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<p>As Artificial Intelligence (AI) has been widely used in education, understanding older adults’ perceptions and acceptance of AI-supported learning is crucial for enhancing technology access and promoting lifelong learning. This study analyzed 75 older adults from diverse gender, education, and technical experience, collecting both hand-drawn and AI-generated images. These images were coded into six categories with 20 elements. Drawing-based Epistemic Network Analysis (ENA) was employed to analyze the results, while interviews provided supplementary insights into their perceptions. The findings revealed that (1) hand-drawn works reflected older adults’ life-based understanding, while AI-generated drawings contained more structured elements; (2) gender, education, and technical experience influenced the content and style of the drawings: males focused on self-improvement, females on emotions like parent–child relationships, those with higher education created more organized scenes, while those with lower education emphasized emotional expression, and more tech-savvy individuals were exploratory, while others required assistance; and (3) most older adults found AI tools practical and expressed willingness to use them. These results highlight key factors influencing technology adoption among older adults and underscore the need for educators to consider contextual variations in AI-supported learning.</p>

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Older adults’ conceptions of AI-supported learning: an epistemic network analysis based on AI-generated drawings

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月6日

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  • Shu Zhao,
  • Congxin Wu,
  • Ziqi Wang,
  • Meizhao Guo,
  • Xian Zhao

摘要

As Artificial Intelligence (AI) has been widely used in education, understanding older adults’ perceptions and acceptance of AI-supported learning is crucial for enhancing technology access and promoting lifelong learning. This study analyzed 75 older adults from diverse gender, education, and technical experience, collecting both hand-drawn and AI-generated images. These images were coded into six categories with 20 elements. Drawing-based Epistemic Network Analysis (ENA) was employed to analyze the results, while interviews provided supplementary insights into their perceptions. The findings revealed that (1) hand-drawn works reflected older adults’ life-based understanding, while AI-generated drawings contained more structured elements; (2) gender, education, and technical experience influenced the content and style of the drawings: males focused on self-improvement, females on emotions like parent–child relationships, those with higher education created more organized scenes, while those with lower education emphasized emotional expression, and more tech-savvy individuals were exploratory, while others required assistance; and (3) most older adults found AI tools practical and expressed willingness to use them. These results highlight key factors influencing technology adoption among older adults and underscore the need for educators to consider contextual variations in AI-supported learning.