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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]

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]