AI作业批改中的表情符号反馈对学生学习动机的影响研究综述

Review on the Impact of Emoji Feedback in AI Homework Grading on Student Learning Motivation

摘要: 人工智能技术在作业批改领域的应用逐渐从标准化评分扩展至情感化反馈，其中表情符号作为一种新型情绪传递载体，对学生的学习动机产生多维度影响。本文通过系统梳理近五年教育技术学与教育心理学相关研究，聚焦AI批改系统使用表情符号代替传统评语的实验效果。研究发现，符号类型与呈现形式的差异显著影响学生的归因风格：高频正向符号可促进努力归因导向，增强任务持久性；负向符号易诱发能力归因偏差，尤其对高焦虑学生存在动机抑制风险。在应用机制层面，符号的“拟人化”特质虽能提升反馈亲和力，却可能引发情感依赖与文化理解冲突。基于归因理论框架，本文进一步提出“符号–文字动态组合”及“学生定制化反馈库”等教育适应性策略，以平衡人机协同中情绪支持的即时性与科学性。未来研究需深化跨文化比较及长期效应追踪，为AI教育的本土化情感反馈设计提供证据支持。

Abstract: The application of artificial intelligence technology in homework grading has expanded from standardized scoring to affective feedback. As a novel emotional communication medium, emojis exert multidimensional effects on students’ learning motivation. This study systematically reviews five years of research in educational technology and psychology, focusing on the experimental effects of AI grading systems using emojis to replace traditional comments. Findings reveal that differences in emoji types and presentation formats significantly influence students’ attribution styles: frequent positive emojis promote effort-oriented attribution and enhance task persistence, while negative emojis may induce ability attribution bias, particularly posing motivational suppression risks for high-anxiety students. At the application mechanism level, the “anthropomorphic” characteristics of emojis enhance feedback affinity but may trigger emotional dependency and cultural comprehension conflicts. Based on attribution theory frameworks, this paper proposes educational adaptive strategies such as “emoji-text dynamic combinations” and “student-customized feedback libraries” to balance immediacy and scientific rigor in human-machine collaborative emotional support. Future research should deepen cross-cultural comparisons and long-term effect tracking to provide evidence-based support for localized affective feedback design in AI education.