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Peer-reviewedCC BYFrontiers in psychology

Teachers' self-regulated learning profiles in artificial intelligence contexts: implications for differentiated professional development.

  • Yuhao Su1
  • Mustafa Alpsülün2
  • Müge Vuran3
  • Hasan Celal Balıkçı2
  • Müzeyyen Barut4
4 affiliations
  1. 1Faculty of Education, Henan University, Kaifeng, China.
  2. 2Department of Computer Programming, Siverek Vocational School, Harran University, Şanlıurfa, Türkiye.
  3. 3Ministry of National Education of the Republic of Türkiye, Ankara, Türkiye.
  4. 4Institute of Educational Sciences, Fırat University, Elazig, Türkiye.
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Abstract

Introduction

The rapid integration of artificial intelligence (AI) into education requires teachers to develop new competencies and manage their own learning. While research has examined teachers' AI attitudes, acceptance, and literacy, less is known about how they regulate their learning in AI contexts. This study examined teachers' self-regulated learning (SRL) profiles and their associations with demographic characteristics and AI-related experience.Methods

In a descriptive survey, 213 in-service teachers completed the Artificial Intelligence Self-Regulated Learning Scale for Teachers (AI-SRLS). Two-Step Cluster Analysis was conducted using five dimensions: task planning, self-monitoring, help seeking, institutional support, and self-evaluation.Results

Two profiles emerged: High Strategic Learners, who scored consistently higher across all five dimensions, and Limited Strategy Users, who reported comparatively lower strategy use. Profile membership was significantly associated with prior AI experience and AI-related training. Gender, age, and professional experience did not significantly differentiate the profiles.Discussion

Teachers' AI-related SRL strategies co-occur in distinct profiles, supporting a differentiated approach to professional development. Teachers with limited strategy use may benefit from structured guidance, scaffolded AI learning opportunities, and stronger institutional support, while highly strategic learners may benefit from more autonomous and advanced learning opportunities.

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Cite

BibTeXRIS
BibTeX
@article{su2026teachers,
  title = {{Teachers' self-regulated learning profiles in artificial intelligence contexts: implications for differentiated professional development.}},
  author = {Yuhao Su and Mustafa Alpsülün and Müge Vuran and Hasan Celal Balıkçı and Müzeyyen Barut},
  journal = {Frontiers in psychology},
  year = {2026},
  month = oct,
  doi = {10.3389/fpsyg.2026.1885895},
  url = {https://europepmc.org/article/MED/42909089},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Teachers' self-regulated learning profiles in artificial intelligence contexts: implications for differentiated professional development.
AU  - Yuhao Su
AU  - Mustafa Alpsülün
AU  - Müge Vuran
AU  - Hasan Celal Balıkçı
AU  - Müzeyyen Barut
PY  - 2026
DA  - 2026/10/07
JO  - Frontiers in psychology
DO  - 10.3389/fpsyg.2026.1885895
UR  - https://europepmc.org/article/MED/42909089
AB  - Introduction  The rapid integration of artificial intelligence (AI) into education requires teachers to develop new competencies and manage their own learning. While research has examined teachers' AI attitudes, acceptance, and literacy, less is known about how they regulate their learning in AI contexts. This study examined teachers' self-regulated learning (SRL) profiles and their associations with demographic characteristics and AI-related experience.Methods  In a descriptive survey, 213 in-service teachers completed the Artificial Intelligence Self-Regulated Learning Scale for Teachers (AI-SRLS). Two-Step Cluster Analysis was conducted using five dimensions: task planning, self-monitoring, help seeking, institutional support, and self-evaluation.Results  Two profiles emerged: High Strategic Learners, who scored consistently higher across all five dimensions, and Limited Strategy Users, who reported comparatively lower strategy use. Profile membership was significantly associated with prior AI experience and AI-related training. Gender, age, and professional experience did not significantly differentiate the profiles.Discussion  Teachers' AI-related SRL strategies co-occur in distinct profiles, supporting a differentiated approach to professional development. Teachers with limited strategy use may benefit from structured guidance, scaffolded AI learning opportunities, and stronger institutional support, while highly strategic learners may benefit from more autonomous and advanced learning opportunities.
SN  - 1664-1078
ER  -