Providing timely, targeted, and multimodal feedback helps students quickly correct errors, build deep understanding and stay motivated, yet making it at scale remains a challenge. This study introduces a real-time AI-facilitated multimodal feedback system that integrates structured textual explanations with dynamic multimedia resources, including the retrieved most relevant slide page references and streaming AI audio narration. In an online crowdsourcing experiment, we compared this system against fixed business-as-usual feedback by educators across three dimensions: (1) learning effectiveness, (2) learner engagement, (3) perceived feedback quality and value. Results showed that AI multimodal feedback achieved learning gains equivalent to original educator feedback while significantly outperforming it on perceived clarity, specificity, conciseness, motivation, satisfaction, and reducing cognitive load, with comparable correctness, trust, and acceptance. Process logs revealed distinct engagement patterns: for multiple-choice questions, educator feedback encouraged more submissions; for open-ended questions, AI-facilitated targeted suggestions lowered revision barriers and promoted iterative improvement. These findings highlight the potential of AI multimodal feedback to provide scalable, real-time, and context-aware support that both reduces instructor workload and enhances student experience.

翻译：提供及时、有针对性且多模态的反馈能帮助学生快速纠正错误、建立深刻理解并保持学习动力，然而大规模实现此类反馈仍具挑战。本研究引入一种实时AI辅助多模态反馈系统，该系统将结构化文本解释与动态多媒体资源相结合，包括检索到的最相关幻灯片页面引用以及流式AI音频解说。在一项在线众包实验中，我们从三个维度比较了该系统与教育者提供的固定常规反馈：(1)学习效果，(2)学习者参与度，(3)感知反馈质量与价值。结果显示，AI多模态反馈实现了与原始教育者反馈等效的学习增益，同时在感知清晰度、针对性、简洁性、激励性、满意度及降低认知负荷方面显著优于后者，而在正确性、可信度和接受度方面表现相当。过程日志揭示了不同的参与模式：对于选择题，教育者反馈鼓励了更多提交次数；对于开放式问题，AI辅助的针对性建议降低了修改障碍并促进了迭代改进。这些发现凸显了AI多模态反馈在提供可扩展、实时且情境感知支持方面的潜力，既能减轻教师工作量，又能提升学生学习体验。