Journal of Applied Data Sciences
Vol 6, No 4: December 2025

Acceptance and Success Model for AI Use in Higher Education: Development, Instrument Decomposition, and Its Triangulation Testing

Aang Subiyakto (Department of Information Technology UIN Syarif Hidayatullah Jakarta)
Muhammad Q Huda (Department of Information Technology, UIN Syarif Hidayatullah Jakarta)
Nashrul Hakiem (Department of Information Technology, UIN Syarif Hidayatullah Jakarta)
Hendra B Suseno (Department of Informatics Engineering, UIN Syarif Hidayatullah Jakarta)
Viva Arifin (Department of Informatics Engineering, UIN Syarif Hidayatullah Jakarta)
Agus N Azmi (Department of International Relations, UIN Syarif Hidayatullah)
Asrul Sani (Department of Information Technology)
Dwi Yuniarto (Department of Informatics, Universitas Sebelas April)
Muhammad S Hartawan (Universitas Krisnadwipayana)
Agung Suryatno (Asia e University)
Muji Muji (Doctoral Program of Management Science, Universitas Tarumanagara)
Fachrul Kurniawan (Department of Informatics Engineering, UIN Maulana Malik Ibrahim)
Ririen Kusumawati (Department of Informatics, UIN Maulana Malik Ibrahim)
Naeem A Balogun (Department of Information Technology, Confluence University of Science and Technology)
Abd. Rahman Ahlan (Department of Information Systems, International Islamic University Malaysia)



Article Info

Publish Date
15 Nov 2025

Abstract

Prior social computing studies described that the performance of technology products is about how the product use benefits the users, including Artificial Intelligence (AI). To have an impact, ensuring how AI is used is a prerequisite after the development. Furthermore, its use is also influenced by how users accept AI. This study aimed to develop an acceptance and success model of AI use in the higher education world from the user perspective, to decompose the model into its instrument level, and to test the validity and reliability of the research instrument. The researchers developed the model by adopting and combining the Technology Acceptance Model (TAM) and the Information System Success Model (ISSM) and adapting the proposed model in the context of AI use in higher education learning. The measurement items were derived from definitions of the variables and indicators of the model. The instrument was tested sequentially using triangulation methods. The quantitative testing was online survey with about 51 respondents and the qualitative one was interview involving five experts. This study may contribute methodologically as one of the guidance for novice scholars in similar works. It may relate to the clarity of the research procedure and the implementation of the mixed testing methods. Of course, the assumptions, samples, and data used in the study cannot be generalized for the other studies. Referring to the model development, the proposed model may not cover the other factors related to the ethical, cultural, and organizational barriers for adopting AI. These barriers may also affect its acceptance and success. Thus, the adoption of the factors related the barriers may also be interesting to study further.

Copyrights © 2025






Journal Info

Abbrev

JADS

Publisher

Subject

Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

Description

One of the current hot topics in science is data: how can datasets be used in scientific and scholarly research in a more reliable, citable and accountable way? Data is of paramount importance to scientific progress, yet most research data remains private. Enhancing the transparency of the processes ...