The use of generative AI (genAI) in higher education is rapidly evolving, provoking both optimism and concern among educators. While some students embrace genAI tools as learning aids, others, including many educators, remain cautious about their implications for critical thinking and academic integrity. Accepting that genAI is readily available and banning its use is not feasible, we explore how it might be integrated meaningfully into pedagogy through the lens of Transformative Learning Theory (TLT). This study investigates how genAI tools influence student learning in an IT Forensics course using diverging assessments, a form of assessment-as-learning where students receive the same authentic tasks but unique data inputs. We examine three research questions addressing genAI's impact on learning strategies, its role in supporting assessment-as-learning tasks, and how it fosters critical reflection and transformation in student learning. Drawing on interviews with 14 students, our findings suggest that, when scaffolded appropriately, genAI use within diverging assessments can catalyze transformative learning by provoking disorienting dilemmas, encouraging reflection, and reshaping problem-solving approaches. Finally, implications for teaching practice and assessment design are discussed.

Purpose The current advancements in technologies and the internet industry provide users with many innovative digital devices for entertainment, communication and trade. However, simultaneous development and the rising sophistication of cybercrimes bring new challenges. Micro businesses use technology like how people use it at home, but face higher cyber risks during riskier transactions, with human error playing a significant role. Moreover, information security researchers have often studied individuals’ adherence to compliance behaviour in response to cyber threats. The study aims to examine the protection motivation theory (PMT)-based model to understand individuals’ tendency to adopt secure behaviours. Design/methodology/approach The study focuses on Australian micro businesses since they are more susceptible to cyberattacks due to the least security measures in place. Out of 877 questionnaires distributed online to Australian micro business owners through survey panel provider “Dynata,” 502 (N = 502) complete responses were included. Structural equational modelling was used to analyse the relationships among the variables. Findings The results indicate that all constructs of the protection motivation, except threat susceptibility, successfully predict the user protective behaviours. Also, increased cybersecurity costs negatively impact users’ safe cyber practices. Originality/value The study has critical implications for understanding micro business owners’ cyber security behaviours. The study contributes to the current knowledge of cyber security in micro businesses through the lens of PMT.

Purpose Intellectual property (IP) theft is an increasing threat that can lead to large financial losses and reputational harm. These attacks are typically noticed only after the IP is stolen, which is usually too late. This paper aims to investigate the psychological profile and the socio-technical events that statistically predict the likelihood of an IP threat. Design/methodology/approach This paper analyses 86 IP theft cases found in court documents. Two novel analyses are conducted. The research uses LLMs to analyse the personality of these insiders, which is followed by an investigation of the pathways to the attack using behaviour sequence analysis (BSA). Findings These IP theft insiders scored significantly higher on measures of Machiavellianism compared to the normal population. Socio-technical variables, including IP theft via photographs, travelling overseas, approaching multiple organisations and delivering presentations, were identified. Contrary to previous assumptions that there is a single pathway to an attack, the authors found that multiple, complex pathways lead to an attack (sometimes multiple attacks). This work, therefore, provides a new framework for considering critical pathways to insider attacks. Practical implications These findings reveal that IP theft insiders may come across as charming, star employees rather than the stereotype of disgruntled employees. Moreover, organisations’ policies may need to consider that IP theft occurs via non-linear and multiple pathways. This means that sequences of events need to be considered in detecting these attacks instead of anomalies outright. The authors also argue that there may be a case for “continuous evaluation” to detect insider activity. Originality/value This paper offers a new framework for understanding and studying insider threats. Instead of a single critical pathway, this work demonstrates the need to consider multiple interconnected pathways. It elucidates the importance of a multidisciplinary approach and provides opportunities to reconsider current practices in detection and prevention.

Generative adversarial networks (GANs) have recently shown high success in applications such as image and time- series classification. However, those applications are vulnerable to complex hacking scenarios, for example, inference and data poisoning attacks, which would alter or infer sensitive information about systems and users. Protecting Electroencephalographic (EEG) brain signals against illegal disclosure has a great interest these days. In this paper, we propose a privacy-preserving GAN method to generate and classify EEG data effectively. Generating EEG data offers a range of capabilities, including sharing experimental data without infringing user privacy, improving machine learning models for brain-computer interface tasks and restore corrupted data. The proposed GAN model is trained under a differential privacy model to enhance the data privacy level by limiting queries of data from artificial trials that could identify the real participants from their EEG signals. The performance of the proposed method was evaluated using a motor imagery classification task, where real EEG data are augmented with artificially generated samples for training machine learning classifiers. The evaluation was performed on a benchmark EEG data set for nine subjects. The experimental outcomes revealed that the non-private version of the proposed approach could produce high-quality data that significantly improve the motor imagery classification performance. The private version showed lower but comparable performance to the standard models trained on real data only.

Online romance scams are a prevalent form of mass-marketing fraud in the West, and yet few studies have presented data-driven responses to this problem. In this type of scam, fraudsters craft fake profiles and manually interact with their victims. Because of the characteristics of this type of fraud and how dating sites operate, traditional detection methods (e.g., those used in spam filtering) are ineffective. In this paper, we investigate the archetype of online dating profiles used in this form of fraud, including their use of demographics, profile descriptions, and images, shedding light on both the strategies deployed by scammers to appeal to victims and the traits of victims themselves. Furthermore, in response to the severe financial and psychological harm caused by dating fraud, we develop a system to detect romance scammers on online dating platforms. This paper presents the first fully described system for automatically detecting this fraud. Our aim is to provide an early detection system to stop romance scammers as they create fraudulent profiles or before they engage with potential victims. Previous research has indicated that the victims of romance scams score highly on scales for idealized romantic beliefs. We combine a range of structured, unstructured, and deep-learned features that capture these beliefs in order to build a detection system. Our ensemble machine-learning approach is robust to the omission of profile details and performs at high accuracy (97%) in a hold-out validation set. The system enables development of automated tools for dating site providers and individual users.

Abstract An online diary study was performed to investigate deception across different media. One hundred and four individuals participated in the study, with 76 completing the diaries. Individuals were most likely to lie on the telephone. Planned lies, which participants also rated the most serious, were more likely told via SMS (short message service) text messaging. Most lies were told to people participants felt closest to. The feature‐based model provides a better account of the deceptions reported by participants than do media richness theory or social distance theory. However, the authors propose a reworked feature‐based model to explain deception across different media. They suggest that instant messaging should be treated as a near synchronous mode of communication. We suggest that the model needs to distinguish between spontaneous and planned lies.

This research expands on the work of Goffman (1959) in seeking to examine how players of Massively Multiplayer Online games (MMOs) use virtual environments as a mechanism to explore their own offline identity, through the use of multiple characters and gender swapping. Using Thematic Analysis of four interviews, five themes have been identified which will inform a larger Grounded Theory study. It is argued that these themes provide the foundation for the construction of solid theoretical constructs which will inform future discussion on all interaction in virtual environments -- not only in computer games, but all other social technologies.