The research examines junk news, followers of problematic sources as well as artificial amplification on Instagram during the 2019 Dutch provincial and European parliamentary elections. First, this study looks at the circulation of junk content in high-engagement political spaces on Instagram. Second, it takes up the question of the mainstreaming of Dutch junk news providers by looking at the intersection between the followers of Dutch political entities and those of junk news sources. Third, it looks at the presence of artificial engagement tactics (specifically fake followers) employed by Dutch political entities and news sources on Instagram. In all it was found that Dutch political Instagram is a relatively healthy space, but not for all issues or political entities.
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The use of immersive technologies has changed the consumption environment in which retailers provide services. We present findings from a study designed to investigate consumer responses toward a $17 million AI-embedded mixed reality (MR) exhibit in a retail/entertainment complex which combines advanced technology entertainment with retail shopping. Findings from our study demonstrate that the quality of AI (i.e., speech recognition and synthesis via machine learning) associated with an augmented object increases MR immersion associated with spatial immersion, MR enjoyment, and consumers’ perceptions of novel experiences. Collectively, these increase consumer engagement, and positively influence behavioral responses—specifically, purchase intentions and intentions to share experiences with social groups. Overall, findings from this study show that interactive AI and MR technology open new avenues to promote consumer engagement.
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In the book, 40 experts speak, who explain in clear language what AI is, and what questions, challenges and opportunities the technology brings.
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Abstract Aims: Medical case vignettes play a crucial role in medical education, yet they often fail to authentically represent diverse patients. Moreover, these vignettes tend to oversimplify the complex relationship between patient characteristics and medical conditions, leading to biased and potentially harmful perspectives among students. Displaying aspects of patient diversity, such as ethnicity, in written cases proves challenging. Additionally, creating these cases places a significant burden on teachers in terms of labour and time. Our objective is to explore the potential of artificial intelligence (AI)-assisted computer-generated clinical cases to expedite case creation and enhance diversity, along with AI-generated patient photographs for more lifelike portrayal. Methods: In this study, we employed ChatGPT (OpenAI, GPT 3.5) to develop diverse and inclusive medical case vignettes. We evaluated various approaches and identified a set of eight consecutive prompts that can be readily customized to accommodate local contexts and specific assignments. To enhance visual representation, we utilized Adobe Firefly beta for image generation. Results: Using the described prompts, we consistently generated cases for various assignments, producing sets of 30 cases at a time. We ensured the inclusion of mandatory checks and formatting, completing the process within approximately 60 min per set. Conclusions: Our approach significantly accelerated case creation and improved diversity, although prioritizing maximum diversity compromised representativeness to some extent. While the optimized prompts are easily reusable, the process itself demands computer skills not all educators possess. To address this, we aim to share all created patients as open educational resources, empowering educators to create cases independently.
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In this article I will discuss theories on students’ success in higher education and the need for adjustments of these theories in the contemporary, information and media saturated world. The integration theory on student retention, founded by Tinto and further developed by him and many others, lies at the base of most studies on student success. In line with Tinto’s theory the majority of studies measure both social and academic integration of a student, alongside background variables. Social integration is shaped by the personal contact with fellow students and staff and whether or not a student enjoys being at the institute. Academic integration has more to do with academic achievement and sharing the academic norms and values. Although the distinction of these types of integration has been experienced as an artificial one and has been abandoned in more recent studies, the conclusion of most studies remains the same: the higher the level of integration, the greater the level of commitment, which in turn has a positive affect on the likelihood of student persistence in college and the success of a student. More recent studies use ‘engagement’ to embed the various factors of integration to avoid the rigid distinction between social and academic and to include new forms of communication between students for social, academic and other purposes. Furthermore the world has changed since the origin of Tinto’s integration theory in the early eighties, especially if you look at the changes in society under the influence of technology in general and in particular the Internet. New ways of communicating has emerged which brought along new possibilities. The emergence of smart phones has played a big part in the various ways we communicate. The new devices and communication tools have made it possible to employ integrating social and academic activities without the necessity of physical presence. The central question of the article is: Should online communities or engaging platforms like Facebook, be taken into account when investigating the influential facors of student success?
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The field of data science and artificial intelligence (AI) is growing at an unprecedented rate. Manual tasks that for thousands of years could only be performed by humans are increasingly being taken over by intelligent machines. But, more importantly, tasks that could never be performed manually by humans, such as analysing big data, can now be automated while generating valuable knowledge for humankind
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Purpose The purpose of this paper is to examine how the quality of change information influences employees’ attitude toward organizational change and turnover intention. Additionally, the role of engagement, psychological contract fulfillment and trust in the relationship between change information and attitude toward change is assessed. Design/methodology/approach In a technology services organization that was implementing a “new way of working,” questionnaire data of 669 employees were gathered. The organizational change in question sought to increase employees’ autonomy by increasing management support and improving IT support to facilitate working at other locations (e.g. at home) or at hours outside of regular working hours (e.g. in evening). Findings The results showed that change information was positively related to psychological contract fulfillment and attitude toward change. Engagement and psychological contract fulfillment were positively related to attitude toward change and negatively related to turnover intention. Contrary to what was expected, trust did not influence attitude toward change but was negatively related to turnover intention. Practical implications The study presents a model that can help management to foster positive affective, behavioral, and cognitive responses to change, as well as to reduce employee turnover. Fulfilling employees’ psychological contracts and cultivating engagement is important in this respect, as well as continuously considering whether information about the organizational change is received in good time, is useful, is adequate and satisfies employees’ questions about the change. Originality/value As one of the first studies in its field, attitude toward change was conceptualized and operationalized as a multidimensional construct, comprising an affective, a behavioral and a cognitive dimension.
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Brochure from the Inauguration of Klaas Dijkstra, professor Computer Vision and Data Science
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Personal data is increasingly used by cities to track the behavior of their inhabitants. While the data is often used to mainly provide information to the authorities, it can also be harnessed for providing information to the citizens in real-time. In an on-going research project on increasing the awareness of motorists w.r.t. the environmental consequences of their driving behavior, we make use of sensors, artificial intelligence, and real-time feedback to design an intervention. A key component for successful deployment of the system is data related to the personal driving behavior of individual motorists. Through this outset, we identify challenges and research questions that relate to the use of personal data in systems, which are designed to increase the quality of life of the inhabitants of the built environment.
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Explainable Artificial Intelligence (XAI) aims to provide insights into the inner workings and the outputs of AI systems. Recently, there’s been growing recognition that explainability is inherently human-centric, tied to how people perceive explanations. Despite this, there is no consensus in the research community on whether user evaluation is crucial in XAI, and if so, what exactly needs to be evaluated and how. This systematic literature review addresses this gap by providing a detailed overview of the current state of affairs in human-centered XAI evaluation. We reviewed 73 papers across various domains where XAI was evaluated with users. These studies assessed what makes an explanation “good” from a user’s perspective, i.e., what makes an explanation meaningful to a user of an AI system. We identified 30 components of meaningful explanations that were evaluated in the reviewed papers and categorized them into a taxonomy of human-centered XAI evaluation, based on: (a) the contextualized quality of the explanation, (b) the contribution of the explanation to human-AI interaction, and (c) the contribution of the explanation to human- AI performance. Our analysis also revealed a lack of standardization in the methodologies applied in XAI user studies, with only 19 of the 73 papers applying an evaluation framework used by at least one other study in the sample. These inconsistencies hinder cross-study comparisons and broader insights. Our findings contribute to understanding what makes explanations meaningful to users and how to measure this, guiding the XAI community toward a more unified approach in human-centered explainability.
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