DEVELOPMENT OF A CLOUD BASED STUDENT INFORMATION CHATBOT SYSTEM

The development of chatbot system is an algorithm that analyzes the student queries and reply messages. In this system, artificial intelligence is built to answer the query of the student. The specific objectives are to determine the required features for the construction of knowledge base, design and implement the model, evaluate the performance of the developed system. Samples of Frequently Asked Questions (FAQ) was collected from the department of Student Affairs, Admission Office and Information Management and Technology Center (IMTC) of the university. The collected sample was analyzed based on the category of question and the model was designed using Unified Modeling Language (UML). The model was implemented with python programming language, HTML, CSS, JavaScript for the client sever side, and also Artificial Intelligence Markup Language (AIML) () and MySQL for the back end. The developed system performance was evaluated using Alpha Beta testing. The proposed system was successfully tested to denote its effectiveness and achievability. It totally eliminates the manual process of retrieving information about a particular domain and reduces manpower, time, for any individual. The developed system will provide adequate assistance to the student on FAQ, thereby reducing the time in visiting the college to enquire about the information in respect of school activities. It will also provide an enabling environment for the students to keep them updated about the school activities

Design pattern for conversational agents handling data-driven requests

The aim of this research project is to identify design principles for the development of CAs. In the context of this thesis, the research questions are: “According to which design principles are Conversational Agents developed?” and “How can these design principles be meaningfully categorized and described?”. For the aggregation of the design principles, the first step was a systematic literature search according to Vom Brocke et al. (2009). The systematic literature review was followed by a qualitative literature analysis according to Kuckartz (2018). The result of this work is the identification of 15 meta-requirements that could be categorised by means of three main categories and a further seven subcategories. This was followed by the declaration of seven design principles based on the subcategories and their meta-requirements

Conversational Agents, Conversational Relevance, and Disclosure: Comparing the Effectiveness of Chatbots and SVITs in Eliciting Sensitive Information

Conversational agents (CAs) in various forms are used in a variety of information systems. An abundance of prior research has focused on evaluating the various traits that make CAs effective. Most studies assume, however, that increasing the anthropomorphism of an agent will improve its performance. In a sensitive information disclosure task, that may not always be the case. We leverage self disclosure, social desirability, and social presence theories to predict how differing modes of conversational agents affect information disclosure. In this paper, we propose a laboratory experiment to compare how the mode of a given CA text based chatbot or voice based smart speaker paired with either high or low levels of conversational relevance, affects the disclosure of personally sensitive information. In addition to understanding influences on disclosure, we aim to break down the mechanisms through which CA design influences disclosure

Intent classification for a management conversational assistant

Intent classification is an essential step in processing user input to a conversational assistant. This work investigates techniques of intent classification of chat messages used for communication among software development teams with the aim of building an intent classifier for a management conversational assistant integrated into modern communication platforms used by developers. Experiments conducted using rule-based and common ML techniques have shown that careful choice of classification features has a significant impact on performance, and the best performing model was able to obtain a classification accuracy of 72%. A set of techniques for extracting useful features for text classification in the software engineering domain was also implemented and tested

Factors Influencing Artificial Intelligence Conversational Agents Usage in the E-commerce Field: A Systematic Literature Review

Artificial intelligence conversational agents have become an important strategy for business, both as an online shopping application and as a customer support solution, where they provide interactive communication for online customers. To ensure the effective usage and successful implementation of the conversational agents, the factors influencing customers\u27 attitudes and acceptance towards conversational agents need to be explored. This paper presents a systematic literature review of conversational agents in the field of e-commerce to identify the variables that influence conversational agents\u27 usage and to present the state-of-the-art in this research area. Twenty-four relevant papers are reviewed, and many significant factors are identified that positively influence customers\u27 acceptance, satisfaction, and trust towards conversational agents’ technology

A Proposed Artificial Intelligence-Based System for Developing E-management Skills in Saudi Primary Schools

This study aims to investigate the impact of Artificial intelligence-driven solutions on school leaders’ proficiencies. Leaders have the responsibility of making decisions in educational institutions as well as carrying out routine tasks daily. Artificial intelligence-assisted applications have noteworthy contributions to the field of educational management. The scope of this study is limited to selected features; data analytics, chatbot, and e-survey. The basic design of this study started with analyzing literature in this domain. This was followed by designing a system consisting of four models: building a dashboard, predicting students’ results, creating a chatbot for responding to parents’ queries, and creating an e-survey for measuring staff satisfaction. The prominent finding of this study is the significant impact of Artificial intelligence on leaders’ competencies

A Proposed Artificial Intelligence-Based System for Developing E-management Skills in Saudi Primary Schools

This study aims to investigate the impact of Artificial intelligence-driven solutions on school leaders’ proficiencies. Leaders have the responsibility of making decisions in educational institutions as well as carrying out routine tasks daily. Artificial intelligence-assisted applications have noteworthy contributions to the field of educational management. The scope of this study is limited to selected features; data analytics, chatbot, and e-survey. The basic design of this study started with analyzing literature in this domain. This was followed by designing a system consisting of four models: building a dashboard, predicting students’ results, creating a chatbot for responding to parents’ queries, and creating an e-survey for measuring staff satisfaction. The prominent finding of this study is the significant impact of Artificial intelligence on leaders’ competencies