A Review of AI-Driven Conversational Chatbots Implementation Methodologies and Challenges (1999-2022)

Article Computer Science, Interdisciplinary Applications

An analysis of children? interaction with an AI chatbot and its impact on their interest in reading

Chen -Chung Liu et al.

Summary: This study explores the role of interaction in students' engagement and interest in reading by using a chatbot built with artificial intelligence techniques as a book talk companion. The findings suggest that students who interacted with the chatbot maintained a stable level of situational interest, which was closely related to their engagement in the reading activity and perceived social connection with the chatbot.

COMPUTERS & EDUCATION (2022)

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Article Acoustics

A method for improving bot effectiveness by recognising implicit customer intent in contact centre conversations

Lukasz Pawlik et al.

Summary: Contact centre systems are utilizing intelligent voicebots and chatbots to enhance customer service. This paper proposes a new method for recognizing customer intent, taking into account the impact of emotions on conversations. Analysis of actual conversations shows that the method can improve the accuracy of actions taken by bots.

SPEECH COMMUNICATION (2022)

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Article Computer Science, Interdisciplinary Applications

Developing an AI-based chatbot for practicing responsive teaching in mathematics

Dabae Lee et al.

Summary: This study aims to develop an AI-based chatbot to enhance preservice teachers' responsive teaching skills, particularly questioning skills, through authentic and meaningful teaching situations. The findings demonstrate that the chatbot effectively improves preservice teachers' questioning patterns and user experiences, with certain design features playing a positive role.

COMPUTERS & EDUCATION (2022)

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Proceedings Paper Computer Science, Information Systems

Question Answering Model Based Conversational Chatbot using BERT Model and Google Dialogflow

Nikita Kanodia et al.

Summary: Contemporary conversational chatbots are user-friendly and can simulate human conversations, but they lack the ability to evaluate large datasets for answering user questions, while state-of-the-art Question Answering Models can provide accurate responses. The research developed a QAM using BERT model and Google Dialogflow to enhance user experience and accuracy in answering questions. The integration of BERT model and Google Dialogflow allows for accurate responses based on comprehensive datasets.

2021 31ST INTERNATIONAL TELECOMMUNICATION NETWORKS AND APPLICATIONS CONFERENCE (ITNAC) (2021)

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Proceedings Paper Automation & Control Systems

Question Answering Chatbot for Troubleshooting Queries based on Transfer Learning

Zeeshan Haque Syed et al.

Summary: This paper presents an experimentation study comparing different Retriever-Reader combinations in the context of troubleshooting documents, aiming to find the best combination of components for speed and processing power.

KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS (KSE 2021) (2021)

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Article Computer Science, Artificial Intelligence