ItemArticle

Understanding learner adoption of generative AI-powered Ed-Tech applications by dissertation-based master students

Title
Understanding learner adoption of generative AI-powered Ed-Tech applications by dissertation-based master students
Creator
Saraf, Vanshika; Davidson, Benny Godwin J.; George, Jossy P.; Muttungal, Peter V.
Description
Purpose This study aims to investigate the elements that influence master students' behavioral intentions to use generative artificial intelligence (AI) in educational contexts. It examines attitudes toward technology, effort expectancy and performance expectancy, with knowledge sharing as a mediating variable, to develop targeted interventions for enhancing the adoption of generative AI in Education Technology (Ed-Tech). Design/methodology/approach The study uses a stratified random sampling method. This sampling technique ensured that participants from various academic disciplines and dissertation themes were well-represented in the sample, thereby increasing data variety and representativeness. The population size was approximately 6, 034, and a sample of 392 participants was chosen for the study. The study employed a tripartite approach, utilizing IBM SPSS and AMOS to evaluate the validity and reliability of the investigated constructs. Structural equation modeling was then applied to test the proposed hypotheses. Findings The results emphasize the importance of Ed-Tech competencies, effort expectancy and performance expectancy in determining students' intent to use generative AI. Furthermore, the mediating role of knowledge sharing emphasizes its influence on technological adoption. Originality/value This study provides practical implications for academic institutions by informing tailored approaches to optimize student learning outcomes, dissertation progress and graduate employability. Through a comprehensive framework, it aims to promote inclusive technology access and create an environment conducive to maximizing the potential of generative AI-Ed-Tech in enhancing student success. 2026 Emerald Publishing Limited.
Source
Journal of International Education in Business;pp.1-25
Date
01-01-2026
Publisher
Emerald Publishing
Subject
Behavioral intention to use AI; ChatGPT; Education technology competencies; Effort expectancy; Generative AI; Knowledge sharing
Coverage
Saraf V., School of Business and Management, Christ University, Bengaluru, India; Davidson B.G.J., School of Business, University of the Fraser Valley, Abbotsford, Canada, Acsenda School of Management, Vancouver, Canada, Western Community College, Surrey, Canada; George J.P., School of Business and Management, Christ University, Bengaluru, India; Muttungal P.V., Department of Psychology, Christ University, Bengaluru, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 2046469X;
Format
online
Language
English
Type
Article

Citation

Saraf, Vanshika; Davidson, Benny Godwin J.; George, Jossy P.; Muttungal, Peter V., “Understanding learner adoption of generative AI-powered Ed-Tech applications by dissertation-based master students,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/22902.

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