Reframing Avatar-Mediated Instruction in Higher Education: A Theory-Building Integrative Review

As Artificial Intelligence (AI) enabled avatars rapidly enter higher education classrooms—particularly in asynchronous formats—faculty, instructional designers, and administrators face a critical challenge: how to understand and implement these tools beyond surface-level functionality. Despite growing adoption, the literature remains fragmented, offering limited guidance on how epistemic trust, learner identity, and institutional strategy intersect in avatar-mediated instruction. This paper addresses that gap through a theory-building integrative review that synthesizes interdisciplinary literature from instructional technology, psychology, sociology, and organizational studies. The review develops a synthesized framework of epistemic trust, learner identity, and organizational readiness for AI avatar integration. This framework helps educators and institutional leaders better understand how AI avatars influence learner engagement, reshape digital identity formation, and drive transformation in knowledge management processes. The analysis identifies key conceptual variables and moderating factors—including gender, AI familiarity, social presence, avatar design features, and institutional culture—that shape both learner trust and organizational response to AI-mediated communication (AMC). These elements together position AMC not as a passive technological tool, but as a cognitive and pedagogical shift with implications for instructional practice, organizational systems, and equitable AI integration. Rather than presenting empirical results, this paper lays the conceptual groundwork for future research. The frameworks offered here are designed to inform evidence-based inquiry, support faculty and instructional design practice, and guide institutional policy as higher education evolves toward AI-enabled knowledge ecosystems.

Authors

Institutions

Publication Details

Journal
International Journal of Learning and Development
Published
2025-06-27
DOI
https://doi.org/10.5296/ijld.v15i2.22988
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Reframing Avatar-Mediated Instruction in Higher Education: A Theory-Building Integrative Review

Chong Ho Yu, Janet Hanson, Mandy Truong
International Journal of Learning and Development
Online Learning and Analytics
article

Reframing Avatar-Mediated Instruction in Higher Education: A Theory-Building Integrative Review

Chong Ho Yu, Janet Hanson, Mandy Truong
article en

Abstract

As Artificial Intelligence (AI) enabled avatars rapidly enter higher education classrooms—particularly in asynchronous formats—faculty, instructional designers, and administrators face a critical challenge: how to understand and implement these tools beyond surface-level functionality. Despite growing adoption, the literature remains fragmented, offering limited guidance on how epistemic trust, learner identity, and institutional strategy intersect in avatar-mediated instruction. This paper addresses that gap through a theory-building integrative review that synthesizes interdisciplinary literature from instructional technology, psychology, sociology, and organizational studies. The review develops a synthesized framework of epistemic trust, learner identity, and organizational readiness for AI avatar integration. This framework helps educators and institutional leaders better understand how AI avatars influence learner engagement, reshape digital identity formation, and drive transformation in knowledge management processes. The analysis identifies key conceptual variables and moderating factors—including gender, AI familiarity, social presence, avatar design features, and institutional culture—that shape both learner trust and organizational response to AI-mediated communication (AMC). These elements together position AMC not as a passive technological tool, but as a cognitive and pedagogical shift with implications for instructional practice, organizational systems, and equitable AI integration. Rather than presenting empirical results, this paper lays the conceptual groundwork for future research. The frameworks offered here are designed to inform evidence-based inquiry, support faculty and instructional design practice, and guide institutional policy as higher education evolves toward AI-enabled knowledge ecosystems.

International Journal of Learning and DevelopmentVol. 15(2)
Hawaii Pacific University (US), Office of International Affairs (US), University of North Texas at Dallas (US)
University of North Texas
Quality Education
Openalex Percentile: Top 79%
Online Learning and Analytics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.