Abstract Book of the 10th International Conference on Advanced Research in Teaching and Education
Year: 2025
[PDF]
An AI-Integrated Approach to Inclusive Learning for Career-Interrupted Learners in Online Learning
Yoon Jung, Kim
ABSTRACT:
This study aims to develop a model that enhances Inclusive Learning Experiences (ILE) for career-interrupted learners in online learning environments through the integrated use of AI Human instructors and AI tools, and to empirically test the effectiveness of that model. Theoretically, inclusion is defined as a concept that extends beyond access to encompass participation and belonging. In this study, inclusion is operationalized as three dimensions such as emotional, social, and cognitive inclusion. Based on this integrated view, we hypothesize that the combined use of AI Humans and AI tools will strengthen learners’ emotional, social, and cognitive inclusion. Two research questions guide the study: (RQ1) What are the core components of an AI-integrated inclusive learning model? (RQ2) When the model is implemented in actual courses, do learning outcomes show positive effects? Data were collected via five-point Likert survey from approximately 100 adult learners enrolled in an online career-design course. Expected results indicate that the proposed model will demonstrate theoretical and measurement validity, that the integrated use of AI Humans and AI tools will produce significant positive changes across all ILE dimensions, and that these gains will translate into improved learning outcomes. These findings provide empirical grounds for instructional design and implementation aimed at the re-inclusion of career-interrupted learners. In sum, the study presents an AI-integrated inclusive learning model that supports re-inclusion and offers foundational evidence for designing re-engagement programs in online higher education.
Keywords: Inclusion, Inclusive Learning Experiences, AI Human Instructors, AI Tools, re-engagement programs