Towards Conversational Co-Creation of Learning Content in Digital Higher Education

Today formal education like higher education relies on digital learning content like learning videos or quizzes. Using such online learning material enables students to learn independently from time and place. While improvements have been made, there are still many issues as the two-year long crisis in 2020 has revealed. Many offerings do not consider the learners’ needs and can result in unsuccessful learning. One way to address these short comings is to actively include learners in the creation process of learning content. However, co-creation oftentimes relies on face to face and or group settings that may not be possible for all students at all times. Therefore, we undertake a long-term action design research project to investigate the novel concept of conversational co-creation of learning material using a conversational agent and persuasive design to engage and motivate learners. In this article we present an early-stage prototype and concept of conversational co-creation

Utilisation of Open Intent Recognition Models for Customer Support Intent Detection

Businesses have sought out new solutions to provide support and improve customer satisfaction as more products and services have become interconnected digitally. There is an inherent need for businesses to provide or outsource fast, efficient and knowledgeable support to remain competitive. Support solutions are also advancing with technologies, including use of social media, Artificial Intelligence (AI), Machine Learning (ML) and remote device connectivity to better support customers. Customer support operators are trained to utilise these technologies to provide better customer outreach and support for clients in remote areas. Interconnectivity of products and support systems provide businesses with potential international clients to expand their product market and business scale. This paper reports the possible AI applications in customer support, done in collaboration with the Knowledge Transfer Partnership (KTP) program between Birmingham City University and a company that handles customer service systems for businesses outsourcing customer support across a wide variety of business sectors. This study explored several approaches to accurately predict customers' intent using both labelled and unlabelled textual data. While some approaches showed promise in specific datasets, the search for a single, universally applicable approach continues. The development of separate pipelines for intent detection and discovery has led to improved accuracy rates in detecting known intents, while further work is required to improve the accuracy of intent discovery for unknown intents

Virtual Assistant Design for Water Systems Operation

Water management systems such as wastewater treatment plants and water distributions systems are big systems which include a multitude of variables and performance indicators that drive the decision making process for controlling the plant. To help water operators make the right decisions, we provide them with a platform to get quick answers about the different components of the system that they are controlling in natural language. In our research, we explore the architecture for building a virtual assistant in the domain of water systems. Our design focused on developing better semantic inference across the different stages of the process. We developed a named entity recognizer that is able to infer the semantics in the water field by leveraging state-of-the art methods for word embeddings. Our model achieved significant improvements over the baseline Term Frequency - Inverse Document Frequency (TF-IDF) cosine similarity model. Additionally, we explore the design of intent classifiers, which involves more challenges than a traditional classifier due to the small ratio of text length compared to the number of classes. In our design, we incorporate the results of entity recognition, produced from previous layers of the Chatbot pipeline to boost the intent classification performance. Our baseline bidirectional Long Short Term Memory Network (LSTM) model showed significant improvements, amounting to 7-10\% accuracy boost on augmented input data and we contrasted its performance with a modified bidirectional LSTM architecture which embeds information about recognized entities. In each stage of our architecture, we explored state-of-the-art solutions and how we can customize them to our problem domain in order to build a production level application. We additionally leveraged Chatbot frameworks architecture to provide a context aware virtual assistance experience which is able to infer implicit references from the conversation flow

Conversational artificial intelligence in the AEC industry: A review of present status, challenges and opportunities

The idea of developing a system that can converse and understand human languages has been around since the 1200 s. With the advancement in artificial intelligence (AI), Conversational AI came of age in 2010 with the launch of Apple’s Siri. Conversational AI systems leveraged Natural Language Processing (NLP) to understand and converse with humans via speech and text. These systems have been deployed in sectors such as aviation, tourism, and healthcare. However, the application of Conversational AI in the architecture engineering and construction (AEC) industry is lagging, and little is known about the state of research on Conversational AI. Thus, this study presents a systematic review of Conversational AI in the AEC industry to provide insights into the current development and conducted a Focus Group Discussion to highlight challenges and validate areas of opportunities. The findings reveal that Conversational AI applications hold immense benefits for the AEC industry, but it is currently underexplored. The major challenges for the under exploration were highlighted and discusses for intervention. Lastly, opportunities and future research directions of Conversational AI are projected and validated which would improve the productivity and efficiency of the industry. This study presents the status quo of a fast-emerging research area and serves as the first attempt in the AEC field. Its findings would provide insights into the new field which be of benefit to researchers and stakeholders in the AEC industry

Conversational artificial intelligence in the AEC industry: A review of present status, challenges and opportunities

The idea of developing a system that can converse and understand human languages has been around since the 1200 s. With the advancement in artificial intelligence (AI), Conversational AI came of age in 2010 with the launch of Apple’s Siri. Conversational AI systems leveraged Natural Language Processing (NLP) to understand and converse with humans via speech and text. These systems have been deployed in sectors such as aviation, tourism, and healthcare. However, the application of Conversational AI in the architecture engineering and construction (AEC) industry is lagging, and little is known about the state of research on Conversational AI. Thus, this study presents a systematic review of Conversational AI in the AEC industry to provide insights into the current development and conducted a Focus Group Discussion to highlight challenges and validate areas of opportunities. The findings reveal that Conversational AI applications hold immense benefits for the AEC industry, but it is currently underexplored. The major challenges for the under exploration were highlighted and discusses for intervention. Lastly, opportunities and future research directions of Conversational AI are projected and validated which would improve the productivity and efficiency of the industry. This study presents the status quo of a fast-emerging research area and serves as the first attempt in the AEC field. Its findings would provide insights into the new field which be of benefit to researchers and stakeholders in the AEC industry

Implementación de un asistente virtual (CHATBOT) para el blog de la Carrera de Software de la Universidad Técnica del Norte utilizando inteligencia artificial

