Artificial Intelligence, Teaching Presence, and Sense of Belonging in Online Higher Education: Implications for Student Retention

Abstract: The increasing use of artificial intelligence (AI) in online higher education has raised questions about its influence on instructional quality, student belonging, and persistence. Grounded in the Community of Inquiry framework and belongingness theory, this study examined the relationships among AI-supported instruction, teaching presence, sense of belonging, and student retention intent. Using a quantitative, cross-sectional design, data were collected from 150 students enrolled in fully online higher education courses. Data were analyzed using SPSS (Version 29), including descriptive statistics, reliability testing, Pearson correlations, multiple regression analyses, and mediation analyses using PROCESS macro. Results indicated that AI-supported instruction significantly predicted teaching presence (β = .46, p < .001), teaching presence significantly predicted sense of belonging (β = .54, p < .001), and sense of belonging significantly predicted retention intent (β = .59, p < .001). Mediation analysis showed that sense of belonging partially mediated the relationship between teaching presence and retention intent, with a significant indirect effect (b = .28, 95% CI [.17, .41]). Sequential mediation analysis further demonstrated that AI-supported instruction influenced retention intent indirectly through teaching presence and sense of belonging (b = .18, 95% CI [.10, .30]), with indirect effects exceeding direct effects. Overall, the findings indicate that AI contributes to student retention primarily through a human-centered pathway in which enhanced teaching presence fosters students’ sense of belonging, thereby strengthening persistence intentions. These results underscore the importance of integrating AI within relational pedagogical practices to support retention in online higher education. Keywords: artificial intelligence in education; teaching presence; sense of belonging; online higher education; student retention; mediation analysis. Title: Artificial Intelligence, Teaching Presence, and Sense of Belonging in Online Higher Education: Implications for Student Retention Author: David Bull International Journal of Computer Science and Information Technology Research ISSN 2348-1196 (print), ISSN 2348-120X (online) Vol. 14, Issue 1, January 2026 - March 2026 Page No: 1-18 Research Publish Journals Website: www.researchpublish.com Published Date: 05-January-2026 DOI: https://doi.org/10.5281/zenodo.18152726 Paper Download Link (Source) https://www.researchpublish.com/papers/artificial-intelligence-teaching-presence-and-sense-of-belonging-in-online-higher-education-implications-for-student-retention

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Publication Details

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Zenodo (CERN European Organization for Nuclear Research)
Published
2026-01-05
DOI
https://doi.org/10.5281/zenodo.18152726
Primary Topic
E-Learning and COVID-19
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article
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article

Artificial Intelligence, Teaching Presence, and Sense of Belonging in Online Higher Education: Implications for Student Retention

David Bull
Zenodo (CERN European Organization for Nuclear Research)
E-Learning and COVID-19
article

Artificial Intelligence, Teaching Presence, and Sense of Belonging in Online Higher Education: Implications for Student Retention

David Bull
article en

Abstract

Abstract: The increasing use of artificial intelligence (AI) in online higher education has raised questions about its influence on instructional quality, student belonging, and persistence. Grounded in the Community of Inquiry framework and belongingness theory, this study examined the relationships among AI-supported instruction, teaching presence, sense of belonging, and student retention intent. Using a quantitative, cross-sectional design, data were collected from 150 students enrolled in fully online higher education courses. Data were analyzed using SPSS (Version 29), including descriptive statistics, reliability testing, Pearson correlations, multiple regression analyses, and mediation analyses using PROCESS macro. Results indicated that AI-supported instruction significantly predicted teaching presence (β = .46, p < .001), teaching presence significantly predicted sense of belonging (β = .54, p < .001), and sense of belonging significantly predicted retention intent (β = .59, p < .001). Mediation analysis showed that sense of belonging partially mediated the relationship between teaching presence and retention intent, with a significant indirect effect (b = .28, 95% CI [.17, .41]). Sequential mediation analysis further demonstrated that AI-supported instruction influenced retention intent indirectly through teaching presence and sense of belonging (b = .18, 95% CI [.10, .30]), with indirect effects exceeding direct effects. Overall, the findings indicate that AI contributes to student retention primarily through a human-centered pathway in which enhanced teaching presence fosters students’ sense of belonging, thereby strengthening persistence intentions. These results underscore the importance of integrating AI within relational pedagogical practices to support retention in online higher education. Keywords: artificial intelligence in education; teaching presence; sense of belonging; online higher education; student retention; mediation analysis. Title: Artificial Intelligence, Teaching Presence, and Sense of Belonging in Online Higher Education: Implications for Student Retention Author: David Bull International Journal of Computer Science and Information Technology Research ISSN 2348-1196 (print), ISSN 2348-120X (online) Vol. 14, Issue 1, January 2026 - March 2026 Page No: 1-18 Research Publish Journals Website: www.researchpublish.com Published Date: 05-January-2026 DOI: https://doi.org/10.5281/zenodo.18152726 Paper Download Link (Source) https://www.researchpublish.com/papers/artificial-intelligence-teaching-presence-and-sense-of-belonging-in-online-higher-education-implications-for-student-retention

Zenodo (CERN European Organization for Nuclear Research)
Quality Education
Openalex Percentile: Top 97%
E-Learning and COVID-19
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