Artificial Intelligence in Primary School Writing Instruction: A Bibliometric Analysis of Global Research Trends (2016–2026)

Abstract

Background: The rapid integration of artificial intelligence (AI) into primary education has generated substantial scholarly interest, yet no comprehensive bibliometric study has systematically mapped the intersection of AI and writing instruction at the primary school level.

Objective: This study aims to analyze global research trends, identify key contributors and collaboration patterns, and uncover dominant thematic clusters within this domain over the period 2016–2026.

Method: A bibliometric research design was employed, drawing on a corpus of 2,277 documents retrieved from the Scopus database in April 2026, filtered to include only English-language journal articles. Data visualization and network mapping were conducted using VOSviewer, encompassing keyword co-occurrence analysis, author co-authorship mapping, country collaboration networks, and overlay visualization by year.

Results: The findings reveal an exponential growth in publication output, rising from five documents in 2016 to a peak of 1,023 in 2025, with a critical inflection point emerging in 2022 coinciding with the public release of generative AI tools. China, the United States, and Indonesia emerged as the three most productive nations, while four thematic clusters were identified: AI tools and writing performance, affective and motivational dimensions, literacy and pedagogy, and machine learning approaches.

Conclusion: The study demonstrates that the field has matured from technically oriented inquiry toward a holistic, learner-centered research agenda. These findings carry significant implications for researchers, educators, and policymakers, underscoring the urgency of developing ethically grounded, contextually inclusive frameworks for AI integration in primary writing instruction, particularly for underrepresented linguistic and cultural contexts globally.