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This article describes designs that use multiple conversational agents within the framework of intelligent tutoring systems. Agents in this case are computerized talking heads or embodied animated avatars that help students learn by performing actions and holding conversations with them in natural language. The earliest conversational intelligent tutoring systems were limited to a single agent that interacted with a student in the role of a teacher or expert. Technological advances have since made possible systems in which multiple agents interact with the learner and each other to model ideal behavior, strategies, reflections, and social interactions. Though still an emerging technology, multi-agent intelligent tutoring systems afford pedagogical benefits that go beyond the capabilities of the single-agent system and have facilitated learning gains on a variety of subject matters and skills, including science, technology, engineering, mathematics, research methods, metacognition, and language comprehension. The present work describes some common multi-agent designs that may be used to achieve a variety of pedagogical goals. We provide examples of how these designs have been implemented in educational or experimental settings and anticipate future use within the field of artificial intelligence. [This article was published in "Technology, Knowledge and Learning" (EJ1262514).]

Intelligent learning technologies are often applied within the educational industries. While these technologies can be used to create learning experiences tailored to an individual student, they cannot address students' affect accurately and quickly during the learning process. This paper focuses on two core research questions. How do students regulate affect and what are the processes that affect regulation? First, this paper reviews the affect regulation methods and processes in an intelligent learning environment based on affective transition and affect compensation. This process, along with affect analysis, affect regulation, intelligent agents, and an intervention strategy can be used to analyze specific affect regulation methods and improve the affective regulation system. Seventy-two 7th grade students were randomly placed into an experimental condition that used Betty's Brain, an intelligent tutoring system (ITS), or a classroom control. A lag sequence analysis and a multinomial processing tree analysis of video data captured at 25-minute intervals revealed significant differences in affect transitions frequencies between the two groups. Based on the results of the above analyses and after-class interviews, we found that Betty's Brain was able to promote effective affect-regulation strategies to students in the domain of forest ecosystems.

With the rapid development of information technology, e-books have become convenient for students to improve their learning performance, especially when learning complicated concepts. However, research showed that acceptance of e-books by teachers is fragmented, due to several factors including the e-book design. Therefore, this study combined the potential positive impacts of openness and interaction on learning to design an open and interactive e-book for teaching K-12 students AI. It then applied a mixed method to investigate the factors that affect teachers' acceptance of this open and interactive e-book based on the technology acceptance model (TAM) and interviews. The obtained results showed that teachers' intention to continue using this e-book is significantly influenced by their perceived usefulness and attitude towards this e-book. Additionally, both the interactive and openness features were very helpful for teachers in using this e-book in their teaching plans. However, some of them raised several concerns like the interactive coding platform should be personalized based on students' age. The findings of this study could help different stakeholders (e.g., instructional designers, teachers, policymakers) in facilitating the design and adoption of open and interactive e-books.

The challenge of predict ing meme success has gained at tentionfrom researchers, largely due to the increased availability ofsocial media data. Many models focus on st ructural features ofonline social networks as predictors of meme success. Thecurrent work takes a different approach, predict ing memesuccess from linguist ic features. We propose predict ive poweris gained by grounding memes in theories of working memory,emot ion, memory, and psycholinguist ics. The linguist iccontent of several memes were analyzed with linguist icanalysis tools. These features were then t rained with amult ilayer supervised backpropagat ion network. A set of newmemes was used to test the generalizat ion of the network.Results indicated the network was able to generalize thelinguist ic features in order to predict success at greater thanchance levels (80% accuracy). Linguist ic features appear to beenough to predict meme t ransmission success without anyinformat ion about social network st ructure.

Intelligent learning technologies are often applied within the educational industries. While these technologies can be used to create learning experiences tailored to an individual student, they cannot address students' affect accurately and quickly during the learning process. This paper focuses on two core research questions. How do students regulate affect and what are the processes that affect regulation? First, this paper reviews the affect regulation methods and processes in an intelligent learning environment based on affective transition and affect compensation. This process, along with affect analysis, affect regulation, intelligent agents, and an intervention strategy can be used to analyze specific affect regulation methods and improve the affective regulation system. Seventy-two 7th grade students were randomly placed into an experimental condition that used Betty's Brain, an intelligent tutoring system (ITS), or a classroom control. A lag sequence analysis and a multinomial processing tree analysis of video data captured at 25-minute intervals revealed significant differences in affect transitions frequencies between the two groups. Based on the results of the above analyses and after-class interviews, we found that Betty's Brain was able to promote effective affect-regulation strategies to students in the domain of forest ecosystems. [ABSTRACT FROM AUTHOR]