Implementar un ayudante virtual (Chatbot) para el blog de carrera de software de la Universidad Técnica del Norte utilizando inteligencia artificial.El objetivo del presente proyecto consiste en desarrollar e implementar en el blog de la carrera de software, un chatbot con inteligencia artificial para la comunicación activa entre el usuario e información de las preguntas frecuente de la Coordinación de la Carrera de Software, debido a que el blog de la carrera no cuenta con estos servicios tecnológicos y realiza sus actividades de información al usuario, de una manera tradicional. Para lograr este objetivo se realizó la recolección de datos y la validación de los mismos, utilizando la metodología XP. Se creó el Chatbot empleando la plataforma Dialogflow de Google, las cuales utilizan técnicas de Procesamiento de Lenguaje Natural, Inteligencia Artificial y Machine Learning, que procesan la información recibida por parte del usuario y responden de una manera lógica. Python y el IDE Visual Studio Code son lenguajes de programación utilizados; Además, para validar el proyecto se utilizó el modelo de éxito DeLone & McLean enfocado en el método de investigación mixta (cualitativo-cuantitativo) para la validación del chatbot con inteligencia artificial. Todo esto hizo posible el logro de la entrega del producto, el mismo que permite interactuar con el usuario mediante el Chatbot que se encuentra disponible 24/7, 365 días del año automatizando tiempos. Esta contribución ayudará a reducir la carga laboral en la Coordinación de la Carrera de Software y a que los estudiantes tengan la información actualizada de requisitos, pasos a seguir y formatos para gestionar trámites sin pérdida de tiempo.Ingenierí

Alfa - um chatbot do tipo perguntas e respostas como assistente virtual no AVA Moodle

A fim de apoiar o processo de Educa??o a Dist?ncia (EAD), a cria??o dos Ambientes Virtuais de Aprendizagem (AVA) proporcionou v?rias possibilidades de intera??es entre os envolvidos no processo de ensino aprendizagem online por meio de ferramentas de colabora??o s?ncronas e ass?ncronas. As intera??es nessas ferramentas t?m implica??es no engajamento, envolvimento, satisfa??o e desempenho dos estudantes. Para alcan?ar um grau satisfat?rio, tanto de comunica??o quanto de pertencimento social e emocional pelos estudantes, o professor precisa organizar as suas a??es pedag?gicas de forma a evitar ou minimizar o sentimento de isolamento dos estudantes, promovendo a colabora??o e o suporte necess?rio para que sejam alcan?ados os objetivos de aprendizagem, sendo um dos desafios mais intensos e exaustivos enfrentados na EAD. Considerando o aumento do volume de cursos na modalidade EAD e h?brido, com turmas cada vez maiores, e sem o apoio adequado de tutores, espera-se que algum suporte autom?tico seja oferecido pelas TICs aos professores no acompanhamento das atividades e intera??es com os estudantes nos AVAs. O uso de tecnologias que apliquem t?cnicas de Intelig?ncia Artificial (IA), como os agentes conversacionais do tipo chatbot, al?m de diminuir o esfor?o relacionado a gest?o e acompanhamento por parte dos professores e tutores, podem proporcionar melhorias dos cursos remotos ofertados, oferecendo um novo modo de intera??o e fornecendo m?tricas e indicadores para que os cursos evoluam, tornando-se cada vez melhores para aqueles que comp?em este modelo de ensino. Esta pesquisa teve como objetivo geral investigar e analisar o contexto da EAD, e desenvolver um chatbot educacional do tipo perguntas e respostas, no formato de um plugin do AVA Moodle, para auxiliar professores e tutores como um primeiro apoio ?s d?vidas e busca de informa??es dos estudantes. O processo metodol?gico t?cnico-cient?fico foi composto por seis etapas, a saber: realiza??o de um levantamento bibliogr?fico para oferecer insights do problema de pesquisa, objetivos e trabalhos relacionados; a cria??o de um survey com professores e tutores visando coletar e analisar a percep??o de professores e tutores em rela??o ao acompanhamento das intera??es e atividades nos AVAs e quanto ao uso de Chatbots como apoio ao professor na EAD; o desenvolvimento de uma an?lise de competidores, com a aplica??o da metodologia de benchmark, para selecionar o melhor framework de desenvolvimento com base no contexto de aplica??o; uma an?lise indireta de intera??es no ambiente do IFPB visando obter informa??es para definir o escopo o chatbot quanto a cria??o de a??es e inten??es contextualizadas, al?m de treinar modelos de entendimento de linguagem natural (NLU); especifica??o e codifica??o do chatbot do tipo perguntas e respostas em um processo iterativo e incremental; e por fim, a aplica??o testes e valida??o da acur?cia e da percep??o do usu?rio para com o chatbot desenvolvido em um ambiente Moodle. Como contribui??es prim?rias do trabalho destacam-se: (i) o resultado do benchmark, que comparando quarenta e tr?s frameworks, plataformas e engines para desenvolvimento de chatbots de prop?sito geral ou espec?fico, apontou o framework Rasa NLU como o mais adequado para o desenvolvimento de chatbots no contexto da aprendizagem online em Ambientes Virtuais de Aprendizagem. O resultado do benchmark proporcionou a tomada de decis?o na escolha do framework para o desenvolvimento do chatbot Alfa; (ii) o chatbot Alfa, que considerou a an?lise indireta de intera??es sobre dados de alguns cursos do IFPB para elabora??o do escopo e para o treinamento do modelo, obtendo bons resultados nos testes realizados, obtendo acur?cia no modelo com 0,92 para entidades e 0,70 para inten??es, assim como na valida??o com os usu?rios, aplicando question?rio baseado no modelo Technology Acceptance Model (TAM), com elevado n?vel de aceita??o