12 research outputs found

    The Development of Chatbot Provided Registration Information Services for Students in Distance Learning

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    In recent years, chatbots have become crucial, particularly for assisting students with real-time registration information. This research focused on 1) synthesizing registry works related to information provided for students, 2) designing chatbots and conversation structures in the form of interactive conversations between students and robots for answering questions and providing information tailored to their needs, and 3) examining and evaluating the use of chatbots in providing information services to students, while analyzing the accuracy and suitability of the developed chatbot. This study, based on research and development, utilized a sample consisting of 16 staff directly involved in the provision of registration information to students and 255 undergraduate students from Sukhothai Thammathirat Open University, with respondents being selected through a simple random sampling technique. The synthesis of the research results revealed the following findings: 1) A qualitative study revealed that the registration information related to students, called STOU Journey, consisted of 10 issues, and was required for the whole learning period. 2) The result of the design and development of the chatbot revealed that the developer chatbot could be used on both the website and the LINE application. It was also found that the chatbot could answer most questions correctly and completely. The chatbot responded quickly and was easy to use. The chatbot used language that was easy to understand and natural, while 3) satisfactory evaluation results from 255 undergraduate students showed that overall, students who had used the completed version of the chatbot were satisfied with the use of the chatbot at a high level (Mean = 4.19, SD = 0.98) while they also felt that the chatbot was easy to use (Mean = 4.33, SD = 0.95) and the using the chatbot felt like a natural conversation (Mean = 4.22, SD = 0.99)

    Geographic information extraction from texts

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    A large volume of unstructured texts, containing valuable geographic information, is available online. This information – provided implicitly or explicitly – is useful not only for scientific studies (e.g., spatial humanities) but also for many practical applications (e.g., geographic information retrieval). Although large progress has been achieved in geographic information extraction from texts, there are still unsolved challenges and issues, ranging from methods, systems, and data, to applications and privacy. Therefore, this workshop will provide a timely opportunity to discuss the recent advances, new ideas, and concepts but also identify research gaps in geographic information extraction

    Anthropomorphized chatbots in mental health applications

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    The number of people suffering from mental health disorders is steadily rising as a result of growing social and economic inequality, ongoing political conflict, and, not least, the COVID 19 pandemic. The rapid progress of artificial intelligence, and within it chatbots, presents an opportunity to address these deficiencies by reducing treatment barriers and providing economic benefits to service providers and consumers. To assure the effectiveness of chatbots in psychological health applications, they have to be accepted by users. A chatbot’s acceptance in mental health interventions is influenced by the benefits of intelligent machines, their expectation of nonjudgmental and unbiased support, and the effect of stigma on trust and belief in healthcare. Based on these insights, the experimental study examines whether users of psychological health apps more readily accept chatbots as opposed to physical health apps. Furthermore, the humanization of chatbots is a proven tool to enhance the quality of interaction with users. Thus, this dissertation additionally aims to investigate if a humanized chatbot entity affects their acceptance in the context of mental health apps. The results suggest that chatbots are more widely accepted in mental health applications compared to physical health applications. Moreover, the findings lead to the recommendation to implement humanized entities in chatbots within mental health applications. The results provide a rationale for conducting additional research to investigate the subject in greater depth. Due to the continuous development of AI, the utilization of chatbots in mental health care should be investigated continuously.O número de pessoas que sofrem de perturbações de saúde mental está a aumentar constantemente devido à desigualdade social e económica, conflitos políticos e da pandemia de COVID-19. O rápido progresso da inteligência artificial representa uma oportunidade para resolver estas perturbações, reduzindo os obstáculos ao tratamento e proporcionando benefícios económicos aos prestadores de serviços e aos pacientes. Para garantir a eficácia dos chatbots nas aplicações de saúde mental, estes têm de ser aceites pelos utilizadores. Esta aceitação nas intervenções de saúde mental é influenciada pelos benefícios das máquinas inteligentes, pela sua expectativa de apoio imparcial e sem juízos de valor e pelo efeito do estigma na confiança e na crença nos cuidados de saúde. Com base nestes conhecimentos, o estudo experimental examina se os chatbots são mais facilmente aceites pelos utilizadores de aplicações de saúde psicológica do que aplicações de saúde física. Além disso, a humanização dos chatbots é uma ferramenta comprovada para melhorar a qualidade da interacção com os utilizadores. Assim, esta dissertação tem como objetivo investigar se uma entidade chatbot humanizada afeta a sua aceitação no contexto de aplicações de saúde mental. Os resultados sugerem que os chatbots são melhor aceites em aplicações de saúde mental do que em aplicações de saúde física. Além disso, os resultados levam à recomendação da implementação de entidades humanizadas em chatbots dentro de aplicações de saúde mental. Devido ao desenvolvimento contínuo da IA, a utilização de chatbots nos cuidados de saúde mental deve ser investigada numa base contínua

