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Analysis of the Use of Artificial Intelligence (AI) to Boost Student Motivation and Reduce Boredom in History Classes

Educational Leadership and Management Journal · Vol. 4 No. 1 (2026) · pp. 108–127

DOI
10.22437/element.v4i1.56951
ISSN
3031-4712
Published
2026-06-02
Publisher
FKIP Universitas Jambi
Language
en

Abstract

Advances in artificial intelligence (AI) technology have created new opportunities for educational transformation, particularly in enhancing student motivation and reducing boredom in high school history learning. This study aims to examine the structural relationships among Teachers’ AI Competence (TAC), Multidimensional Work Motivation (MWMS), Students’ Learning Agility (SLA), Student Engagement (SE), and Boredom in Learning (BIL) within AI-supported history education. Using a quantitative approach, data were collected from 101 public high school students in Jambi Province and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings revealed that teachers’ AI competence positively and significantly influenced students’ learning agility and engagement. In addition, student engagement was found to significantly reduce boredom in learning, indicating that students who were more actively involved in classroom activities experienced lower levels of boredom. The proposed model demonstrated substantial explanatory power in predicting student engagement and boredom outcomes. These findings highlight the importance of strengthening teachers’ AI-related competencies to foster more adaptive, engaging, and meaningful learning experiences. This study contributes to the growing body of literature on AI integration in secondary education by providing empirical evidence from history learning contexts and offering practical implications for the design of innovative and student-centered instructional practices.

Keywords

Collaboration network of the authors

39 authors · 99 co-authorship links

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

  • Politeknik Negeri Ujung Pandang
  • Diponegoro University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Sofyan SofyanPoliteknik Negeri Ujung Pandang2
Dian AndriadiAffiliation unknown2
Akhmad HabibiAffiliation unknown2
Hadiyanto HadiyantoDiponegoro University2
Nunung FajaryaniAffiliation unknown2
Okky WulandariAffiliation unknown1
Habibullah HabibullahAffiliation unknown1
Eddy HaryantoAffiliation unknown1
Rossa Aprilia SiregarAffiliation unknown1
Delita SartikaAffiliation unknown1
Tri AndiniAffiliation unknown1
Rachmawati RachmawatiAffiliation unknown1
Muchlash AbrarAffiliation unknown1
Destrinelli DestrinelliAffiliation unknown1
Siti ArtikaAffiliation unknown1
Failasofah FailasofahAffiliation unknown1
Robin PratamaAffiliation unknown1
Priscilla Wulan TerstaAffiliation unknown1
Eddy Haryanto4Affiliation unknown1
Aprillizavivaryati AprillizavivaryatiAffiliation unknown1
Wila YurnitaAffiliation unknown1
M Haris ElwardiansyahAffiliation unknown1
Mohamad MuspawiAffiliation unknown1
Yantoro yantoroAffiliation unknown1
Ajuzar FiqhiAffiliation unknown1
Mulyadi MulyadiAffiliation unknown1
Fatriani FatrianiAffiliation unknown1
M. Rd. Ali M. Rd. AliAffiliation unknown1
Netty HerawatyAffiliation unknown1
Erivan Afri RantiAffiliation unknown1
Jatmiko Budi SantosoAffiliation unknown1
Atika JuniwatiAffiliation unknown1
Jumeiliani SirinAffiliation unknown1
Diva MusyaffaAffiliation unknown1
Maya Ramayang DiandikaAffiliation unknown1
Ariyanto BudimanAffiliation 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/element/oai, first harvested 6 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Analysis of the Use of Artificial Intelligence (AI) to Boost Student Motivation and Reduce Boredom in History Classes</dc:title>
	<dc:creator xml:lang="en">Maharani, Elsa Fauziah</dc:creator>
	<dc:creator xml:lang="en">Sulistyo, Urip</dc:creator>
	<dc:creator xml:lang="en">Masbirorotni, Masbirorotni</dc:creator>
	<dc:subject xml:lang="en">artificial intelligence, motivation to learn, boredom in learning, history education, student engagement</dc:subject>
	<dc:description xml:lang="en">Advances in artificial intelligence (AI) technology have created new opportunities for educational transformation, particularly in enhancing student motivation and reducing boredom in high school history learning. This study aims to examine the structural relationships among Teachers’ AI Competence (TAC), Multidimensional Work Motivation (MWMS), Students’ Learning Agility (SLA), Student Engagement (SE), and Boredom in Learning (BIL) within AI-supported history education. Using a quantitative approach, data were collected from 101 public high school students in Jambi Province and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings revealed that teachers’ AI competence positively and significantly influenced students’ learning agility and engagement. In addition, student engagement was found to significantly reduce boredom in learning, indicating that students who were more actively involved in classroom activities experienced lower levels of boredom. The proposed model demonstrated substantial explanatory power in predicting student engagement and boredom outcomes. These findings highlight the importance of strengthening teachers’ AI-related competencies to foster more adaptive, engaging, and meaningful learning experiences. This study contributes to the growing body of literature on AI integration in secondary education by providing empirical evidence from history learning contexts and offering practical implications for the design of innovative and student-centered instructional practices.</dc:description>
	<dc:publisher xml:lang="en">FKIP Universitas Jambi</dc:publisher>
	<dc:date>2026-06-02</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://online-journal.unja.ac.id/element/article/view/56951</dc:identifier>
	<dc:identifier>10.22437/element.v4i1.56951</dc:identifier>
	<dc:source xml:lang="en">Educational Leadership and Management Journal; Vol. 4 No. 1 (2026):  Element - 2026; 108-127</dc:source>
	<dc:source>3031-4712</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://online-journal.unja.ac.id/element/article/view/56951/24507</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2026 Educational Leadership and Management Journal</dc:rights>
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