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All Journal Multidiciplinary Output Research for Actual and International Issue (Morfai Journal)
Ade Himernio Adnis
Universitas Islam As-Syafi'iyah
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Social Sciences Other

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"DETERMINING FACTORS OF ARTIFICIAL INTELLIGENCE (AI) ADOPTION IN LEARNING: SYSTEMATIC LITERATURE REVIEW (SLR). BASED ON THE THEORY OF DIFFUSION OF INNOVATION" Rian Budi Purnomo; Jack Sholeh Jamil; Nurjanah; Ade Himernio Adnis
Multidiciplinary Output Research For Actual and International Issue (MORFAI) Vol. 6 No. 2 (2026): Multidiciplinary Output Research For Actual and International Issue
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54443/morfai.v6i2.5016

Abstract

The integration of Artificial Intelligence (AI) into the education sector offers significant transformational potential, but its successful implementation depends heavily on the factors driving the adoption of the technology by educators and institutions. This study aims to identify and analyze the determinants of AI adoption in learning contexts using Everett Rogers' Diffusion of Innovation (DOI) Theory framework. The study employed a Systematic Literature Review (SLR) method. A literature search was conducted in reputable databases (such as Google Scholar, Scopus, and Eric) spanning the past five years. Data were analyzed through the stages of identification, screening, eligibility, and inclusion of relevant articles. The findings indicate that the five characteristics of innovation in DOI Theory—relative advantage, compatibility, complexity, triability, and observability—play a crucial role in AI adoption decisions. Relative advantage in the form of administrative efficiency and personalized learning are key drivers, while technical complexity and lack of supporting infrastructure are significant barriers. Furthermore, external factors such as institutional policies and teacher self-efficacy contribute to accelerating the diffusion process. The study concludes that to increase AI adoption, technology developers and policymakers should focus on reducing system complexity and aligning technology with existing curricula.
Co-Authors Jack Sholeh Jamil Nurjanah Rian Budi Purnomo