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The recent advances in educational technology enabled the development of solutions that collect and analyse data from learning scenarios to inform the decision-making processes. Research fields like Learning Analytics (LA) and Artificial Intelligence (AI) aim at supporting teaching and learning by using such solutions. However, their adoption in authentic settings is still limited, among other reasons, derived from ignoring the stakeholders' needs, a lack of pedagogical contextualisation, and a low trust in new technologies. Thus, the research fields of Human-Centered LA (HCLA) and Human-Centered AI (HCAI) recently emerged, aiming to understand the active involvement of stakeholders in the creation of such proposals. This paper presents a systematic literature review of 47 empirical research studies on the topic. The results show that more than two-thirds of the papers involve stakeholders in the design of the solutions, while fewer papers involved them during the ideation and prototyping, and the majority do not report any evaluation. Interestingly, while multiple techniques were used to collect data (mainly interviews, focus groups and workshops), few papers explicitly mentioned the adoption of existing HC design guidelines. Further evidence is needed to show the real impact of HCLA/HCAI approaches (e.g., in terms of user satisfaction and adoption). [ABSTRACT FROM AUTHOR]

This paper explores the different insider employee-led cyber frauds (IECF) based on the recent large-scale fraud events of prominent Indian banking institutions. Examining the different types of fraud and appropriate control measures will protect the banking industry from fraudsters. In this study, we identify and classify Cyber Fraud (CF), map the severity of the fraud on a scale of priority, test the mitigation effectiveness, and propose optimal mitigation measures. The identification and classification of CF losses were based on a literature review and focus group discussions with risk and vigilance officers and cyber cell experts. The CF was analyzed using secondary data. We predicted and prioritized CF based on machine learning-derived Random Forest (RF). An efficient fraud mitigation model was developed based on an offender-victim-centric approach. Mitigation is advised both before and after fraud occurs. Through the findings of this research, banks and fraud investigators can prevent CF by detecting it quickly and controlling it on time. This study proposes a structured, sustainable CF mitigation plan that protects banks, employees, regulators, customers, and the economy, thus saving time, resources, and money. Further, these mitigation measures will improve the reputation of the Indian banking industry and ensure its survival. [ABSTRACT FROM AUTHOR]

This paper explores the effect of chatbot empathy and identity disclosure on willingness to donate (WTD) to a fundraising project and the psychological mechanism in the relationship. In a 3 (information-only vs. cognitive empathy vs. affective empathy) × 2 (chatbot vs. human identity) experiment, 496 US adults had a conversation about a fundraising event with one of six artificial intelligence-powered chatbots. The results revealed that neither chatbot empathy nor identity disclosure has a significant effect on WTD, rejecting the hypotheses. However, the data supported a significant interaction effect between chatbot empathy and identity disclosure on WTD. Moreover, the results suggested that the interaction effect between chatbot empathy and identity disclosure on WTD would be mediated through human likeness and social presence. This study suggests how empathy and human identity of chatbots should be applied with care to avoid the uncanny valley effect. [ABSTRACT FROM AUTHOR]

The growing importance of social media in conflicts and crises is accompanied by an ever-increasing research interest in the crisis informatics field in order to identify potential benefits and develop measures against the technology's abuse. This special issue sets out to give an overview of current research on the use of social media in conflicts and crises. In doing so, it focuses on both good and malicious aspects of social media and includes a variety of papers of conceptual, theoretical and empirical nature. In six sections, the special issue presents an overview of the field, analytical methods, technical challenges, current advancements and the accepted papers before concluding. Specific topics range from cyber deception over information trustworthiness to mining and near-real-time processing of social media data. [ABSTRACT FROM AUTHOR]

This paper presents a study about communicability of conversational interfaces (namely chatbots) under a semiotic perspective. A chatbot is a software system that allows you to simulate real conversations between devices and users by means of a conversational interface (CI). After introducing the chatbot concept, focusing on its advantages and issues, we will present two domains of use in which chatbot interfaces can be effective: healthcare and smart home. For carrying out simple tasks such as finding information or triggering operations, users need an easy-to-use and to an easy-to-learn system to communicate with. To face this, conversational interfaces represent the latest trend in the field of digital design. For studying the communicability aspects of a CI, we carried out a user test to compare traditional and chatbot interfaces. This paper aims at evaluating the benefits at the communicability level of a chatbot in comparison to traditional GUI for incrementing the effectiveness and efficacy of communication between users and the system specifically for users with poor attitude in using technologies. In details, we evaluated the communicability of two prototypes that can be used to solve simple tasks in order to favour user inclusion, including everyone with very little exposure to technologies. [ABSTRACT FROM AUTHOR]

Personal Intelligent Agents (PIAs) like Siri and Alexa are becoming more popular among users. In this paper, we investigate the effect of humour and voice of personal intelligent agents, their impact on users' perceptions of intelligence and anthropomorphism, and the relationship of these perceptions with cognitive- and emotion-based trust. The results from an online experiment show that humour and voice significantly and positively influence users' perceptions of anthropomorphism. These perceptions positively impact users' emotion-based trust, which increases their intention to use the PIA. We also find that perceptions of intelligence shape users' cognitive-based trust in the PIA. Our model is novel because it examines two key design characteristics of PIAs and articulates their effects on user perceptions. The effect of human-like characteristics, specifically humour and voice, on perceptions of intelligence and anthropomorphism and the potential impact on users' cognitive- and emotion-based trust in PIAs have not been explored in an IS context. [ABSTRACT FROM AUTHOR]

There are great interests of designing research metrics and indices to measure the research impacts in research institutes. Unfortunately, most of those indices ignore critical design issues, e.g. the disparity between domains, the impact of journals or conferences in which papers are published, normalising the range of the index values to certain intervals, and the scalability of using the index to rank different research entities. In this paper, a new normalised fuzzy index, (NFindex), is proposed as a fuzzy-based research impact metric. The proposed index is a scalable index whose values are normalised to the percentage levels. NFindex achieves both inter-discipline normalisation and intra-discipline consistency. The capability of NFindex to achieve the inter-discipline normalisation enables fair comparison between different research domains regardless their nature in terms of influence and contribution to other research areas, e.g. natural science. Therefore, NFindex gives a universal normalised single-number metric that can be used by research institutes to solve the problem of inter-discipline scholar ranking. Moreover, it can help universal ranking of universities and research institutes according to their research capabilities and impacts. The obtained results, on diverse research areas, prove the potential of NFindex in terms of both intra-discipline consistency and inter-discipline normalisation. [ABSTRACT FROM AUTHOR]