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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2504.07125 |
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| _version_ | 1866909573492768768 |
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| author | Long Zhang Shijun Chen |
| author_facet | Long Zhang Shijun Chen |
| contents | Self-regulated learning (SRL) and Artificial-Intelligence (AI) literacy are becoming key competencies for successful human-AI interactive learning, vital to future education. However, despite their importance, students face imbalanced and underdeveloped SRL and AI literacy capabilities, inhibiting effective using AI for learning. This study analyzed data from 1,704 Chinese undergraduates using clustering methods to uncover four learner groups reflecting developing process(Potential, Development, Master, and AI-Inclined) characterized by varying SRL and AI literacy differentiation. Results highlight obvious disparities in SRL and AI literacy synchronization, with the Master Group achieving balanced development and critical AI-using for SRL, while AI-Inclined Group demonstrate over-reliance on AI and poor SRL application. The Potential Group showed a close mutual promotion trend between SRL and AI literacy, while the Development Group showed a discrete correlation. Resources and instructional guidance support emerged as key factors affecting these differentiations. To translate students to master SRL-AI literacy level and progress within it, the study proposes differentiated support strategies and suggestions. Synergizing SRL and AI literacy growth is the core of development, ensuring equitable and advanced human-centered interactive learning models for future human-AI integrating. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_07125 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Synergizing Self-Regulation and Artificial-Intelligence Literacy Towards Future Human-AI Integrative Learning Long Zhang Shijun Chen Computers and Society Self-regulated learning (SRL) and Artificial-Intelligence (AI) literacy are becoming key competencies for successful human-AI interactive learning, vital to future education. However, despite their importance, students face imbalanced and underdeveloped SRL and AI literacy capabilities, inhibiting effective using AI for learning. This study analyzed data from 1,704 Chinese undergraduates using clustering methods to uncover four learner groups reflecting developing process(Potential, Development, Master, and AI-Inclined) characterized by varying SRL and AI literacy differentiation. Results highlight obvious disparities in SRL and AI literacy synchronization, with the Master Group achieving balanced development and critical AI-using for SRL, while AI-Inclined Group demonstrate over-reliance on AI and poor SRL application. The Potential Group showed a close mutual promotion trend between SRL and AI literacy, while the Development Group showed a discrete correlation. Resources and instructional guidance support emerged as key factors affecting these differentiations. To translate students to master SRL-AI literacy level and progress within it, the study proposes differentiated support strategies and suggestions. Synergizing SRL and AI literacy growth is the core of development, ensuring equitable and advanced human-centered interactive learning models for future human-AI integrating. |
| title | Synergizing Self-Regulation and Artificial-Intelligence Literacy Towards Future Human-AI Integrative Learning |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2504.07125 |