A Systematic Literature Review of Intelligent Tutoring Systems With Dialogue in Natural Language

Paladines, José; Ramírez, Jaime

doi:10.1109/access.2020.3021383

Cited by 51 publications

(19 citation statements)

References 83 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Following this discrimination, only a few CITS exist at present, due to the complexity and time-consuming nature of development (Cai et al, 2019). Well-known examples of CITS include AutoTutor (Graesser et al, 2004), and Oscar (Latham et al, 2014) (for a more detailed review of CITS see Paladines & Ramírez, 2020). Their impact on learning has been found to be as effective as human tutors (Cai et al, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Enabling adaptive, personalised and context-aware interaction in a smart learning environment: Piloting the iCollab system

Oliveira

Barba²,

Corrin³

2021

AJET

View full text Add to dashboard Cite

Smart learning environments (SLE) provide students with opportunities to interact with learning resources and activities in ways that are customised to their particular learning goals and approaches. A challenge in developing SLEs is providing resources and tasks within a single system that can seamlessly tailor learning experience in terms of time, place, platform, and form. In this paper we introduce the iCollab platform, an adaptive environment where learning activities are moderated through conversation with an intelligent agent who can operate across multiple web-based platforms, integrating formal and informal learning opportunities. Fifty-eight undergraduate computer science students were randomly assigned to either an intervention or control group for the 12 weeks of the pilot study. Learning analytics were used to examine their interactions with iCollab, while their course performance investigated the impact of using iCollab on learning outcomes. Results from the study showed a high level of interaction with iCollab, especially social interaction, indicating an interweaving of formal learning within their informal network spaces. These findings open up new possibilities for ways that SLEs can be designed to incorporate different factors, improving the ability of the system to provide adaptive and personalised learning experiences in relation to context and time.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Enabling adaptive, personalised and context-aware interaction in a smart learning environment: Piloting the iCollab system

Oliveira

Barba²,

Corrin³

2021

AJET

View full text Add to dashboard Cite

show abstract

“…"), only allowing a few follow-up questions, and restricted to simple task domains. However, due to technological advancement and an ever growing market for digital assistants [1], it can be expected that conversational assistants (CA) enter more sophisticated domains and be used for very challenging tasks, like decision-making [2], learning [3], or planning tasks [4]. In order to be accepted and trusted in these delicate domains, conversational interfaces must extend their assistance capabilities and be equipped with more human-like assistant behaviour.…”

Section: Introductionmentioning

confidence: 99%

The Role of Trust in Proactive Conversational Assistants

et al. 2021

View full text Add to dashboard Cite

Humans and machines harmoniously collaborating and benefiting from each other is a long lasting dream for researchers in robotics and artificial intelligence. An important feature of efficient and rewarding cooperation is the ability to assume possible problematic situations and act in advance to prevent negative outcomes. This concept of assistance is known under the term proactivity. In this article, we investigate the development and implementation of proactive dialogues for fostering a trustworthy humancomputer relationship and providing adequate and timely assistance. Here, we make several contributions. A formalisation of proactive dialogue in conversational assistants is provided. The formalisation forms a framework for integrating proactive dialogue in conversational applications. Additionally, we present a study showing the relations between proactive dialogue actions and several aspects of the perceived trustworthiness of a system as well as effects on the user experience. The results of the experiments provide significant contributions to the line of proactive dialogue research. Particularly, we provide insights on the effects of proactive dialogue on the human-computer trust relationship and dependencies between proactive dialogue and user specific and situational characteristics.

show abstract

“…Human-Computer dialogue systems provide a natural language based interface between humans and computers. They have a broad range of applications such as: Intelligent Tutoring systems [1], e-Health Applications [2], Argumentation Detection [3], Natural Language Generation [4], etc. The Human-Computer dialogue system can be composed of different components: The speech signal of the user is converted to a sequence of words by the Automatic Speech Recognition (ASR) system.…”

Section: Introductionmentioning

confidence: 99%

Impact of Using Bidirectional Encoder Representations from Transformers (BERT) Models for Arabic Dialogue Acts Identification

Joukhadar¹,

Ghneim²,

Rebdawi³

2021

ISI

View full text Add to dashboard Cite

In Human-Computer dialogue systems, the correct identification of the intent underlying a speaker's utterance is crucial to the success of a dialogue. Several researches have studied the Dialogue Act Classification (DAC) task to identify Dialogue Acts (DA) for different languages. Recently, the emergence of Bidirectional Encoder Representations from Transformers (BERT) models, enabled establishing state-of-the-art results for a variety of natural language processing tasks in different languages. Very few researches have been done in the Arabic Dialogue acts identification task. The BERT representation model has not been studied yet in Arabic Dialogue acts detection task. In this paper, we propose a model using BERT language representation to identify Arabic Dialogue Acts. We explore the impact of using different BERT models: AraBERT Original (v0.1, v1), AraBERT Base (v0.2, and v2) and AraBERT Large (v0.2, and v2), which are pretrained on different Arabic corpora (different in size, morphological segmentation, language model window, …). The comparison was performed on two available Arabic datasets. Using AraBERTv0.2-base model for dialogue representations outperformed all other pretrained models. Moreover, we compared the performance of AraBERTv0.2-base model to the state-of-the-art approaches applied on the two datasets. The comparison showed that this representation model outperformed the performance both state-of-the-art models.

show abstract

A Systematic Literature Review of Intelligent Tutoring Systems With Dialogue in Natural Language

Cited by 51 publications

References 83 publications

Enabling adaptive, personalised and context-aware interaction in a smart learning environment: Piloting the iCollab system

Enabling adaptive, personalised and context-aware interaction in a smart learning environment: Piloting the iCollab system

The Role of Trust in Proactive Conversational Assistants

Impact of Using Bidirectional Encoder Representations from Transformers (BERT) Models for Arabic Dialogue Acts Identification

Contact Info

Product

Resources

About