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<p>With the rapid development of generative artificial intelligence (GenAI), leveraging its capabilities to optimize video learning has emerged as a critical research topic. However, empirical evidence about its effectiveness and underlying cognitive mechanisms remains scarce. This study recruited 120 undergraduate and postgraduate students and employed multimodal analysis to investigate the impact of a GenAI-supported questioning mechanism (AI-VL mode) on learners’ intrinsic motivation, cognitive load, attention allocation, and learning performance. Results showed that the AI-VL mode significantly outperformed traditional video learning (TVL) across intrinsic motivation, cognitive load, and learning performance, and further outperformed embedded question-and-answering video learning (QA-VL) specifically in learning performance. Regarding cognitive processing, eye-tracking metrics confirmed that the AI-VL mode redistributed cognitive resources through real-time feedback, reducing intrinsic and extraneous cognitive load. Furthermore, Lag Sequential Analysis of interactive behaviors revealed a cross-modal attentional cycle, specifically the “feedback-viewing” path, which enabled the reallocation of liberated cognitive resources into germane cognitive load. However, cluster analysis revealed that most learners remained at a surface-level interaction stage. These findings provide empirical evidence and practical guidance for designing adaptive GenAI-driven educational scaffolds for future online video learning platforms.</p>

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From passive to interactive: exploring the effects of GenAI-enhanced video learning through multimodal analysis

  • 期刊论文

  • 发表日期：

    2026年6月29日

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  • Bin Jing,
  • Rui Liu,
  • Tianyou Du,
  • Qin Wang,
  • Hongliang Ma

摘要

With the rapid development of generative artificial intelligence (GenAI), leveraging its capabilities to optimize video learning has emerged as a critical research topic. However, empirical evidence about its effectiveness and underlying cognitive mechanisms remains scarce. This study recruited 120 undergraduate and postgraduate students and employed multimodal analysis to investigate the impact of a GenAI-supported questioning mechanism (AI-VL mode) on learners’ intrinsic motivation, cognitive load, attention allocation, and learning performance. Results showed that the AI-VL mode significantly outperformed traditional video learning (TVL) across intrinsic motivation, cognitive load, and learning performance, and further outperformed embedded question-and-answering video learning (QA-VL) specifically in learning performance. Regarding cognitive processing, eye-tracking metrics confirmed that the AI-VL mode redistributed cognitive resources through real-time feedback, reducing intrinsic and extraneous cognitive load. Furthermore, Lag Sequential Analysis of interactive behaviors revealed a cross-modal attentional cycle, specifically the “feedback-viewing” path, which enabled the reallocation of liberated cognitive resources into germane cognitive load. However, cluster analysis revealed that most learners remained at a surface-level interaction stage. These findings provide empirical evidence and practical guidance for designing adaptive GenAI-driven educational scaffolds for future online video learning platforms.