{"title":"The Effects of Cognitive Engagement and Humanness of Chatbots on Learning Outcomes and Motivation","authors":"Jiwon Lai Kim;Gahgene Gweon;Muhsin Menekse","doi":"10.1109/TLT.2025.3577950","DOIUrl":null,"url":null,"abstract":"According to the interactive–constructive–active–passive (ICAP) framework, engaging in <italic>constructive</i> cognitive modes yields better learning outcomes than <italic>active</i> modes. Also, prior studies on educational chatbots suggest that enhancing chatbot humanness can improve learning. However, these two ideas have not been fully explored together, especially within the context of text-based, disembodied chatbots. This study investigates the impact of the cognitive engagement modes (<italic>constructive</i> versus <italic>active</i>) and chatbot humanness (humanized versus nonhumanized) on learning outcomes and five dimensions of learning motivation. We conducted a two-by-two factorial user experiment with 55 chatbot users. Data were analyzed through a mixed-method approach to examine the main and interaction effects of the two independent variables. Regarding learning outcomes, our data showed that learners who interacted with <italic>constructive</i> chatbots showed higher learning outcomes than those who interacted with <italic>active</i> chatbots. In addition, learners who interacted with nonhumanized chatbots reported higher learning outcomes than those who interacted with humanized chatbots. Lastly, we observed a significant interaction effect between the two independent variables on tension-pressure and perceived competence, which are two dimensions of learning motivation. Our study extended the applicability of the ICAP framework to the domain of chatbot-based learning, challenged the assumption that the humanness of chatbots can lead to improved learning outcomes, and underscored the importance of exploring both the cognitive engagement modes and the humanness of chatbots when designing chatbots to enhance users’ learning motivation.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"652-665"},"PeriodicalIF":2.9000,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Learning Technologies","FirstCategoryId":"95","ListUrlMain":"https://ieeexplore.ieee.org/document/11027796/","RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
According to the interactive–constructive–active–passive (ICAP) framework, engaging in constructive cognitive modes yields better learning outcomes than active modes. Also, prior studies on educational chatbots suggest that enhancing chatbot humanness can improve learning. However, these two ideas have not been fully explored together, especially within the context of text-based, disembodied chatbots. This study investigates the impact of the cognitive engagement modes (constructive versus active) and chatbot humanness (humanized versus nonhumanized) on learning outcomes and five dimensions of learning motivation. We conducted a two-by-two factorial user experiment with 55 chatbot users. Data were analyzed through a mixed-method approach to examine the main and interaction effects of the two independent variables. Regarding learning outcomes, our data showed that learners who interacted with constructive chatbots showed higher learning outcomes than those who interacted with active chatbots. In addition, learners who interacted with nonhumanized chatbots reported higher learning outcomes than those who interacted with humanized chatbots. Lastly, we observed a significant interaction effect between the two independent variables on tension-pressure and perceived competence, which are two dimensions of learning motivation. Our study extended the applicability of the ICAP framework to the domain of chatbot-based learning, challenged the assumption that the humanness of chatbots can lead to improved learning outcomes, and underscored the importance of exploring both the cognitive engagement modes and the humanness of chatbots when designing chatbots to enhance users’ learning motivation.
期刊介绍：
The IEEE Transactions on Learning Technologies covers all advances in learning technologies and their applications, including but not limited to the following topics: innovative online learning systems; intelligent tutors; educational games; simulation systems for education and training; collaborative learning tools; learning with mobile devices; wearable devices and interfaces for learning; personalized and adaptive learning systems; tools for formative and summative assessment; tools for learning analytics and educational data mining; ontologies for learning systems; standards and web services that support learning; authoring tools for learning materials; computer support for peer tutoring; learning via computer-mediated inquiry, field, and lab work; social learning techniques; social networks and infrastructures for learning and knowledge sharing; and creation and management of learning objects.
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