Building Pedagogical Conversational Agents, Affectively Correct

Feidakis, Michalis; Kasnesis, Panagiotis; Giatraki, Eva; Giannousis, Christos; Patrikakis, Charalampos Z.; Monachelis, Panagiotis

doi:10.5220/0007771001000107

Cited by 6 publications

(4 citation statements)

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“…A learner review is chunked into multiple sentences and extracted the topic and sentiment for each sentence. To deliver pedagogical cognitive and affective skills, conversational agents based on reinforcement learning (RL) and sentiment analysis were proposed by Feidakis [74]. In that study, the learning task is expressed in a goal-oriented way to RL agents.…”

Section: Learning and Teaching Systems Evaluationmentioning

confidence: 99%

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Sentiment analysis and opinion mining on educational data: A survey

Shaik,

Tao,

Dann

et al. 2023

Preprint

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Sentiment analysis AKA opinion mining is one of the most widely used NLP applications to identify human intentions from their reviews. In the education sector, opinion mining is used to listen to student opinions and enhance their learning-teaching practices pedagogically. With advancements in sentiment annotation techniques and AI methodologies, student comments can be labelled with their sentiment orientation without much human intervention. In this review article, 1) we consider the role of emotional analysis in education from four levels: document level, sentence level, entity level, and aspect level, 2)sentiment annotation techniques including lexicon-based and corpus-based approaches for unsupervised annotations are explored, 3)the role of AI in sentiment analysis with methodologies like machine learning,

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Section: Learning and Teaching Systems Evaluationmentioning

confidence: 99%

“…Li et al [97] proposed a novel adaptive cross-contextual word embedding cess can be enhanced to provide improved support for goal-oriented learning tasks discussed in [74].…”

Section: Polysemous Wordsmentioning

confidence: 99%

Sentiment analysis and opinion mining on educational data: A survey

Shaik,

Tao,

Dann

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…[82] showcased conversational agents in education to help students in re-learning in administration issues. [45] [83] implemented pedagogical-driven conversational agents with sentiment analysis using reinforcement learning. In education, mostly conversational agents were based on retrieval or pattern-based.…”

Section: ) Conversational Agents In Education -mentioning

confidence: 99%

AI-Based Conversational Agents: A Scoping Review From Technologies to Future Directions

et al. 2022

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Artificial intelligence is changing the world, especially the interaction between machines and humans. Learning and interpreting natural languages and responding have paved the way for many technologies and applications. The amalgam of machine learning, deep learning, and natural language processing helped Conversational Artificial Intelligence (AI) to change the face of Human-Computer Interaction (HCI). A conversational agent is an excellent example of conversational AI, which imitates the natural language. This article presents a sweeping overview of conversational agents that includes different techniques such as pattern-based, machine learning, and deep learning used to implement conversational agents. It also discusses the panorama of different tasks in conversational agents. This study also focuses on how conversational agents can simulate human behavior by adding emotions, sentiments, and affect to the context. With the advancements in recent trends and the rise in deep learning models, the authors review the deep learning techniques and various publicly available datasets used in conversational agents. This article unearths the research gaps in conversational agents and gives insights into future directions.

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“…The use of online platforms allows the implementation of pedagogical conversational agents, which have been of increasing interest for the scientific community over the last decade [27]. They have been applied in various disciplines such as physics, mathematics, languages and chemistry, usually inspired by gamification-related pedagogical strategies [15]. In general, chat-based processes are well accepted by users.…”

Section: Introductionmentioning

confidence: 99%

The Virtual Tutor: Tasks for conversational agents in Online Collaborative Learning Environments

Gabriel¹,

Hahne²,

Zimmermann³

et al. 2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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Online collaborative learning environments are becoming increasingly popular in higher education. Etutors need to supervise, guide students and look out for conflicts within the online environment to ensure a successful learning experience. Web-based platforms allow for interactive elements such as conversational agents to disencumber the e-tutor. Repeatable tasks, which do not require a human response, can be automatized by these systems. The aim of this study is to identify and synthesize the tasks an e-tutor has and to investigate the automatisation potential with conversational agents. Using a design science research approach a literature review is conducted, identifying 13 tasks. Subsequently, a matrix is established, contrasting the tasks with requirements for the use of conversational agents. Furthermore, a virtual tutor framework is developed, clarifying the agent type selection, the technical structure and components for a prototype development in an online collaborative learning environment.

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Building Pedagogical Conversational Agents, Affectively Correct

Cited by 6 publications

References 0 publications

Sentiment analysis and opinion mining on educational data: A survey

Sentiment analysis and opinion mining on educational data: A survey

AI-Based Conversational Agents: A Scoping Review From Technologies to Future Directions

The Virtual Tutor: Tasks for conversational agents in Online Collaborative Learning Environments

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