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ADOPTION OF ARTIFICIAL INTELLIGENCE IN STEM LEARNING: EXAMINING THE EFFECTS OF PERFORMANCE, EFFORT, AND SOCIAL FACTORS

Jurnal Ilmiah Ilmu Terapan Universitas Jambi · Vol. 9 No. 4 (2025) · pp. 1341–1354

DOI
10.22437/jiituj.v9i4.44475
ISSN
2580-2259, 2580-2240
Published
2025-10-12
Publisher
LPPM Universitas Jambi
Language
en
License
creativecommons.org/licenses/by/4.0

Abstract

Artificial Intelligence (AI) has recently gained prominence in higher education, particularly in Science, Technology, Engineering, and Mathematics (STEM) disciplines, offering transformative potential for learning and innovation. However, students’ adoption of AI tools is influenced by multiple psychological and contextual factors. This study aims to examine the effects of performance expectancy, effort expectancy, and social influence on students’ behavioral intentions to integrate AI into STEM education. A quantitative research design was employed, involving 203 undergraduate students from the University of Jambi and Universitas PGRI Mahadewa Indonesia. Data were analyzed using Structural Equation Modeling (SEM) through SmartPLS 3.3 to identify direct and mediating relationships among variables. The findings revealed that performance expectancy significantly influenced students’ behavioral intentions, indicating that perceived usefulness of AI outweighs ease of use in determining adoption. Effort expectancy also had a substantial effect and mediated the relationship between performance expectancy and behavioral intentions, while social influence showed no significant impact. These results highlight that students’ engagement with AI in STEM learning is driven more by perceived academic and functional benefits than by peer or social reinforcement. The novelty of this study lies in its integration of the Unified Theory of Acceptance and Use of Technology (UTAUT) framework with the STEM education context in developing countries, providing new empirical insights into AI adoption behavior. The study recommends designing AI-supported learning environments that emphasize practical benefits, user-friendly interfaces, and pedagogical integration to enhance students’ learning outcomes and technological readiness.

Keywords

Collaboration network of the authors

40 authors · 100 co-authorship links · 1 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Halu Oleo
  • Universitas Ahmad Dahlan
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Adrian TawaiUniversitas Halu Oleo4
Sahiruddin SahiruddinUniversitas Ahmad Dahlan3
La Ode Muhammad ElwanUniversitas Halu Oleo3
La Ode AslimUniversitas Halu Oleo3
Ambo AkoAffiliation unknown2
Djoni Prawira RahardjaAffiliation unknown2
Sartono SartonoUniversitas Halu Oleo2
Arifin UthaUniversitas Halu Oleo2
Nur Fitriani Usdyana AtthamidAffiliation unknown2
Sri IndriatiAffiliation unknown2
Haryanto AsriAffiliation unknown2
Abdul Latief TolengAffiliation unknown2
Sudirman BacoAffiliation unknown2
Lellah RahimAffiliation unknown2
Zulkharnaim MaupaAffiliation unknown2
Isnawaty IsnawatyAffiliation unknown1
Rahmat RamadhanUniversitas Halu Oleo1
la ode Maksar MuhurunaAffiliation unknown1
Ayi Nur FauziahUniversitas Halu Oleo1
Muhammad AmirUniversitas Halu Oleo1
Saidin SaidinUniversitas Halu Oleo1
Abdul Latif TolengAffiliation unknown1
Rika HaryaniAffiliation unknown1
mawardi A. Asja ZulkarnaimAffiliation unknown1
Taufik TaufikUniversitas Halu Oleo1
Amalan AmalanAffiliation unknown1
Eka SuaibUniversitas Halu Oleo1
Amrullah AmrullahAffiliation unknown1
Alim BahriAffiliation unknown1
Ilham IlhamAffiliation unknown1
Muh AmirUniversitas Halu Oleo1
Ratu KhumaerahAffiliation unknown1
Fitria FitriaAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://online-journal.unja.ac.id/index.php/JIITUJ/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">ADOPTION OF ARTIFICIAL INTELLIGENCE IN STEM LEARNING: EXAMINING THE EFFECTS OF PERFORMANCE, EFFORT, AND SOCIAL FACTORS</dc:title>
	<dc:creator xml:lang="en">Sumandya, I Wayan</dc:creator>
	<dc:creator xml:lang="en">Widana, I Wayan</dc:creator>
	<dc:creator xml:lang="en">Hendra, Robi</dc:creator>
	<dc:creator xml:lang="en">Supian, Supian</dc:creator>
	<dc:creator xml:lang="en">Yusuf, Muhammad</dc:creator>
	<dc:creator xml:lang="en">Wijaya, Hansein Arif</dc:creator>
	<dc:creator xml:lang="en">Safitri, Rahma Ayu</dc:creator>
	<dc:subject xml:lang="en">Artificial Intelligence</dc:subject>
	<dc:subject xml:lang="en">Behavioral Intention</dc:subject>
	<dc:subject xml:lang="en">Effort Expectancy</dc:subject>
	<dc:subject xml:lang="en">Performance Expectancy</dc:subject>
	<dc:subject xml:lang="en">Social Influence</dc:subject>
	<dc:subject xml:lang="en">STEM</dc:subject>
	<dc:description xml:lang="en">Artificial Intelligence (AI) has recently gained prominence in higher education, particularly in Science, Technology, Engineering, and Mathematics (STEM) disciplines, offering transformative potential for learning and innovation. However, students’ adoption of AI tools is influenced by multiple psychological and contextual factors. This study aims to examine the effects of performance expectancy, effort expectancy, and social influence on students’ behavioral intentions to integrate AI into STEM education. A quantitative research design was employed, involving 203 undergraduate students from the University of Jambi and Universitas PGRI Mahadewa Indonesia. Data were analyzed using Structural Equation Modeling (SEM) through SmartPLS 3.3 to identify direct and mediating relationships among variables. The findings revealed that performance expectancy significantly influenced students’ behavioral intentions, indicating that perceived usefulness of AI outweighs ease of use in determining adoption. Effort expectancy also had a substantial effect and mediated the relationship between performance expectancy and behavioral intentions, while social influence showed no significant impact. These results highlight that students’ engagement with AI in STEM learning is driven more by perceived academic and functional benefits than by peer or social reinforcement. The novelty of this study lies in its integration of the Unified Theory of Acceptance and Use of Technology (UTAUT) framework with the STEM education context in developing countries, providing new empirical insights into AI adoption behavior. The study recommends designing AI-supported learning environments that emphasize practical benefits, user-friendly interfaces, and pedagogical integration to enhance students’ learning outcomes and technological readiness.</dc:description>
	<dc:publisher xml:lang="en">LPPM Universitas Jambi</dc:publisher>
	<dc:date>2025-10-12</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://online-journal.unja.ac.id/JIITUJ/article/view/44475</dc:identifier>
	<dc:identifier>10.22437/jiituj.v9i4.44475</dc:identifier>
	<dc:source xml:lang="en">Jurnal Ilmiah Ilmu Terapan Universitas Jambi; Vol. 9 No. 4 (2025): Volume 9, Nomor 4, December 2025; 1341-1354</dc:source>
	<dc:source>2580-2259</dc:source>
	<dc:source>2580-2240</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://online-journal.unja.ac.id/JIITUJ/article/view/44475/22268</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2025 I Wayan Sumandya, I Wayan Widana, Robi Hendra, Supian Supian, Muhammad Yusuf, Hansein Arif Wijaya, Rahma Ayu Safitri</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>