Predicting Causes of Reformulation in Intelligent Assistants

Intelligent assistants (IAs) such as Siri and Cortana conversationally interact with users and execute a wide range of actions (e.g., searching the Web, setting alarms, and chatting). IAs can support these actions through the combination of various components such as automatic speech recognition, natural language understanding, and language generation. However, the complexity of these components hinders developers from determining which component causes an error. To remove this hindrance, we focus on reformulation, which is a useful signal of user dissatisfaction, and propose a method to predict the reformulation causes. We evaluate the method using the user logs of a commercial IA. The experimental results have demonstrated that features designed to detect the error of a specific component improve the performance of reformulation cause detection.Comment: 11 pages, 2 figures, accepted as a long paper for SIGDIAL 201

Optimising user engagement in highly automated virtual assistants to improve energy management and consumption

This paper presents a multi-dimensional taxonomy of levels of automation and reparation specifically adapted to Virtual Assistants (VAs) in the context of Human-Human-Interaction (HHI). Building from this framework, the main output of this study provides a method of calculation which helps to generate a trust rating by which this score can be used to optimise users' engagement. The authors believe that this framework could play a critical role in optimising energy efficiency in both management and consumption, particular attention has been given to the relevance of contextual events and dynamism in enhancing trust. For instance by understanding that trust formation is a dynamic process that starts before the user's first contact with the system, and continues long thereafter. Furthermore, following the evolving nature of the system, factors affecting trust and the system itself change during user interactions over time; thus, systems need to be able to adapt and evolve. Present work is being dedicated to further understanding of how contexts and its derivative unintended consequences affect trust in highly automated VAs in the area of energy consumption

Integrating AI and Learning Analytics for Data-Driven Pedagogical Decisions and Personalized Interventions in Education

This research study delves into the conceptualization, development, and deployment of an innovative learning analytics tool, leveraging the capabilities of OpenAI's GPT-4 model. This tool is designed to quantify student engagement, map learning progression, and evaluate the efficacy of diverse instructional strategies within an educational context. Through the analysis of various critical data points such as students' stress levels, curiosity, confusion, agitation, topic preferences, and study methods, the tool offers a rich, multi-dimensional view of the learning environment. Furthermore, it employs Bloom's taxonomy as a framework to gauge the cognitive levels addressed by students' questions, thereby elucidating their learning progression. The information gathered from these measurements can empower educators by providing valuable insights to enhance teaching methodologies, pinpoint potential areas for improvement, and craft personalized interventions for individual students. The study articulates the design intricacies, implementation strategy, and thorough evaluation of the learning analytics tool, underscoring its prospective contributions to enhancing educational outcomes and bolstering student success. Moreover, the practicalities of integrating the tool within existing educational platforms and the requisite robust, secure, and scalable technical infrastructure are addressed. This research opens avenues for harnessing AI's potential in shaping the future of education, facilitating data-driven pedagogical decisions, and ultimately fostering a more conducive, personalized learning environment.Comment: 22 pages, 7 figures, 8537 word

On Conversational Agents in Information Systems Research: Analyzing the Past to Guide Future Work

Conversational agents (CA), i.e. software that interacts with its users through natural language, are becoming increasingly prevalent in everyday life as technological advances continue to significantly drive their capabilities. CA exhibit the potential to support and collaborate with humans in a multitude of tasks and can be used for innovation and automation across a variety of business functions, such as customer service or marketing and sales. Parallel to the increasing popularity in practice, IS researchers have engaged in studying a variety of aspects related to CA in the last few years, applying different research methods and producing different types of theories. In this paper, we review 36studies to assess the status quo of CA research in IS, identify gaps regarding both the studied aspects as well as applied methods and theoretical approaches, and propose directions for future work in this research area

How chatbots are used in recruitment and selection practices?