    Bias and Fairness in Chatbots: An Overview

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    Chatbots have been studied for more than half a century. With the rapid development of natural language processing (NLP) technologies in recent years, chatbots using large language models (LLMs) have received much attention nowadays. Compared with traditional ones, modern chatbots are more powerful and have been used in real-world applications. There are however, bias and fairness concerns in modern chatbot design. Due to the huge amounts of training data, extremely large model sizes, and lack of interpretability, bias mitigation and fairness preservation of modern chatbots are challenging. Thus, a comprehensive overview on bias and fairness in chatbot systems is given in this paper. The history of chatbots and their categories are first reviewed. Then, bias sources and potential harms in applications are analyzed. Considerations in designing fair and unbiased chatbot systems are examined. Finally, future research directions are discussed

    Designing a Conversation Mining System for Customer Service Chatbots

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    As chatbots are gaining popularity in customer service, it is critically important for companies to continuously analyze and improve their chatbots’ performance. However, current analysis approaches are often limited to the question-answer level or produce highly aggregated metrics (e.g., conversations per day) instead of leveraging the full potential of the large volume of conversation data to provide actionable insights for chatbot developers and chatbot managers. To address this challenge, we developed a novel chatbot analytics approach — conversation mining — based on concepts and methods from process mining. We instantiated our approach in a conversation mining system that can be used to visually analyze customer-chatbot conversations at the process level. The results of four focus group evaluations suggest that conversation mining can help chatbot developers and chatbot managers to extract useful insights for improving customer service chatbots. Our research contributes to research and practice with novel design knowledge for conversation mining systems

    Automatic generation of software interfaces for supporting decisionmaking processes. An application of domain engineering & machine learning

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    [EN] Data analysis is a key process to foster knowledge generation in particular domains or fields of study. With a strong informative foundation derived from the analysis of collected data, decision-makers can make strategic choices with the aim of obtaining valuable benefits in their specific areas of action. However, given the steady growth of data volumes, data analysis needs to rely on powerful tools to enable knowledge extraction. Information dashboards offer a software solution to analyze large volumes of data visually to identify patterns and relations and make decisions according to the presented information. But decision-makers may have different goals and, consequently, different necessities regarding their dashboards. Moreover, the variety of data sources, structures, and domains can hamper the design and implementation of these tools. This Ph.D. Thesis tackles the challenge of improving the development process of information dashboards and data visualizations while enhancing their quality and features in terms of personalization, usability, and flexibility, among others. Several research activities have been carried out to support this thesis. First, a systematic literature mapping and review was performed to analyze different methodologies and solutions related to the automatic generation of tailored information dashboards. The outcomes of the review led to the selection of a modeldriven approach in combination with the software product line paradigm to deal with the automatic generation of information dashboards. In this context, a meta-model was developed following a domain engineering approach. This meta-model represents the skeleton of information dashboards and data visualizations through the abstraction of their components and features and has been the backbone of the subsequent generative pipeline of these tools. The meta-model and generative pipeline have been tested through their integration in different scenarios, both theoretical and practical. Regarding the theoretical dimension of the research, the meta-model has been successfully integrated with other meta-model to support knowledge generation in learning ecosystems, and as a framework to conceptualize and instantiate information dashboards in different domains. In terms of the practical applications, the focus has been put on how to transform the meta-model into an instance adapted to a specific context, and how to finally transform this later model into code, i.e., the final, functional product. These practical scenarios involved the automatic generation of dashboards in the context of a Ph.D. Programme, the application of Artificial Intelligence algorithms in the process, and the development of a graphical instantiation platform that combines the meta-model and the generative pipeline into a visual generation system. Finally, different case studies have been conducted in the employment and employability, health, and education domains. The number of applications of the meta-model in theoretical and practical dimensions and domains is also a result itself. Every outcome associated to this thesis is driven by the dashboard meta-model, which also proves its versatility and flexibility when it comes to conceptualize, generate, and capture knowledge related to dashboards and data visualizations

