Cybersecurity breaches are often attributed to human behaviour, where individuals fail to integrate ethical principles in their decision-making. This empirical study investigates the effectiveness of the Ripple Down Rules (RDR) method, a knowledge acquisition and representation method, in enhancing ethical awareness and reasoning in cybersecurity contexts. The proposed approach combines rule-based reasoning, case-based learning, reflection, and situated cognition to bridge the gap between ethical knowledge and action by systematically connecting scenario elements to ethical principles. Participants, recruited from a cohort of first-year psychology students, were exposed to training incorporating five ethical principles—Beneficence, Non-Maleficence, Justice, Autonomy, and Explicability—applied to realistic cybersecurity scenarios. The study employed a randomised controlled design with two treatment and one control groups, using pre- and post-study assessments to evaluate improvements in ethical principle identification and reasoning. Participants rated RDR as a clear and helpful tool for understanding ethical reasoning, with sensibility and helpfulness scores ranging from moderate to high. Results demonstrate that RDR training significantly improved participants' ability to identify ethical principles compared to learning without RDR, particularly for principles like autonomy and explicability. However, challenges persisted in distinguishing overlapping principles, such as beneficence and non-maleficence. Implications and guidance for use of RDR for ethics training are discussed.

Interventions to improve health and well-being abound. Whether they are designed for prevention, maintenance or improvement, a key challenge is the motivation of the user to change their current behaviours, such as persisting or taking new actions. To encourage someone to change their behaviour requires persuading them to change their goals and/or their beliefs about the behaviour or their ability to perform it. Our embodied conversational agent (ECA) uses explanations based on the goals and beliefs of the user to promote a sense of personalisation and engagement with the treatment plan which could form a bond as the dyad develop shared goals and tasks together. To keep our message minimal and understand whether belief-based or goal-based explanations are more efficacious in changing behaviour intention, we collected data in the context of a scenario where the ECA seeks to change four behaviours recommended to help students manage their study stress. Our findings suggest that when the behaviour requires a change in desire, we need goal-based explanation, when adoption of the behaviour requires addressing a barrier we need belief-based explanation and warrant future investigation. Further, the stratified analysis suggested that more tailoring to the student’s context could provide more motivation to change.

Virtual reality (VR) technology can increase prosocial behaviour toward a target person or group by enhancing their empathic response for the subject, but such technology has not always improved learning outcomes. This interdisciplinary study compared the potential advantages of delivering the same learning material about daily life in an ancient Greek household via two modes of delivery: VR technology and classroom lecture. The VR group explored a Greek villa containing historical artefacts and virtual characters with whom they were able to interact through set dialogues. The dialogues illustrated social hierarchies, gender relations, the situation of slaves, cult practice, and religious beliefs. The classroom group received the same information in a classroom environment. Both randomly-assigned groups answered a multiple-choice quiz to evaluate the knowledge gained. They also responded to open-text questions designed to test the degree of empathy that was aroused. We found that classroom lecture delivery was significantly superior in terms of the acquisition of factual knowledge, consistent with cognitive learning theory. We identified this as learning with the mind. The immersive VR environment, however, imparted a level of empathic response to the lived experiences of people in ancient Greece; in that sense it allowed learning with the heart.

Virtual advisors (VAs) are being utilised almost in every service nowadays from entertainment to healthcare. To increase the user's trust in these VAs and encourage the users to follow their advice, they should have the capability of explaining their decisions, particularly, when the decision is vital such as health advice. However, the role of an explainable VA in health behaviour change is understudied. There is evidence that people tend to change their intentions towards health behaviour when the persuasion message is linked to their mental state. Thus, this study explores this link by introducing an explainable VA that provides explanation according to the user's mental state (beliefs and goals) rather than the agent's mental state as commonly utilised in explainable agents. It further explores the influence of different explanation patterns that refer to beliefs, goals, or beliefs&goals on the user's behaviour change. An explainable VA was designed to advise undergraduate students how to manage their study-related stress by motivating them to change certain behaviours. With 91 participants, the VA was evaluated and the results revealed that user-specific explanation could significantly encourage behaviour change intentions and build good user-agent relationship. Small differences were found between the three types of explanation patterns.

