Intelligent Tutoring Systems with Conversational Dialogue

By A. Graesser, Kurt VanLehn, Carolyn Penstein Rosé, Pamela W. Jordan, Derek Harter. The AI Magazine, 2001

460 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Developmental and Educational Psychology

View Paper: https://www.semanticscholar.org/paper/bdafe02794ea743d794e2c4c954f4ede9636872d

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Citations in the corpus

AutoTutor and affective autotutor: Learning by talking with cognitively and emotionally intelligent computers that talk back (2012, background): “Some of the successful dialog-based ITSs include AutoTutor [Graesser et al. 2004; VanLehnet al. 2007], Why Atlas [Graesser et al. 2001b; VanLehn et al. 2002], ITSPOKE [Litman and Silliman 2004], CIRCSIM-Tutor [Shah et al. 2002], DC-Trains [Pon-Barry et al. 2004], Mission Rehearsal [Swartout et…”
AutoTutor and Family: A Review of 17 Years of Natural Language Tutoring (2014, unknown): “The WHY2 project was intended as a conceptual successor to the early WHY tutoring architecture for tutoring students on interactions within physical systems (Graesser et al. 2001b; Stevens and Collins 1977).”
Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features (2010, background): “…the reasons behind student errors, and mastery learning through assessments of the probability that the student knows each skill relevant to the system (Anderson et al. 1995, 2005; Gertner and VanLehn 2000; Graesser et al. 2005, 2001; Koedinger et al. 1997; VanLehn 1990; VanLehn et al. 2005).”
Scaffolding Deep Comprehension Strategies Through Point&Query, AutoTutor, and iSTART (2005, background): “AutoTutor is a computer tutor that attempts to simulate the dialogue moves of a human tutor (Graesser, Lu, et al., 2004; Graesser, Person & Harter, 2001; Graesser, VanLehn, Rose, Jordan, & Harter, 2001).”
The Relative Effectiveness of Human Tutoring, Intelligent Tutoring Systems, and Other Tutoring Systems (2011, background): “A common belief among computer tutoring researchers is that human tutoring has an effect size of d = 2.0 relative to classroom teaching without tutoring (Bloom, 1984; Corbett, 2001; Evens & Michael, 2006; Graesser et al., 2001; VanLehn et al., 2007; Woolf, 2009).”
A Social-Cognitive Framework for Pedagogical Agents as Learning Companions (2006, background): “Traditionally, computer-based learning environments (e.g., intelligent tutoring systems) have been tailored to meet a student’s individual needs, supporting each learner independently when the environments were well designed (Aimeur & Frasson, 1996; Anderson, Corbett, Koedinger, & Pelletier, 1995; G”
AutoTutor: A tutor with dialogue in natural language (2004, methodology): “These considerations motivated the design of AutoTutor (Graesser, Person, Harter, & the Tutoring Research Group (TRG), 2001; Graesser, VanLehn, et al., 2001; Graesser, K. Wiemer-Hastings, P. Wiemer-Hastings, Kreuz, & the TRG, 1999), which will be described in the next section.”
Are We There Yet? - A Systematic Literature Review on Chatbots in Education (2021, background): “Intelligent Tutoring Systems are defined as computerized learning environments that incorporate computational models (Graesser et al., 2001) and provide feedback based on learning progress.”
Automatic detection of learner’s affect from conversational cues (2008, background): “The Tutoring Research Group (TRG) at the University of Memphis developed AutoTutor, a fully automated computer tutor that simulates human tutors and holds conversations with students in natural language (Graesser et al.; 2001; 2005a; 2005b).”
Predicting Student Emotions in Computer-Human Tutoring Dialogues (2004, background): “…tutoring dialogue systems have become more prevalent in recent years (Aleven and Rose, 2003), as one method of improving the performance gap between computer and human tutors; recent experiments with such systems (e.g., (Graesser et al., 2002)) are starting to yield promising empirical results.”