grading. The findings suggest that AI-enhanced classroom assessment is not merely a technological issue but a pedagogical and professional concern requiring assessment literacy, AI literacy, ethical judgment, and teacher agency. The study contributes a field-level understanding of this emerging research domain and highlights the need for more focused, ethical, and context-sensitive studies on how pre-service teachers design, evaluate, and use AI-supported classroom assessments.