To personalise dialogue to different users, relational agents need to learn about the users' preferences for relational cues used by the agent. In the context of a virtual advisor to reduce students' study stress, we designed a between subjects study with three groups (empathic, neutral and adaptive) who either received all cues, no cues or helpful cues only, respectively, and compared rapport and changes in study stress scores. To avoid the cold start problem, we sought to train the agent and adapt its dialogue to include or exclude 10 relational cues based on the user's responses to whether an example of each relational cue is found helpful prior to the session with the virtual advisor. The results of an experiment with 111 students show that the rapport scores for the empathic and adaptive groups were significantly higher than the neutral group; change in rapport scores was significantly higher in the adaptive group than in the empathic group. Furthermore, study stress scores significantly reduced for the adaptive and empathic groups, but not for the neutral group. We found some relationships between the number of times students found helpful what they received and other variables. We also found that the number of discrepancies and matches between what relational cues users received and what they found helpful were greatest in the adaptive group. This indicates the effectiveness of this approach for dealing with the cold start problem.

Intelligent virtual agents are being endowed with empathic behaviours to perform roles such as virtual therapists. Studies often evaluate the level of rapport established, but do not measure the therapeutic benefit and the relative advantage of empathic versus neutral behaviours. We have created two virtual (empathic/neutral) therapists. Our experiment with 63 participants consisted of one within-subjects (empathic/neutral) and one between-subjects (order) factors. Regardless of the virtual therapist used, improvements in baseline emotion were reported after the first interaction (time one) and further improvement after the second interaction (time two). Our study reveals that if the human initially expresses strong emotional feeling for a problem they are facing, rapport will be higher for the empathic therapist and the level of rapport established at the first meeting will persist regardless of whether the second encounter used empathic or neutral dialogue. Conversely, participants experiencing low emotional feeling reported greater rapport with the neutral therapist, and that level of rapport persisted in the second encounter with the alternative therapist. This study shows that an empathic agent will not necessarily build more rapport or deliver better emotional outcomes than a neutral agent. Further studies are needed to determine when tailoring and complex behaviours are justified.

This study investigates the influence of a virtual human (VH) with recorded human voice vs VH with a machine-generated voice (text-to-speech) on building trust and working alliance. We measured the co-presence perception to understand the impact of VH's perception on building the human-VH relationship. The results revealed no differences between the two types of voices on co-presence perception, trust or working alliance.

Understanding how and in what circumstances users respond to different verbal expressions of empathy will be important for designing Intelligent Virtual Agents able to influence the emotions and intrinsic motivations of users. We report a study to teach healthy study habits and tips involving 239 undergraduate students, 161 of which interacted with a character designed to express empathy through dialogue. We elicited participants' personality and psychological state (depression, anxiety and stress levels) and attitudes to study. In this paper we present a detailed analysis of participants' responses to specific empathic dialogue snippets designed to include one or more empathic cues to determine whether they found it helpful, stupid or empathic. We provide an example of how we have used data mining on our dataset to suggest which cues are most appropriate to which user types to enhance user models and improve agent decision-making regarding the expression of empathy.

Dance has universally been used as a form of human expression for thousands of years. This common human behaviour and communication method has not been explored much in the context of computer-based technology, even within the field of virtual human research. This paper presents an experimental study investigating the impact of watching dancing virtual characters on human emotions. The study analysed the responses of 55 participants, composed of a mix of dancers and non-dancers, who watched a dancing virtual character perform 3 different dances that represented anger, sadness and happiness in different display orders. The participants’ reported changes in their emotions and their feelings of anger, sadness and happiness were significantly dependent on which dancing character’s emotion they watched and the emotional change did not rely on correct recognition of the depicted emotion. For experimental control, our characters were faceless and danced without music. Our results suggest that just by watching a dancing virtual character some of the benefits associated with dancing could be accessed in circumstances where it is not desirable or feasible to dance, justifying further research to develop a personalised character with a face and music that adapts according to the humans’ emotions and preferences.

This paper addresses the question of how to develop forecasting models resulting from business processes that can be embodied in an intelligent decision support system. Moreover the design is suitable for evolving logistics and economic situations in which ports plan or foresee to have an improved economic role. The key objective of this work is to offer a model-based approach to Time Series Data Mining (TSDM) based on the assumptions that the time series may be produced by an underlying model, and that its flexibility is suitable to perform multivariate time-series analysis encompassing the notion of model selection and statistical learning known as the core of forecasting systems. Results indicate that for the period 2001 to 2005, the commodity throughput of coffee (tons) handled in the port of Buenaventura gains importance in the prediction of the Colombian national exports of coffee, thus indicating that the port operation was able to affect the economy in this regard. The previous period was strongly affected by outliers, creating a random walk process difficult to fit but feasible to produce due to unstable conditions evidenced in the economy.