{"title":"基于genai的个性化教育内容系统的设计与评价,该系统为适应性学习量身定制了个性特征和情感反应","authors":"Wentao Hu , Zichen Shao","doi":"10.1016/j.chbr.2025.100735","DOIUrl":null,"url":null,"abstract":"<div><div>This research integrates personality traits and emotional responses with GenAI to create personalized educational content. Using a design-based approach, the Psychologically-Aware Generative Education (PAGE) system was developed to adapt learning materials based on learners' Big Five personality profiles and real-time emotional feedback. Quasi-experimental testing with 200 university students demonstrated that PAGE significantly enhanced emotional satisfaction (4.4/5 vs 3.6/5, Cohen's d = 1.05) and learning engagement compared to traditional adaptive systems, with 22 % higher task completion rates (87.6 % vs 72.3 %) and 34 % increased study duration. The system successfully tailored content style, difficulty, and support mechanisms according to individual psychological characteristics. Content personalization was particularly effective for students with high neuroticism, reducing dropout rates by 48 % and negative emotions. This study provides empirical evidence that psychological adaptation in educational technology produces more engaging learning experiences than solely cognitive-based approaches, contributing design principles for developing psychologically-aware AI systems in education. These findings offer practical implications for educational institutions seeking to implement more humanized and culturally responsive technological solutions.</div></div>","PeriodicalId":72681,"journal":{"name":"Computers in human behavior reports","volume":"19 ","pages":"Article 100735"},"PeriodicalIF":5.8000,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Design and evaluation of a GenAI-based personalized educational content system tailored to personality traits and emotional responses for adaptive learning\",\"authors\":\"Wentao Hu , Zichen Shao\",\"doi\":\"10.1016/j.chbr.2025.100735\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This research integrates personality traits and emotional responses with GenAI to create personalized educational content. Using a design-based approach, the Psychologically-Aware Generative Education (PAGE) system was developed to adapt learning materials based on learners' Big Five personality profiles and real-time emotional feedback. Quasi-experimental testing with 200 university students demonstrated that PAGE significantly enhanced emotional satisfaction (4.4/5 vs 3.6/5, Cohen's d = 1.05) and learning engagement compared to traditional adaptive systems, with 22 % higher task completion rates (87.6 % vs 72.3 %) and 34 % increased study duration. The system successfully tailored content style, difficulty, and support mechanisms according to individual psychological characteristics. Content personalization was particularly effective for students with high neuroticism, reducing dropout rates by 48 % and negative emotions. This study provides empirical evidence that psychological adaptation in educational technology produces more engaging learning experiences than solely cognitive-based approaches, contributing design principles for developing psychologically-aware AI systems in education. These findings offer practical implications for educational institutions seeking to implement more humanized and culturally responsive technological solutions.</div></div>\",\"PeriodicalId\":72681,\"journal\":{\"name\":\"Computers in human behavior reports\",\"volume\":\"19 \",\"pages\":\"Article 100735\"},\"PeriodicalIF\":5.8000,\"publicationDate\":\"2025-07-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computers in human behavior reports\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2451958825001502\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PSYCHOLOGY, EXPERIMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers in human behavior reports","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2451958825001502","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, EXPERIMENTAL","Score":null,"Total":0}
引用次数: 0
摘要
该研究将人格特征和情感反应与GenAI结合起来,创建个性化的教育内容。采用基于设计的方法,开发了心理感知生成教育(PAGE)系统,根据学习者的大五人格概况和实时情绪反馈来调整学习材料。对200名大学生进行的准实验测试表明,与传统的适应系统相比,PAGE显著提高了情绪满意度(4.4/5 vs 3.6/5, Cohen’s d = 1.05)和学习参与度,任务完成率提高了22% (87.6% vs 72.3%),学习时间增加了34%。该系统成功地根据个体的心理特点量身定制了内容风格、难度和支持机制。内容个性化对高度神经质的学生特别有效,减少了48%的辍学率和负面情绪。本研究提供了经验证据,表明教育技术中的心理适应比单纯基于认知的方法产生更吸引人的学习体验,为开发教育中具有心理意识的人工智能系统提供了设计原则。这些发现为教育机构寻求实施更加人性化和文化响应的技术解决方案提供了实际意义。
Design and evaluation of a GenAI-based personalized educational content system tailored to personality traits and emotional responses for adaptive learning
This research integrates personality traits and emotional responses with GenAI to create personalized educational content. Using a design-based approach, the Psychologically-Aware Generative Education (PAGE) system was developed to adapt learning materials based on learners' Big Five personality profiles and real-time emotional feedback. Quasi-experimental testing with 200 university students demonstrated that PAGE significantly enhanced emotional satisfaction (4.4/5 vs 3.6/5, Cohen's d = 1.05) and learning engagement compared to traditional adaptive systems, with 22 % higher task completion rates (87.6 % vs 72.3 %) and 34 % increased study duration. The system successfully tailored content style, difficulty, and support mechanisms according to individual psychological characteristics. Content personalization was particularly effective for students with high neuroticism, reducing dropout rates by 48 % and negative emotions. This study provides empirical evidence that psychological adaptation in educational technology produces more engaging learning experiences than solely cognitive-based approaches, contributing design principles for developing psychologically-aware AI systems in education. These findings offer practical implications for educational institutions seeking to implement more humanized and culturally responsive technological solutions.