Open Access
AutoTutor and Family: A Review of 17 Years of Natural Language Tutoring.
Benjamin D. Nye,Arthur C. Graesser,Xiangen Hu +2 more
- Vol. 24, pp 427-469
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TLDR
This paper summarizes and integrates significant findings produced by studies using AutoTutor and related systems, and considers advances in pedagogical agent roles and in tutoring technologies, such semantic processing and tutoring delivery platforms.Abstract:
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 of natural language tutoring are presented. Next, we review three central themes in AutoTutor’s development: human-inspired tutoring strategies, pedagogical agents, and technologies that support natural-language tutoring. Research on early versions of AutoTutor documented the impact on deep learning by co-constructed explanations, feedback, conversational scaffolding, and subject matter content. Systems that evolved from AutoTutor added additional components that have been evaluated with respect to learning and motivation. The latter findings include the effectiveness of deep reasoning questions for tutoring multiple domains, of adapting to the affect of low-knowledge learners, of content over surface features such as voices and persona of animated agents, and of alternative tutoring strategies such as collaborative lecturing and vicarious tutoring demonstrations. The paper also considers advances in pedagogical agent roles (such as trialogs) and in tutoring technologies, such semantic processing and tutoring delivery platforms. This paper summarizes and integrates significant findings produced by studies using AutoTutor and related systems.read more
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Evolution and Revolution in Artificial Intelligence in Education
Ido Roll,Ruth Wylie +1 more
TL;DR: It is suggested that two parallel strands of research need to take place in order to impact education in the next 25 years: one is an evolutionary process, focusing on current classroom practices, collaborating with teachers, and diversifying technologies and domains, and the other is a revolutionary process.
Proceedings Article
Affective personalization of a social robot tutor for children's second language skills
Goren Gordon,Samuel Spaulding,Jacqueline M. Kory Westlund,Jin Joo Lee,Luke Plummer,Marayna Martinez,Madhurima Das,Cynthia Breazeal +7 more
TL;DR: This integrated system of tablet-based educational content, Affective sensing, affective policy learning, and an autonomous social robot holds great promise for a more comprehensive approach to personalized tutoring.
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Stupid Tutoring Systems, Intelligent Humans
TL;DR: The potential of educational data mining driving human decision-making as an alternate paradigm for online learning, focusing on intelligence amplification rather than artificial intelligence is discussed.
Journal ArticleDOI
Face-to-Face Interaction with Pedagogical Agents, Twenty Years Later
W. Lewis Johnson,James C. Lester +1 more
TL;DR: This article re-examines the concepts and predictions in the 2000 article in the context of the current state of the field, and outlines a variety of possible uses for pedagogical agents.
Journal ArticleDOI
Conversations with AutoTutor Help Students Learn
TL;DR: This article selectively highlights the status of AutoTutor’s dialogue moves, learning gains, implementation challenges, differences between human and ideal tutors, and some of the systems that evolved from autoTutor.
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