In the modern era, Artificial Intelligence (AI) has affected different functions of businesses, including Human Resources, in recruitment processes. With Chatbots (conversational agents) systems in place, HR can perform tasks like identifying, selecting, and interviewing talented candidates with more speed and consequently focus on strategic goals more effectively. This study aims to assess the current state of chatbot usage in HR processes in organisations, particularly in Higher Education Institutions (HEIs). For Part Ⅰ, the chatbot’s role is evaluated in detail for each stage of recruitment (i.e., sourcing, screening, selection and onboarding of candidates). Moreover, we will discuss how chatbot providers develop this service in terms of needed technical technologies and communicational aspects. The findings will help identify the best practices in developing better chatbots that align with the demands of modern hiring. In addition, we investigate chatbots’ impact on higher education with the rise of online learning and the Covid-19 pandemic. In part two, we develop a chatbot using the Google DialogFlow platform to support the admission process for prospective PhD students in the Doctoral Management Program of the UPC. This FAQ bot will be implemented as a supplementary channel in the doctoral program website to understand students’ queries and provide predefined answers. A survey is conducted based on the TAM framework to assess the chatbot’s functionality, quality, and intention of use. Based on the responses and findings, we will discuss how chatbots are a viable option to create new innovative services that are helpful for both candidates and educators. In the end, based on lessons learned, we propose conclusions, discussion and several recommendations for these intelligent systems. It is hoped that this work will open up new research possibilities for future optimisations in the fields of chatbots and recruitment in the future.En la era moderna, la Inteligencia Artificial (IA) ha afectado a diferentes funciones de las empresas, incluida la de Recursos Humanos, en los procesos de contratación. Con los sistemas de Chatbots (agentes conversacionales) implementados, HR puede realiza r tareas como identificar, seleccionar y entrevistar a personas candidat as talentos a s con más velocidad y, en consecuencia, enfocarse en objetivos estratégicos de manera más efectiva. Este estudio tiene como objetivo evaluar el estado actual del uso de chatbots en los procesos de recursos humanos en las organizaciones, particularmente en las Instituciones de Educación Superior (IES). Para la Parte Ⅰ, el rol del chatbot se evalúa en detalle para cada etapa del reclutamiento (i.e., planificación, abastecimiento, selec ción, verificación de referencias, selección e incorporación de candidatos). Además, discutiremos cómo los proveedores de chatbots desarrollan este servicio en términos de tecnologías técnicas necesarias y aspectos de comunicación. Los hallazgos ayudarán a identificar las mejores prácticas para desarrollar mejores chatbots que se alineen con las demandas de la contratación moderna. Además, investigamos el impacto de los chatbots en la educación superior con el aumento del aprendizaje en línea y la pandemia de Covid19. En la segunda parte, desarrollamos un chatbot utilizando la plataforma Google DialogFlow para apoyar el proceso de admisión de futuros estudiantes de doctorado Doctorado de la UPC. Este bot de preguntas en el Programa de Gestión de frecuentes se implementará como un canal complementario en el sitio web del programa de doctorado para comprender las consultas de los estudiantes y proporcionar respuestas predefinidas. Se realiza una encuesta basada en el marco TAM para evaluar la funcio nalidad, la calidad y la intención de uso del chatbot. Según las respuestas y los hallazgos, analizaremos cómo los chatbots son una opción viable para crear nuevos servicios innovadores que sean útiles tanto para los personas como para los educadores. Al candidat as final, en base a las lecciones aprendidas, proponemos conclusiones, discusión y varias recomendaciones para estos sistemas inteligentes. Se espera que este trabajo abra nuevas posibilidades de investigación para futuras optimizaciones en los campos de los chatbots y el reclutamiento en el futur

Editor's Note. Towards an intelligent society: advances in marketing and neuroscience

Special Issue on Use Cases of Artificial Intelligence, Digital Marketing and Neuroscience.This Special Issue focuses in cases that explore the relationship between Artificial Intelligence and marketing, as well as neuroscience. AI can be combined with specific neuroscience techniques to achieve a more successful and profitable neuromarketing. For this Special Issue, we have found that descriptions of successful use cases are highly valuable to help researchers identify fields where novel applications of AI can enhance the outcome of digital marketing and neuroscience

Editor’s Note. Towards an Intelligent Society: Advances in Marketing and Neuroscience

This Special Issue focuses in cases that explore the relationship between Artificial Intelligence and marketing, as well as neuroscience. AI can be combined with specific neuroscience techniques to achieve a more successful and profitable neuromarketing. For this Special Issue, we have found that descriptions of successful use cases are highly valuable to help researchers identify fields where novel applications of AI can enhance the outcome of digital marketing and neuroscience

Development of a smart tourism information chatbot for Mauritius

Due to the current COVID-19 situation worldwide, the tourism industry has been heavily impacted worldwide. Chatbots help to minimise the spread of the virus, by limiting physical interaction, whilst help to promote the industry and make available tourism information in an accessible familiar manner. This paper aims to analyse the various aspects of the tourism industry and identify the gaps that need to be addressed in order to improve the customer experiences in Mauritius. The aim was deploy a tourism information chatbot that will provide the necessary information and recommendations to tourists coming to Mauritius and attract potential tourists plan their next trip in a few steps, using off-the-shelf technologies. The main advantage of the developed Chatbot is that is built on off the shelf technologies (Rasa, Telegram, etc), but with the ability to be further extended with APIs. Thus the chatbot developed exhibits a number of innovations for a Tourism chatbot, such as Google search, weather acquisition based on location and COVID-19 statistics

Systematic review of research on artificial intelligence applications in higher education – where are the educators?

According to various international reports, Artificial Intelligence in Education (AIEd) is one of the currently emerging fields in educational technology. Whilst it has been around for about 30 years, it is still unclear for educators how to make pedagogical advantage of it on a broader scale, and how it can actually impact meaningfully on teaching and learning in higher education. This paper seeks to provide an overview of research on AI applications in higher education through a systematic review. Out of 2656 initially identified publications for the period between 2007 and 2018, 146 articles were included for final synthesis, according to explicit inclusion and exclusion criteria. The descriptive results show that most of the disciplines involved in AIEd papers come from Computer Science and STEM, and that quantitative methods were the most frequently used in empirical studies. The synthesis of results presents four areas of AIEd applications in academic support services, and institutional and administrative services: 1. profiling and prediction, 2. assessment and evaluation, 3. adaptive systems and personalisation, and 4. intelligent tutoring systems. The conclusions reflect on the almost lack of critical reflection of challenges and risks of AIEd, the weak connection to theoretical pedagogical perspectives, and the need for further exploration of ethical and educational approaches in the application of AIEd in higher education