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<p>The role of generative artificial intelligence (GenAI) tools in education is becoming increasingly critical. Their purposeful integration into learning processes has become a key priority for supporting meaningful and collaborative learning. The capacity of GenAI tools such as ChatGPT to deliver personalized, real-time, and meaningful feedback holds the potential to support not only individual regulation but also co-regulated processes within collaborative learning communities. In this context, the effects of GenAI tools on self-regulated learning (SRL) should be examined not only from an individual perspective but also through the lens of socially shared regulation of learning (SSRL). Within the framework of self-determination theory (SDT), the collective-level needs for autonomy, competence, and relatedness are also key indicators for understanding students’ motivation in group-based learning environments. This study aimed to examine how ChatGPT-4o-mediated mathematical modeling (MM) activities address preservice teachers’ SSRL processes and collective motivational needs based on SDT. The study was conducted using the Delphi method, grounded in the expert judgments of specialists in the field. The panel included 10 experts with extensive experience in MM and the integration of technology into learning environments. In addition, 40 preservice mathematics teachers participated as the implementation group during the field application phase. Following the second Delphi round, field data were utilized as feedback prior to the third round to enrich the panelists’ evaluations and enhance the practical validity of the proposed activities. Each MM activity was designed based on expert input, in accordance with the transactive, deeply metacognitive, collectively agentic, and socio-historical/contextualized features of SSRL, as well as the collective need dimensions of SDT. As a result of the study, a multidimensional classification tool was developed, consisting of 18 ChatGPT-4o-mediated MM activities labeled through the frameworks of SSRL and collective-level SDT. This tool offers a comprehensive view of how GenAI tools can support regulation, motivation, and pedagogy in collaborative learning settings. It also provides meaningful guidance for MM instruction and contributes to broader standards for the pedagogical integration of GenAI in education.</p>

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A socially shared regulation of learning and self-determination theory-based classification of generative artificial intelligence-mediated mathematical modeling activities: the case of ChatGPT

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月15日

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  • Deniz Kaya,
  • Hatice Yildiz Durak

摘要

The role of generative artificial intelligence (GenAI) tools in education is becoming increasingly critical. Their purposeful integration into learning processes has become a key priority for supporting meaningful and collaborative learning. The capacity of GenAI tools such as ChatGPT to deliver personalized, real-time, and meaningful feedback holds the potential to support not only individual regulation but also co-regulated processes within collaborative learning communities. In this context, the effects of GenAI tools on self-regulated learning (SRL) should be examined not only from an individual perspective but also through the lens of socially shared regulation of learning (SSRL). Within the framework of self-determination theory (SDT), the collective-level needs for autonomy, competence, and relatedness are also key indicators for understanding students’ motivation in group-based learning environments. This study aimed to examine how ChatGPT-4o-mediated mathematical modeling (MM) activities address preservice teachers’ SSRL processes and collective motivational needs based on SDT. The study was conducted using the Delphi method, grounded in the expert judgments of specialists in the field. The panel included 10 experts with extensive experience in MM and the integration of technology into learning environments. In addition, 40 preservice mathematics teachers participated as the implementation group during the field application phase. Following the second Delphi round, field data were utilized as feedback prior to the third round to enrich the panelists’ evaluations and enhance the practical validity of the proposed activities. Each MM activity was designed based on expert input, in accordance with the transactive, deeply metacognitive, collectively agentic, and socio-historical/contextualized features of SSRL, as well as the collective need dimensions of SDT. As a result of the study, a multidimensional classification tool was developed, consisting of 18 ChatGPT-4o-mediated MM activities labeled through the frameworks of SSRL and collective-level SDT. This tool offers a comprehensive view of how GenAI tools can support regulation, motivation, and pedagogy in collaborative learning settings. It also provides meaningful guidance for MM instruction and contributes to broader standards for the pedagogical integration of GenAI in education.