DeepTutor: An Effective, Online Intelligent Tutoring System That Promotes Deep Learning

Rus, Vasile; Niraula, Nobal B.; Banjade, Rajendra

doi:10.1609/aaai.v29i1.9269

Cited by 21 publications

(11 citation statements)

References 1 publication

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“…The addition of artificial intelligence in such a semantic system allows the realization of a personal intelligent learning agent, which will aim to optimize the learning of each learner according to the model drawn up of this one and the knowledge to be transmitted through of the various educational resources available. The tutor making the link between the learner and the system, the use of Natural Language Processing (NLP) and various cognitive strategies improve the construction of the learner's knowledge while improving the quality of the student model (Rus, Niraula & al., 2015).…”

Section: Tutor Modelmentioning

confidence: 99%

Intelligent Tutor Using Peripheral Artificial Intelligence: Opportunities and Limits

Aubeuf¹

2022

Education and New Developments 2022 – Volume 2

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"This article aims to present a model of Intelligent Tutoring System exploiting artificial intelligence to personalize the learning of the learner and to automate certain tasks of the teacher. All the resources consulted and the educational objectives achieved by the learner will be processed using the TinCan API and the limitation of the amount of sensitive data sent to the cloud will be ensured by the use of peripheral artificial intelligence. We start by defining the concepts of artificial intelligence and Intelligent Tutoring System, then we focus on the implementation of machine learning in such a system and the advantages that this technique brings. Finally, we describe the limits of such a technology and the possible solutions to it."

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Section: Tutor Modelmentioning

confidence: 99%

Intelligent Tutor Using Peripheral Artificial Intelligence: Opportunities and Limits

Aubeuf¹

2022

Education and New Developments 2022 – Volume 2

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“…Many ITSs provide feedback guiding students towards the correct solution in an exercise (Ramachandran et al 2018;Rus, Niraula, and Banjade 2015;Nye, Graesser, and Hu 2014;Ventura et al 2018;Al-Nakhal and Abu-Naser 2017;Al-Dahdooh and Abu-Naser 2017;Tamura et al 2015;Guo et al 2016;Shah, Shah, and Kurup 2017;Serban et al 2020;Kochmar et al 2020). However, to our knowledge, none have previously employed a neural discourse-based mechanism for personalized feedback.…”

Section: Related Workmentioning

confidence: 99%

“…Our approach contrasts considerably with existing feedback models, such as Rus, Niraula, and Banjade (2015) and Kochmar et al (2020). While these systems can capably guide students towards fixing incorrect ideas, they cannot directly comment on which concepts the students have misunderstood.…”

Section: Introductionmentioning

confidence: 99%

Deep Discourse Analysis for Generating Personalized Feedback in Intelligent Tutor Systems

Grenander

Belfer

Kochmar

et al. 2021

AAAI

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We explore creating automated, personalized feedback in an intelligent tutoring system (ITS). Our goal is to pinpoint correct and incorrect concepts in student answers in order to achieve better student learning gains. Although automatic methods for providing personalized feedback exist, they do not explicitly inform students about which concepts in their answers are correct or incorrect. Our approach involves decomposing students answers using neural discourse segmentation and classification techniques. This decomposition yields a relational graph over all discourse units covered by the reference solutions and student answers. We use this inferred relational graph structure and a neural classifier to match student answers with reference solutions and generate personalized feedback. Although the process is completely automated and data-driven, the personalized feedback generated is highly contextual, domain-aware and effectively targets each student's misconceptions and knowledge gaps. We test our method in a dialogue-based ITS and demonstrate that our approach results in high-quality feedback and significantly improved student learning gains.

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“…As a solution to this, intelligent tutoring systems (ITSs), which aim to scale up individualized education by imitating human expert tutors, are actively explored. Many ITSs have been successfully deployed to improve students' achievements and learning efficiency in a broad range of educational domains such as language learning and scientific reasoning [4], [5], [6].…”

Section: Introductionmentioning

confidence: 99%

DIRECT: Toward Dialogue-Based Reading Comprehension Tutoring

2023

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A major challenge in education is to provide students with a personalized learning experience. This study aims to address this by developing a dialogue-based intelligent tutoring system (ITS) that imitates human expert tutors. The ITS asks questions, assesses student answers, provides hints, and even chats to encourage student engagement. We constructed the Dialogue-based Reading Comprehension Tutoring (DIRECT) dataset to simulate real-world pedagogical scenarios with the assessment labels and key sentences to support tutoring. The DIRECT dataset is based on RACE, which is a large-scale English reading comprehension dataset. In addition, we propose a neural pipeline approach to model the tutoring tasks and conduct a comprehensive analysis on the results, including a human evaluation. The results show that our model performs well in generating questions, assessing answers, and chatting, showing high potential although some challenges remain. The proposed model provides a good basis for further development of dialogue-based ITSs.INDEX TERMS Computer aided instruction, dialogue-based tutoring, educational technology, intelligent tutoring, natural language processing.

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DeepTutor: An Effective, Online Intelligent Tutoring System That Promotes Deep Learning

Cited by 21 publications

References 1 publication

Intelligent Tutor Using Peripheral Artificial Intelligence: Opportunities and Limits

Intelligent Tutor Using Peripheral Artificial Intelligence: Opportunities and Limits

Deep Discourse Analysis for Generating Personalized Feedback in Intelligent Tutor Systems

DIRECT: Toward Dialogue-Based Reading Comprehension Tutoring

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