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The rapid adoption of large language models (LLMs) has significantly influenced distance education practices, yet existing research remains unevenly distributed across system levels. This study presents a PRISMA 2020-compliant systematic review to examine integration patterns and governance implications of LLM adoption in distance education. Data were collected from Scopus and Web of Science databases, yielding 250 records, of which 170 unique studies were screened and 94 full-text articles were included. Empirical studies were assessed using the Mixed Methods Appraisal Tool (MMAT, 2018), indicating predominantly moderate methodological quality.
The findings show that LLM adoption is primarily concentrated at the micro level, where models are used as tools or pedagogical assistants, while meso-level integration—particularly through learning management systems and institutional workflows—has increased notably after 2024. In contrast, macro-level governance considerations remain limited. Chi-square analysis revealed a significant association between publication year and system-level distribution (χ² = 14.53, p < .05, Cramer's V = .28), suggesting a structural shift toward institutional embedding over time.
The study contributes to the literature by providing a multilevel analysis of LLM adoption and highlighting emerging governance implications. The results indicate that while integration is expanding across system levels, governance-related considerations are developing more gradually, pointing to an area requiring further empirical and conceptual attention.
Keywords:
LLM, AI, Large Language Model, Artificial Intelligence, Distance Education, Educational Technologies.
Cite Article:
"A Multilevel Analysis of Large Language Model Adoption in Distance Education", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 3, page no.b322-b331, March-2026, Available :http://www.ijrti.org/papers/IJRTI2603140.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator