Determinants of artificial intelligence acceptance among undergraduates

Kowang, Tan Owee; Kim Yew, Lim; Chin Fei, Goh; Choon Hee, Ong

doi:10.11591/ijere.v14i4.32565

Akses terbuka Jurnal SINTA 1

Determinants of artificial intelligence acceptance among undergraduates

International Journal of Evaluation and Research in Education (IJERE) · Vol. 14 No. 4 (2025) · hlm. 2773–2780

DOI: 10.11591/ijere.v14i4.32565
ISSN: 2620-5440, 2252-8822
Terbit: 2025-08-01
Penerbit: Institute of Advanced Engineering and Science
Bahasa: eng
Lisensi: creativecommons.org/licenses/by-nc/4.0

Abstrak

Despite the potential benefits of artificial intelligence (AI) brings to education, its extensive use does not automatically guarantee effective integration or consistent improvements in learning. Hence, this research aims to identify the determinants of AI acceptance among undergraduates and examine the relationship between these determinants and AI acceptance. Five determinants of AI acceptance were identified based on the technology acceptance model (TAM) and empirical evidence: perceived effectiveness of AI, user satisfaction, user attitude toward AI technology, attitude toward using AI, and user self-efficacy. This quantitative study focused on 791 undergraduates from a management school in Malaysia. A questionnaire was distributed to 310 undergraduates using a stratified sampling method, and 259 responses were collected. Descriptive analysis results indicated that undergraduates perceive attitudes toward AI technology and using AI as very important determinants of AI acceptance. Pearson correlation analysis also revealed that four determinants (perceived effectiveness of AI, satisfaction in using AI, attitude towards AI technology, attitude towards using AI) significantly correlated with AI acceptance. This finding suggests that, within the context of AI acceptance among management school undergraduates, attitude-related determinants are the primary drivers. The findings from this research could be used by the management school as a reference to enhance undergraduates’ AI acceptance levels and identify areas for inclusive education system improvement.

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25 penulis · 89 tautan kepenulisan bersama · 51 lintas organisasi

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University of Technology Malaysia
INTI International University
Universiti Malaysia Terengganu
Southern Taiwan University of Science and Technology
Raffles University
Raffles University Iskandar
University of Kuala Lumpur
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Rekan penulis	Organisasi	Artikel bersama
Lim Lee Ping	University of Technology Malaysia	15
Choi Sang Long	Raffles University	8
Amran Rasli	INTI International University	7
Mohd Saiful Izwaan Saadon	Universiti Malaysia Terengganu	7
Chi-Hua Wu	Southern Taiwan University of Science and Technology	6
Mohamad Fawzy Bakry	University of Technology Malaysia	4
How Wen Yen	University of Technology Malaysia	4
Lim Peidi	University of Technology Malaysia	4
Baharudin Kadir	University of Kuala Lumpur	4
Siti Zulaiha Binti Abdulah Apandi	University of Technology Malaysia	3
Mohamad Rosni Othman	Universiti Malaysia Terengganu	3
Chong Hui Shi	University of Technology Malaysia	3
Goh Chin Fei	University of Technology Malaysia	3
Adriana Mohd Rizal	University of Technology Malaysia	3
Puong Koh Hii	Raffles University Iskandar	2
Ri Wei Mah	University of Technology Malaysia	2
Wushuang Li	University of Technology Malaysia	2
Zhou Fei	Shinawatra University	2
Hii Puong Koh	UCSI University	2
Chin Fei Goh	University of Technology Malaysia	1
Sang Long Choi	University of Technology Malaysia	1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijere.iaescore.com/index.php/IJERE/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.

Metadata asli (oai_dc)

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	<dc:title xml:lang="en-US">Determinants of artificial intelligence acceptance among undergraduates</dc:title>
	<dc:creator>Kowang, Tan Owee</dc:creator>
	<dc:creator>Kim Yew, Lim</dc:creator>
	<dc:creator>Chin Fei, Goh</dc:creator>
	<dc:creator>Choon Hee, Ong</dc:creator>
	<dc:subject xml:lang="en-US">Acceptance of AI; AI user satisfaction; AI user self-efficacy; Artificial Intelligence; Attitude toward AI technology; Attitude toward using AI; Perceived AI effectiveness</dc:subject>
	<dc:description xml:lang="en-US">Despite the potential benefits of artificial intelligence (AI) brings to education, its extensive use does not automatically guarantee effective integration or consistent improvements in learning. Hence, this research aims to identify the determinants of AI acceptance among undergraduates and examine the relationship between these determinants and AI acceptance. Five determinants of AI acceptance were identified based on the technology acceptance model (TAM) and empirical evidence: perceived effectiveness of AI, user satisfaction, user attitude toward AI technology, attitude toward using AI, and user self-efficacy. This quantitative study focused on 791 undergraduates from a management school in Malaysia. A questionnaire was distributed to 310 undergraduates using a stratified sampling method, and 259 responses were collected. Descriptive analysis results indicated that undergraduates perceive attitudes toward AI technology and using AI as very important determinants of AI acceptance. Pearson correlation analysis also revealed that four determinants (perceived effectiveness of AI, satisfaction in using AI, attitude towards AI technology, attitude towards using AI) significantly correlated with AI acceptance. This finding suggests that, within the context of AI acceptance among management school undergraduates, attitude-related determinants are the primary drivers. The findings from this research could be used by the management school as a reference to enhance undergraduates’ AI acceptance levels and identify areas for inclusive education system improvement.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
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	<dc:date>2025-08-01</dc:date>
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	<dc:identifier>10.11591/ijere.v14i4.32565</dc:identifier>
	<dc:source xml:lang="en-US">International Journal of Evaluation and Research in Education (IJERE); Vol 14, No 4: August 2025; 2773-2780</dc:source>
	<dc:source>2620-5440</dc:source>
	<dc:source>2252-8822</dc:source>
	<dc:source>10.11591/ijere.v14i4</dc:source>
	<dc:language>eng</dc:language>
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	<dc:rights xml:lang="en-US">Copyright (c) 2025 Institute of Advanced Engineering and Science</dc:rights>
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