The Relative Effectiveness of Human Tutoring, Intelligent Tutoring Systems, and Other Tutoring Systems

By Kurt VanLehn. 2011

2,067 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3b924661db089b3511465ae48e6400e20a1dd232

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In Artificial Intelligence

Citations in the corpus

The Robot Who Tried Too Hard: Social Behaviour of a Robot Tutor Can Negatively Affect Child Learning (2015, background): “One-to-one tutoring has been shown to result in significantly higher knowledge gains than group education [5, 23].”
Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions (2023, background): “On the one hand, for students, AIED has focused on developing models [111] and adaptive systems that can identify learners’ strengths and weaknesses across a variety of topics, leading to customized instructions and resources that align with their learning needs [112].”
The ASSISTments Ecosystem: Building a Platform that Brings Scientists and Teachers Together for Minimally Invasive Research on Human Learning and Teaching (2014, background): “Van Lehn’s (VanLehn et al. 2005) Lesson Learned paper points out that all systems involve thousands of little design decisions; not all of them can be shared. The two lessons most worth sharing with others in the field, that are not in any of our prior publications, are 1) to “build a platform” that”
Artificial intelligence for education: Knowledge and its assessment in AI-enabled learning ecologies (2020, methodology): “Among the implementations of AI-enabled embedded assessment are: intelligent tutoring systems (Carbonell, 1970; Mark & Greer, 1995; VanLehn, 2011); log file and clickstream analyses that predict learner success (Crossley, Paquette, Dascalu, McNamara, & Baker, 2016); games and simulations that…”
Effectiveness of Intelligent Tutoring Systems (2016, background): “The first-generation computer tutors have been given the retronym CAI tutors (for computer-assisted instruction tutors ); the second-generation tutors are usually called intelligent tutoring systems , or ITSs (VanLehn, 2011).”
Vision, challenges, roles and research issues of Artificial Intelligence in Education (2020, background): “Several meta-analytic studies have demonstrated the effectiveness of intelligent tutoring systems to promote learning outcomes (Ma, Adesope, Nesbit, & Liu, 2014; Steenbergen-Hu & Cooper, 2014; VanLehn, 2011).”
Impact of AI assistance on student agency (2023, background): “…Bogdanova, Serral, Snoeck, & De Weerdt, 2021), personalised nudging systems remind students of upcoming deadlines (Damgaard & Nielsen, 2018) and adaptive educational systems (Ma, Adesope, Nesbit, & Liu, 2014; VanLehn, 2011) tailor instructions and curriculum to the needs of individual students.”
Learning is Not a Spectator Sport: Doing is Better than Watching for Learning from a MOOC (2015, background): “When well designed, they yield significant learning gains [e.g., 24, 27, 35].”
Social robots for education: A review (2018, background): “Previous work (59) has suggested that human tutors produce a mean cognitive outcome effect size (Cohen’s d) of 0.”
Empowering educators to be AI-ready (2022, background): “…as less effective than human one-to-one tutoring (dependent upon the expertise of the human tutor), but ITSs can be more effective than traditional classroom instruction, reading printed or digital texts or doing home-work alone (Van Lehn, 2011; Steenbergen-Hu & Cooper, 2014; du Boulay 2016).”
Pedagogical Agents: Back to the Future (2018, positive): “We predict that future agents will combine affective computing technologies with rela-tionship-building behaviors, resulting in empathetic pedagogical agents that develop and maintain relationships with learners (Walker and Ogan 2016).”
Affective Personalization of a Social Robot Tutor for Children's Second Language Skills (2016, background): “The subfield of “affect-aware tutors” Woolf et al. (2009) seeks to design more effective ITSs that explicitly sense, model, and reason about students’ affective states.”
The impact of artificial intelligence on learner–instructor interaction in online learning (2021, background): “…artificial intelligence (AI) in online learning and teaching are broad (Anderson et al., 1985; Baker, 2016; Roll et al., 2018; Seo et al., 2020b; VanLehn, 2011), ranging from personalized learning for students and automation of instructors’ routine tasks to AI-powered assessments (Popenici &…”
Synthesizing Results From Empirical Research on Computer-Based Scaffolding in STEM Education (2016, methodology): “…types, such as dynamic assessment (Swanson & Lussier, 2001), scaffolding in intelligent tutoring systems (Ma, Adesope, Nesbit, & Liu, 2014; VanLehn, 2011), scaffolding for students with learning disabilities (Swanson & Deshler, 2003), and a pilot meta-analysis on a wider swath of…”
A meta-analysis of the effectiveness of intelligent tutoring systems on college students' academic learning (2014, positive): “VanLehn (2011) reviewed randomized experiments that compared the effectiveness of human tutoring, computer tutoring, and no tutoring.”