AutoTutor: an intelligent tutoring system with mixed-initiative dialogue

By A. Graesser, P. Chipman, Brian C. Haynes, Andrew McGregor Olney. IEEE Transactions on Education, 2005

740 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0342f49e1c549a783e973aed0ac060d0807643c6

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In Artificial Intelligence

Citations in the corpus

Five Major Shifts in 100 Years of Engineering Education (2012, methodology): “development time has been reduced since developers can use authoring tools such as Authorware [195] or platforms such as AutoTutor [192].”
Rediscovering the use of chatbots in education: A systematic literature review (2020, methodology): “Autotutor [22] is a multidisciplinary chatbot aimed at encouraging learning through conversation with a learner.”
Automatic detection of learner’s affect from conversational cues (2008, background): “…makes use of facial feature tracking, speech analyses, and posture patterns for affect detection (D'Mello, Craig, Gholson, Franklin, Picard, & Graesser, 2005), this paper focuses on detecting affect from discourse features obtained from AutoTutor's natural language mixed-initiative dialogue.”
Challenges and Future Directions of Big Data and Artificial Intelligence in Education (2020, background): “Research on AI in education over the past few decades has been dedicated to advancing intelligent computing technologies such as intelligent tutoring systems (Graesser et al., 2005; Gobert et al., 2013; Nye, 2015), robotic systems (Toh et al.”
The role of emotion in self-explanations by cognitive agents (2017, background): “In tutor and pedagogical systems, natural dialog between the user and system has been shown to increase the training effect of such systems [12].”
Grounded Adaptation for Zero-shot Executable Semantic Parsing (2020, background): “…parsers (Zelle and Mooney, 1996; Zettle-moyer and Collins, 2005; Liang et al., 2011) build executable meaning representations for a range of tasks such as question-answering (Yih et al., 2014), robotic control (Matuszek et al., 2013), and intelligent tutoring systems (Graesser et al., 2005).”
Explainable Artificial Intelligence: a Systematic Review (2020, background): “AutoTutor [127], designed to be integrated into tutoring systems, is grounded on learning theo-”
Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features (2010, background): “2.1 AutoTutor AutoTutor is a fully automated tutor for Newtonian physics, computer literacy, and critical thinking (Graesser et al. 2005, 2004).”
AutoTutor and affective autotutor: Learning by talking with cognitively and emotionally intelligent computers that talk back (2012, methodology): “computational linguistics, discourse processing, affective computing, machine learning, embodied conversational agents, cognitive science, and the learning sciences [Graesser et al. 2005].”
AutoTutor and Family: A Review of 17 Years of Natural Language Tutoring (2014, methodology): “For a full physics problem similar to Table 1 that has many expectations, the dialog can last up to 30 to 100 turns before all expectations are fully covered (Graesser et al. 2005b).”
A New Paradigm for Intelligent Tutoring Systems: Example-Tracing Tutors (2009, positive): “…to impressive improvement in student learning in a range of domains, using a variety of different approaches (Beal, Walles, Arroyo, & Woolf, 2007; Graesser, Chipman, Haynes, & Olney, 2005; Koedinger, Anderson, Hadley, & Mark, 1997; Martin & Mitrovic, 2002; Mostow & Beck, 2007; Rickel & Johnson,…”