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<p>As artificial intelligence (AI) becomes integrated into higher education, its impact on doctoral students’ innovativeness warrants scholarly attention. Grounded in self-determination theory (SDT), this study examines how AI knowledge influences doctoral students’ innovation behavior, with AI dependence serving as a mediator and achievement motivation as a moderator. Data were collected from 607 doctoral students in China. Structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) were used. SEM results show that AI knowledge negatively predicts AI dependence (B = –0.36, p &lt; 0.001). AI dependence mediates the relationship between AI knowledge and innovation behavior (B = 0.07, p &lt; 0.01), and achievement motivation weakens the negative relationship between AI dependence and innovation behavior (B = 0.25, p &lt; 0.001). fsQCA identifies two configurational pathways associated with low AI dependence and high achievement motivation. The study expands SDT in educational technology and provides practical implications for fostering AI literacy and promoting innovation among doctoral students.</p>

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Depend less, think more: How AI knowledge empowers doctoral students’ innovation behavior

  • 期刊论文

  • 发表日期：

    2026年1月29日

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  • Xuan He,
  • Huan Huang,
  • Jiangyu Li

摘要

As artificial intelligence (AI) becomes integrated into higher education, its impact on doctoral students’ innovativeness warrants scholarly attention. Grounded in self-determination theory (SDT), this study examines how AI knowledge influences doctoral students’ innovation behavior, with AI dependence serving as a mediator and achievement motivation as a moderator. Data were collected from 607 doctoral students in China. Structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) were used. SEM results show that AI knowledge negatively predicts AI dependence (B = –0.36, p < 0.001). AI dependence mediates the relationship between AI knowledge and innovation behavior (B = 0.07, p < 0.01), and achievement motivation weakens the negative relationship between AI dependence and innovation behavior (B = 0.25, p < 0.001). fsQCA identifies two configurational pathways associated with low AI dependence and high achievement motivation. The study expands SDT in educational technology and provides practical implications for fostering AI literacy and promoting innovation among doctoral students.