The Coronavirus pandemic has created complex challenges and adverse circumstances. This research identifies public sentiment amidst problematic socioeconomic consequences of the lockdown, and explores ensuing four potential public sentiment associated scenarios. The severity and brutality of COVID-19 have led to the development of extreme feelings, and emotional and mental healthcare challenges. This research focuses on emotional consequences - the presence of extreme fear, confusion and volatile sentiments, mixed along with trust and anticipation. It is necessary to gauge dominant public sentiment trends for effective decisions and policies. This study analyzes public sentiment using Twitter Data, timealigned to the COVID-19 reopening debate, to identify dominant sentiment trends associated with the push to reopen the economy. Present research uses textual analytics methodologies to analyze public sentiment support for two potential divergent scenarios - an early opening and a delayed opening, and consequences of each. Present research concludes on the basis of textual data analytics, including textual data visualization and statistical validation, that tweets data from American Twitter users shows more positive sentiment support, than negative, for reopening the US economy. This research develops a novel sentiment polarity based public sentiment scenarios (PSS) framework, which will remain useful for future crises analysis, well beyond COVID-19. With additional validation, this research stream could present valuable time sensitive opportunities for state governments, the federal government, corporations and societal leaders to guide local and regional communities, and the nation into a successful new normal future.

This article conducts a meta-analysis to determine the effect of perceived risk on intention to purchase in e-commerce settings. The literature generally favors the negative relationship between the two variables; however, some studies have found this relationship to be not significant or even positive. Thirty-five studies were examined, representing 39 overall samples across multiple contexts with a total sample size of 13779 and overall weighted effect size (Pearson’s r) for all studies of −0.362 (95% CI: −.432, −.286), a significant medium effect, with a stronger effect in B2C than B2B contexts. Major IS journals rarely report non-significant results and reflect publication bias, posing a problem for a comprehensive meta-analysis. Due to the reluctance to report or accept, studies showing non-significant results represent a significant challenge for IS research. Our aim is to encourage more meta-analysis in IS literature and utilize this methodology to analyze previously held theories.

Introduction: Artificial intelligence in the educational domain has many uses; however, using AI specifically to enhance education and teaching in a K-12 environment poses the most significant challenges to its use. Beyond usage and application, the quality of the education is made even more arduous due to the dynamics of teaching primary and secondary school children, whose needs far exceed mere fact recollection. Utilizing prior research using AI in education and online education in the K-12 space, we explore some of the hurdles that AI applications face in K-12 teaching and provide core attributes for a “Turing Teacher,” i.e., an AI powered technology for learning, specifically targeting the K-12 space.Methods: Using a survey, which included qualitative responses during the implementation of online learning during the Covid Pandemic, we analyze the results using univariate and multivariate tests and analyzed the qualitative responses to create core attributes needed for AI powered teaching technology.Results: The results present the challenges faced by any technology in an education setting and show that AI technology must help overcome negative feelings about technology in education. Further, the core attributes identified in the research must be addressed from the three stakeholder perspectives of teachers, parents and students.Discussion: We present our findings and lay the groundwork for future research in the area of AI powered education. The Turing Teacher must be able to adapt and collaborate with real teachers and address the varying needs of students. In addition, we explore the use of AI technology as a means to close the digital divide in traditionally disadvantaged communities.

This study works on the development of a Generative AI University Support System (GenAI-USS) by improvising retrieval augmented generation (RAG) architecture to improve the performance of Large Language Models (LLMs) in a way that supports step-wise transparency. We aim to achieve improved accuracy of responses to queries based on university data assimilated from university webpages and knowledge sources. We employ RAG to develop a plug-and-play mechanism along with prompt selection to boost LLM accuracy. One of the key components in our GenAI-USS is the capture and integration of real-time information via live retrieval into the generative AI process. This domain-specific knowledge assimilation with real-time updates to capture changes and new information serves as a specialized dynamic expert knowledge database for RAG. Our RAG mechanism pulls in relevant, up-to-date information from the dynamic database, which pulls real-time data from targeted predetermined knowledge sources. The other key component in our GenAI-USS design is the deliberately designed information processing visibility at each stage of the process to ensure full transparency, and this includes: overview, data collection, storage encoding, testing, chatbot interaction, and search. The testing module allows for interactive viewing of generated responses and their sources. Our strategy is expected to lead to higher-quality AI-generated output via targeted information retrieval, hallucination mitigation, accuracy improvement, and timely data updates. Essentially, on the submission of a query, the RAG-dependent GenAI-USS first identifies the most relevant information from the specialized expert knowledge database and then factors this into the generative AI response development process. This results in a transparent RAG-based generative AI process with notable improvements in the quality of output produced.

Contemporary public discourse surrounding artificial intelligence (AI) often exhibits a disproportionate level of fear and confusion relative to AI’s factually documented capabilities and implications. This study examines how the systematic use of alarmist and fear-inducing language by news media outlets contributes to negative public perceptions of AI. This study analyzed nearly 70,000 AI-related news headlines using natural language processing (NLP) methods, machine learning (ML) algorithms, and large language models (LLMs) to identify dominant themes and sentiment patterns. The theoretical framework draws on existing literature that posits the power of fear-inducing headlines to influence public perception and behavior, even when such headlines represent a relatively small proportion of total coverage. The methods used include classical and state-of-the-art NLP techniques such as word frequency analysis, sentiment analysis, emotion classification, topic modeling, and thematic evaluations by human experts. Topic modeling and fear sentiment classification were performed using BERT, LLaMA, and Mistral, in conjunction with supervised ML techniques. The findings show a consistently notable presence of emotionally negative and fear-laden language in AI news coverage. This portrayal of AI as being dangerous to humans, or other negative views such as AI being an existential threat to humanity, has a profound impact on public perception, and the resulting AI phobia and confusion lead to behavioral resistance toward AI, and are inherently detrimental to the science of AI. Furthermore, this can also have an adverse impact on AI policies and regulations, leading to a stunted growth environment for AI. This study concludes with the articulation of implications and recommendations to counter fear-driven narratives, and suggests ways to improve public understanding of AI through responsible news media coverage, broad AI education, democratization of AI resources, and the drawing of clear distinctions between AI as a science versus commercial AI applications, to promote enhanced fact-based mass engagement with AI, while preserving human dignity and agency.

The Coronavirus pandemic has created complex challenges and adverse circumstances. This research discovers public sentiment amidst problematic socioeconomic consequences of the lockdown, and explores ensuing four potential sentiment associated scenarios. The severity and brutality of COVID-19 have led to the development of extreme feelings, and emotional and mental healthcare challenges. This research identifies emotional consequences - the presence of extreme fear, confusion and volatile sentiments, mixed along with trust and anticipation. It is necessary to gauge dominant public sentiment trends for effective decisions and policies. This study analyzes public sentiment using Twitter Data, time-aligned to COVID-19, to identify dominant sentiment trends associated with the push to 'reopen' the economy. Present research uses textual analytics methodologies to analyze public sentiment support for two potential divergent scenarios - an early opening and a delayed opening, and consequences of each. Present research concludes on the basis of exploratory textual analytics and textual data visualization, that Tweets data from American Twitter users shows more trust sentiment support, than fear, for reopening the US economy. With additional validation, this could present a valuable time sensitive opportunity for state governments, the federal government, corporations and societal leaders to guide the nation into a successful new normal future.