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Jesús Pérez; Eladio Dapena; Jose Aguilar – Education and Information Technologies, 2024
In tutoring systems, a pedagogical policy, which decides the next action for the tutor to take, is important because it determines how well students will learn. An effective pedagogical policy must adapt its actions according to the student's features, such as knowledge, error patterns, and emotions. For adapting difficulty, it is common to…
Descriptors: Feedback (Response), Intelligent Tutoring Systems, Reinforcement, Difficulty Level
Wiedbusch, Megan; Lester, James; Azevedo, Roger – Metacognition and Learning, 2023
Pedagogical agents have been designed to support the significant challenges that learners face when self-regulating in advanced learning environments. Evidence suggests differences in learners' prior skills and abilities, in conjunction with excessive didactic support, can cause overreliance on these external aids, which in turn prevents deeper…
Descriptors: Measurement Techniques, Metacognition, Learning Processes, Nonverbal Communication
Lehman, Blair; D'Mello, Sidney; Strain, Amber; Mills, Caitlin; Gross, Melissa; Dobbins, Allyson; Wallace, Patricia; Millis, Keith; Graesser, Art – International Journal of Artificial Intelligence in Education, 2013
Cognitive disequilibrium and its affiliated affective state of confusion have been found to positively correlate with learning, presumably due to the effortful cognitive activities that accompany their experience. Although confusion naturally occurs in several learning contexts, we hypothesize that it can be induced and scaffolded to increase…
Descriptors: Psychological Patterns, Undergraduate Students, Intelligent Tutoring Systems, Learning
Benjamin D. Nye; Arthur C. Graesser; Xiangen Hu – Grantee Submission, 2014
AutoTutor is a natural language tutoring system that has produced learning gains across multiple domains (e.g., computer literacy, physics, critical thinking). In this paper, we review the development, key research findings, and systems that have evolved from AutoTutor. First, the rationale for developing AutoTutor is outlined and the advantages…
Descriptors: Intelligent Tutoring Systems, Natural Language Processing, Computer Software, Artificial Intelligence
Benjamin D. Nye; Arthur C. Graesser; Xiangen Hu – International Journal of Artificial Intelligence in Education, 2014
AutoTutor is a natural language tutoring system that has produced learning gains across multiple domains (e.g., computer literacy, physics, critical thinking). In this paper, we review the development, key research findings, and systems that have evolved from AutoTutor. First, the rationale for developing AutoTutor is outlined and the advantages…
Descriptors: Intelligent Tutoring Systems, Natural Language Processing, Computer Software, Artificial Intelligence
Lin, Hao-Chiang Koong; Wang, Cheng-Hung; Chao, Ching-Ju; Chien, Ming-Kuan – Turkish Online Journal of Educational Technology - TOJET, 2012
Emotional expression in Artificial Intelligence has gained lots of attention in recent years, people applied its affective computing not only in enhancing and realizing the interaction between computers and human, it also makes computer more humane. In this study, emotional expressions were applied into intelligent tutoring system, where learners'…
Descriptors: Foreign Countries, Intelligent Tutoring Systems, Artificial Intelligence, Focus Groups

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