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Cited by 17Innovative Teaching and Learning MethodsArtificial Intelligence in EducationEducation and Learning Interventions

The Self-Regulation for AI-Based Learning Scale: Psychometric Properties and Validation

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Yurt, Eyüp·2025·International Journal of Current Educational Studies
DOI 10.46328/ijces.176
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Abstract

Artificial intelligence technologies are transforming university students' learning processes, making self-regulation skills increasingly crucial. However, existing self-regulation scales inadequately reflect AI-assisted learning environments' unique dynamics. This study developed the Self-Regulation for AI-Based Learning Scale (SRAILS) for university students and examined its psychometric properties. The scale comprises four main dimensions and nine sub-dimensions: motivational components (intrinsic/extrinsic motivation, self-efficacy), cognitive/metacognitive strategies, time and task management, resource management, and technological adaptation. The study included 750 university students from various Turkish faculties. Exploratory Factor Analysis supported construct validity, while Confirmatory Factor Analysis confirmed the nine-factor structure, demonstrating good model fit. Convergent and discriminant validity were established. Cronbach's alpha and McDonald's omega reliability coefficients ranged from .853-.913. Criterion-related validity was confirmed through significant positive correlations between all scale dimensions and external criteria: academic GPA, technology interest, and digital literacy levels (r = .20-.60, p < .01). SRAILS provides a comprehensive, reliable assessment of students' self-regulation skills in AI-assisted learning environments. This scale contributes originally to literature by enabling personalized learning experience design and supporting effective instructional strategy development.

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  • Recent psychometric work proposes a four-pillar self-regulated learning profile tailored to AI contexts-motivational components, cognitive/metacognitive strategies, time and task management, and environmental/technological self-regulation-with solid construct validity and criterion links to GPA, technology interest, and digital literacy.

    “Recent psychometric work proposes a four-pillar self-regulated learning profile tailored to AI contexts-motivational components (intrinsic/extrinsic motivation, selfefficacy), cognitive/metacognitive strategies, time and task management, and environmental/technological self-regulation-with solid construct validity and criterion links to GPA, technology interest, and digital literacy [36].”

    From Artificial Intelligence in Higher Education: A State-of-the-Art Overview of Pedagogical Integrity, Artificial Intelligence Literacy, and Policy Integration (2025) · cites this paper as [36]

  • Goal setting, monitoring/evaluating AI outputs, attention control, time-boxing, resource vetting, and technological adaptation are core competencies for responsible, independent learning with LLMs and learning analytics.

    “These findings imply that goal setting, monitoring/evaluating AI outputs, attention control, time-boxing, resource vetting, and technological adaptation are core competencies for responsible, independent learning with LLMs and learning analytics.”

    From Artificial Intelligence in Higher Education: A State-of-the-Art Overview of Pedagogical Integrity, Artificial Intelligence Literacy, and Policy Integration (2025) · cites this paper as [36]

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Authors
Yurt, E.
Affiliations
Bursa Uludağ Üni̇versi̇tesi̇ (TR)
Corresponding author
Yurt, Eyüp
ORCID
Yurt, Eyüp
Venue
International Journal of Current Educational Studies
Publisher
ISTES Organization
Published
2025-06-22
Volume
4
Issue
1
Pages
95-118
ISSN
2822-4914 (ISSN-L)
DOI
10.46328/ijces.176
OpenAlex
W4411993198
Cited by
17
References
13
Citations by year
2026: 13 · 2025: 4
FWCI
34.11
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Current Educational Studies (publishedVersion, cc-by-nc)
  • Bursa Uludag University - AVESIS (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Physical Sciences › Computer Science › Information Systems › Education and Learning Interventions
Keywords
student self-regulation, AI-assisted learning, scale development, university students, intrinsic and extrinsic motivation, self-efficacy, metacognitive strategies, resource management, technology adaptation, digital literacy, academic performance

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