From Assistance to Autonomy: AI Integration in Structured Research-Based Learning for Higher Education.
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| Title: | From Assistance to Autonomy: AI Integration in Structured Research-Based Learning for Higher Education. |
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| Authors: | Festiyed, Festiyed1 festiyed@fmipa.unp.ac.id, Desnita, Desnita2 desnita@fmipa.unp.ac.id, Natasya, Ziola1 ziolanatasya@student.unp.ac.id, Fadillah, Muhammad Aizri1 m.aizrifadillah@gmail.com, Novitra, Fuja2 fujanovitra@fmipa.unp.ac.id |
| Source: | Electronic Journal of e-Learning. 2026, Vol. 24 Issue 1, p109-124. 16p. |
| Subject Terms: | *Artificial intelligence, *Inquiry-based learning, *Cognitive ability, *STEM education, *Educational technology, *Autonomy (Psychology), *Higher education, *Physics education |
| Abstract: | Despite the growing interest in artificial intelligence (AI) for science education, little is known about its role within structured research-based learning (RBL) frameworks that balance technological assistance with developing independent research competencies. Existing studies often focus on AI as an isolated tool or a single-stage intervention, leaving a gap in understanding how AI can be systematically embedded across the research process without diminishing students' cognitive engagement. This study addresses that gap by implementing the newly developed IFTAR model, which organizes RBL into five sequential phases--Identification, Find Literature, Determine Methodology, Accommodate/Analyze/Interpret Data, and Report & Present--with AI selectively integrated into the literature search and data analysis stages. A quasi-experimental, non-equivalent control group PreTest--PostTest design was conducted with ninety undergraduate physics education students assigned to one control and two experimental groups. Cognitive outcomes were measured using a validated instrument and analyzed through classical ANCOVA, rank-based ANCOVA, and robust ANCOVA to account for assumption violations. Across all analytical approaches, both experimental groups significantly outperformed the control group, with no significant difference between the experimental conditions. These findings demonstrate that phase-specific AI integration within a transparent and scaffolded RBL framework can enhance cognitive performance while preserving methodological autonomy, offering a replicable model for purposeful AI use in STEM higher education. [ABSTRACT FROM AUTHOR] |
| Copyright of Electronic Journal of e-Learning is the property of Academic Conferences & Publishing International Ltd. 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 |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 191559157 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: From Assistance to Autonomy: AI Integration in Structured Research-Based Learning for Higher Education. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Festiyed%2C+Festiyed%22">Festiyed, Festiyed</searchLink><relatesTo>1</relatesTo><i> festiyed@fmipa.unp.ac.id</i><br /><searchLink fieldCode="AR" term="%22Desnita%2C+Desnita%22">Desnita, Desnita</searchLink><relatesTo>2</relatesTo><i> desnita@fmipa.unp.ac.id</i><br /><searchLink fieldCode="AR" term="%22Natasya%2C+Ziola%22">Natasya, Ziola</searchLink><relatesTo>1</relatesTo><i> ziolanatasya@student.unp.ac.id</i><br /><searchLink fieldCode="AR" term="%22Fadillah%2C+Muhammad+Aizri%22">Fadillah, Muhammad Aizri</searchLink><relatesTo>1</relatesTo><i> m.aizrifadillah@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Novitra%2C+Fuja%22">Novitra, Fuja</searchLink><relatesTo>2</relatesTo><i> fujanovitra@fmipa.unp.ac.id</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Electronic+Journal+of+e-Learning%22">Electronic Journal of e-Learning</searchLink>. 2026, Vol. 24 Issue 1, p109-124. 16p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22Inquiry-based+learning%22">Inquiry-based learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Cognitive+ability%22">Cognitive ability</searchLink><br />*<searchLink fieldCode="DE" term="%22STEM+education%22">STEM education</searchLink><br />*<searchLink fieldCode="DE" term="%22Educational+technology%22">Educational technology</searchLink><br />*<searchLink fieldCode="DE" term="%22Autonomy+%28Psychology%29%22">Autonomy (Psychology)</searchLink><br />*<searchLink fieldCode="DE" term="%22Higher+education%22">Higher education</searchLink><br />*<searchLink fieldCode="DE" term="%22Physics+education%22">Physics education</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Despite the growing interest in artificial intelligence (AI) for science education, little is known about its role within structured research-based learning (RBL) frameworks that balance technological assistance with developing independent research competencies. Existing studies often focus on AI as an isolated tool or a single-stage intervention, leaving a gap in understanding how AI can be systematically embedded across the research process without diminishing students' cognitive engagement. This study addresses that gap by implementing the newly developed IFTAR model, which organizes RBL into five sequential phases--Identification, Find Literature, Determine Methodology, Accommodate/Analyze/Interpret Data, and Report & Present--with AI selectively integrated into the literature search and data analysis stages. A quasi-experimental, non-equivalent control group PreTest--PostTest design was conducted with ninety undergraduate physics education students assigned to one control and two experimental groups. Cognitive outcomes were measured using a validated instrument and analyzed through classical ANCOVA, rank-based ANCOVA, and robust ANCOVA to account for assumption violations. Across all analytical approaches, both experimental groups significantly outperformed the control group, with no significant difference between the experimental conditions. These findings demonstrate that phase-specific AI integration within a transparent and scaffolded RBL framework can enhance cognitive performance while preserving methodological autonomy, offering a replicable model for purposeful AI use in STEM higher education. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Electronic Journal of e-Learning is the property of Academic Conferences & Publishing International Ltd. 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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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.34190/ejel.24.1.4416 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 109 Subjects: – SubjectFull: Artificial intelligence Type: general – SubjectFull: Inquiry-based learning Type: general – SubjectFull: Cognitive ability Type: general – SubjectFull: STEM education Type: general – SubjectFull: Educational technology Type: general – SubjectFull: Autonomy (Psychology) Type: general – SubjectFull: Higher education Type: general – SubjectFull: Physics education Type: general Titles: – TitleFull: From Assistance to Autonomy: AI Integration in Structured Research-Based Learning for Higher Education. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Festiyed, Festiyed – PersonEntity: Name: NameFull: Desnita, Desnita – PersonEntity: Name: NameFull: Natasya, Ziola – PersonEntity: Name: NameFull: Fadillah, Muhammad Aizri – PersonEntity: Name: NameFull: Novitra, Fuja IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 14794403 Numbering: – Type: volume Value: 24 – Type: issue Value: 1 Titles: – TitleFull: Electronic Journal of e-Learning Type: main |
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