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Academic performance in AI Era: salient factors in higher education.

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Title: Academic performance in AI Era: salient factors in higher education.
Authors: Hendra, Robi1, Rasyono, Rasyono2, Habibi, Akhmad1,3 akhmad.habibi@unja.ac.id, Yaqin, Lalu Nurul4, Alahmari, Sarah A.5, Alharmali, Turki Mesfer6, Wijaya, Hansein Arif1
Source: Journal of E-Learning & Knowledge Society. Aug2025, Vol. 21 Issue 2, p18-30. 13p.
Subject Terms: *Student engagement, *Higher education, *Academic achievement, *Data analysis, *Artificial intelligence, *Instructional systems, Structural equation modeling, Sampling methods
Abstract: This research integrates teacher AI competence (TAC), student learning agility (SLA), and student engagement (SE), as factors affecting student academic performance (SAP). We employed a survey methodology in which the instrument's validation was conducted through content and face validity, as well as a content validity index and measurement model in SmartPLS. A total of 380 lecturers from three universities participated as respondents in this survey study. Partial least squares structural equation modeling (PLS-SEM) procedures were employed for the primary data analysis of the study. The findings informed the validity and reliability of the model, highlighting the important roles of SLA and SA in relation to SAP. In addition, TAC was also correlated with SAP and SLA, while it has no relationship with SA. [ABSTRACT FROM AUTHOR]
Copyright of Journal of E-Learning & Knowledge Society is the property of Italian e-Learning Society / Societa Italiana di e-Learning (SIe-L) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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Header DbId: ehh
DbLabel: Education Research Complete
An: 190730729
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Academic performance in AI Era: salient factors in higher education.
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  Data: <searchLink fieldCode="AR" term="%22Hendra%2C+Robi%22">Hendra, Robi</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Rasyono%2C+Rasyono%22">Rasyono, Rasyono</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Habibi%2C+Akhmad%22">Habibi, Akhmad</searchLink><relatesTo>1,3</relatesTo><i> akhmad.habibi@unja.ac.id</i><br /><searchLink fieldCode="AR" term="%22Yaqin%2C+Lalu+Nurul%22">Yaqin, Lalu Nurul</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Alahmari%2C+Sarah+A%2E%22">Alahmari, Sarah A.</searchLink><relatesTo>5</relatesTo><br /><searchLink fieldCode="AR" term="%22Alharmali%2C+Turki+Mesfer%22">Alharmali, Turki Mesfer</searchLink><relatesTo>6</relatesTo><br /><searchLink fieldCode="AR" term="%22Wijaya%2C+Hansein+Arif%22">Wijaya, Hansein Arif</searchLink><relatesTo>1</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+E-Learning+%26+Knowledge+Society%22">Journal of E-Learning & Knowledge Society</searchLink>. Aug2025, Vol. 21 Issue 2, p18-30. 13p.
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  Data: *<searchLink fieldCode="DE" term="%22Student+engagement%22">Student engagement</searchLink><br />*<searchLink fieldCode="DE" term="%22Higher+education%22">Higher education</searchLink><br />*<searchLink fieldCode="DE" term="%22Academic+achievement%22">Academic achievement</searchLink><br />*<searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22Instructional+systems%22">Instructional systems</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+equation+modeling%22">Structural equation modeling</searchLink><br /><searchLink fieldCode="DE" term="%22Sampling+methods%22">Sampling methods</searchLink>
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  Label: Abstract
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  Data: This research integrates teacher AI competence (TAC), student learning agility (SLA), and student engagement (SE), as factors affecting student academic performance (SAP). We employed a survey methodology in which the instrument's validation was conducted through content and face validity, as well as a content validity index and measurement model in SmartPLS. A total of 380 lecturers from three universities participated as respondents in this survey study. Partial least squares structural equation modeling (PLS-SEM) procedures were employed for the primary data analysis of the study. The findings informed the validity and reliability of the model, highlighting the important roles of SLA and SA in relation to SAP. In addition, TAC was also correlated with SAP and SLA, while it has no relationship with SA. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of E-Learning & Knowledge Society is the property of Italian e-Learning Society / Societa Italiana di e-Learning (SIe-L) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.20368/1971-8829/1136015
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              Text: Aug2025
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