    Komunikacja naukowa w Polsce. Szczepionki, medycyna alternatywna, zmiany klimatyczne, GMO pod lupą

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    W monografii zaprezentowano wyniki jakościowego badania przeprowadzonego w projekcie H2020 CONCISE – Communication role on perception and beliefs of EU Citizens about Science, którego celem było zbadanie roli komunikacji naukowej w kształtowaniu wiedzy, opinii i przekonań obywateli Unii Europejskiej na tematy związane z nauką. W Polsce, Hiszpanii, Portugalii, na Słowacji i we Włoszech przeprowadzono jednodniowe konsultacje społeczne, w których brało udział stu odpowiednio dobranych mieszkańców danego kraju. W niniejszej publikacji przedstawione zostały wyniki konsultacji zrealizowanych w Polsce – we wrześniu 2019 roku w Łodzi. W trakcie moderowanej dyskusji uczestnicy konsultacji podzielili się swoimi opiniami na temat komunikacji naukowej, w tym szans i barier upowszechniania informacji naukowej. Publikacja składa się z czterech rozdziałów odpowiadających czterem tematom konsultacji – zmiany klimatyczne, szczepionki, GMO, medycyna alternatywna – oraz z podsumowania. Na podstawie wypowiedzi uczestników konsultacji przeprowadzonych w ramach projektu CONCISE udało się wysnuć wnioski na temat komunikacji naukowej dla omawianych tematów, jednak nie było możliwości znalezienia standardu dla komunikacji naukowej w Polsce, który mógłby stanowić uniwersalny drogowskaz dla naukowców, popularyzatorów nauki czy dziennikarzy. Każdy temat naukowy wymaga właściwej mu strategii komunikacyjnej. Dla każdego z tematów odbiorcy sformułowali odmienne preferencje dotyczące tego, skąd i w jakiej formie chcieliby otrzymywać informacje. Dlatego wydaje się uzasadnioną rekomendacją, aby osoby komunikujące treści naukowe w danym obszarze tematycznym wnikliwie rozpoznały preferencje odbiorców w tym zakresie, zanim przystąpią do komunikacji naukowej.Projekt CONCISE był finansowany z programu Unii Europejskiej „Horyzont 2020” w zakresie badań i innowacji na podstawie umowy o dofinansowanie nr 82453

    Bridging distance: Practical and pedagogical implications of virtual Makerspaces

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    Makerspaces are locations where people with common interests can work on projects, share ideas, tools, and expertise to make or create. There is an abundance of ‘how to’ guides and research studies on physical makerspaces, little research focuses on describing the virtual making processes and the experiences therein. This qualitative study explores the experiences of seven participants who engaged in a synchronous virtual makerspace. Meeting once a month over 16 weeks, members of the International Maker Educator Network (IMEN) participated in the making a robot. This case study describes how the virtual making occurred, the personal experiences of the makers, technology used to support virtual making, and the affordances and inhibitors of virtual making. Data are analysed through the lens of a professional learning community and the People, Means and Activities makerspace framework. The paper concludes with implications for virtual making in practice and future research opportunities
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