{"title":"在智能教育游戏中通过迭代设计增强学生的体验和学习","authors":"Ryan Hare, Sarah Ferguson, Ying Tang","doi":"10.1111/bjet.13526","DOIUrl":null,"url":null,"abstract":"<div>\n \n <section>\n \n <p>With increasing interest in computer-assisted educa- tion, AI-integrated systems become highly applicable with their ability to adapt based on user interactions. In this context, this paper focuses on understanding and analysing first-year undergraduate student responses to an intelligent educational system that applies multi-agent reinforcement learning as an AI tutor. With human–computer interaction at the centre, we discuss principles of interface design and educational gamification in the context of multiple years of student observations, student feedback surveys and focus group interviews. We show positive feedback from the design methodology we discuss as well as the overall process of providing automated tutoring in a gamified virtual environment. We also discuss students' thinking in the context of gamified educational systems, as well as unexpected issues that may arise when implementing such systems. Ultimately, our design iterations and analysis both offer new insights for practical implementation of computer-assisted educational systems, focusing on how AI can augment, rather than replace, human intelligence in the classroom.</p>\n </section>\n \n <section>\n \n <div>\n \n <div>\n \n <h3>Practitioner notes</h3>\n <p>What is already known about this topic\n\n </p><ul>\n \n <li>AI-integrated systems show promise for personalizing learning and improving student education.</li>\n \n <li>Existing research has shown the value of personalized learner feedback.</li>\n \n <li>Engaged students learn more effectively.</li>\n </ul>\n <p>What this paper adds\n\n </p><ul>\n \n <li>Student opinions of and responses to an HCI-based personalized educational system.</li>\n \n <li>New insights for practical implementation of AI-integrated educational systems informed by years of student observations and system improvements.</li>\n \n <li>Qualitative insights into system design to improve human–computer interaction in educational systems.</li>\n </ul>\n <p>Implications for practice and/or policy\n\n </p><ul>\n \n <li>Actionable design principles for computer-assisted tutoring systems derived from first-hand student feedback and observations.</li>\n \n <li>Encourage new directions for human–computer interaction in educational systems.</li>\n </ul>\n </div>\n </div>\n </section>\n </div>","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 2","pages":"551-568"},"PeriodicalIF":6.7000,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bjet.13526","citationCount":"0","resultStr":"{\"title\":\"Enhancing student experience and learning with iterative design in an intelligent educational game\",\"authors\":\"Ryan Hare, Sarah Ferguson, Ying Tang\",\"doi\":\"10.1111/bjet.13526\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n <section>\\n \\n <p>With increasing interest in computer-assisted educa- tion, AI-integrated systems become highly applicable with their ability to adapt based on user interactions. In this context, this paper focuses on understanding and analysing first-year undergraduate student responses to an intelligent educational system that applies multi-agent reinforcement learning as an AI tutor. With human–computer interaction at the centre, we discuss principles of interface design and educational gamification in the context of multiple years of student observations, student feedback surveys and focus group interviews. We show positive feedback from the design methodology we discuss as well as the overall process of providing automated tutoring in a gamified virtual environment. We also discuss students' thinking in the context of gamified educational systems, as well as unexpected issues that may arise when implementing such systems. Ultimately, our design iterations and analysis both offer new insights for practical implementation of computer-assisted educational systems, focusing on how AI can augment, rather than replace, human intelligence in the classroom.</p>\\n </section>\\n \\n <section>\\n \\n <div>\\n \\n <div>\\n \\n <h3>Practitioner notes</h3>\\n <p>What is already known about this topic\\n\\n </p><ul>\\n \\n <li>AI-integrated systems show promise for personalizing learning and improving student education.</li>\\n \\n <li>Existing research has shown the value of personalized learner feedback.</li>\\n \\n <li>Engaged students learn more effectively.</li>\\n </ul>\\n <p>What this paper adds\\n\\n </p><ul>\\n \\n <li>Student opinions of and responses to an HCI-based personalized educational system.</li>\\n \\n <li>New insights for practical implementation of AI-integrated educational systems informed by years of student observations and system improvements.</li>\\n \\n <li>Qualitative insights into system design to improve human–computer interaction in educational systems.</li>\\n </ul>\\n <p>Implications for practice and/or policy\\n\\n </p><ul>\\n \\n <li>Actionable design principles for computer-assisted tutoring systems derived from first-hand student feedback and observations.</li>\\n \\n <li>Encourage new directions for human–computer interaction in educational systems.</li>\\n </ul>\\n </div>\\n </div>\\n </section>\\n </div>\",\"PeriodicalId\":48315,\"journal\":{\"name\":\"British Journal of Educational Technology\",\"volume\":\"56 2\",\"pages\":\"551-568\"},\"PeriodicalIF\":6.7000,\"publicationDate\":\"2024-10-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bjet.13526\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"British Journal of Educational Technology\",\"FirstCategoryId\":\"95\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/bjet.13526\",\"RegionNum\":1,\"RegionCategory\":\"教育学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"EDUCATION & EDUCATIONAL RESEARCH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"British Journal of Educational Technology","FirstCategoryId":"95","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/bjet.13526","RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
Enhancing student experience and learning with iterative design in an intelligent educational game
With increasing interest in computer-assisted educa- tion, AI-integrated systems become highly applicable with their ability to adapt based on user interactions. In this context, this paper focuses on understanding and analysing first-year undergraduate student responses to an intelligent educational system that applies multi-agent reinforcement learning as an AI tutor. With human–computer interaction at the centre, we discuss principles of interface design and educational gamification in the context of multiple years of student observations, student feedback surveys and focus group interviews. We show positive feedback from the design methodology we discuss as well as the overall process of providing automated tutoring in a gamified virtual environment. We also discuss students' thinking in the context of gamified educational systems, as well as unexpected issues that may arise when implementing such systems. Ultimately, our design iterations and analysis both offer new insights for practical implementation of computer-assisted educational systems, focusing on how AI can augment, rather than replace, human intelligence in the classroom.
Practitioner notes
What is already known about this topic
AI-integrated systems show promise for personalizing learning and improving student education.
Existing research has shown the value of personalized learner feedback.
Engaged students learn more effectively.
What this paper adds
Student opinions of and responses to an HCI-based personalized educational system.
New insights for practical implementation of AI-integrated educational systems informed by years of student observations and system improvements.
Qualitative insights into system design to improve human–computer interaction in educational systems.
Implications for practice and/or policy
Actionable design principles for computer-assisted tutoring systems derived from first-hand student feedback and observations.
Encourage new directions for human–computer interaction in educational systems.
期刊介绍：
BJET is a primary source for academics and professionals in the fields of digital educational and training technology throughout the world. The Journal is published by Wiley on behalf of The British Educational Research Association (BERA). It publishes theoretical perspectives, methodological developments and high quality empirical research that demonstrate whether and how applications of instructional/educational technology systems, networks, tools and resources lead to improvements in formal and non-formal education at all levels, from early years through to higher, technical and vocational education, professional development and corporate training.
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