Aim/Purpose: This study explores the Knowledge, Attitude, and Perception (KAP) towards ChatGPT among university students and faculty. It also examines the faculty’s readiness to cope with the challenges and leverage the opportunities presented by AI-powered conversational models. Background: Launched on November 30, 2022, ChatGPT took the world by storm with its capability to generate high-quality written expressions in a conversational manner. The reactions to this innovation varied, from enthusiasm regarding its potential to enrich students’ learning to concerns about its threat to students’ cognitive development and academic integrity. A systematic exploration of students’ and faculty’s KAP towards ChatGPT can play an important role in addressing the multifaceted dimensions of AI-driven conversational models. Methodology: This study employs a cross-sectional survey research design based on questionnaires distributed to 145 faculty members, as well as 855 undergraduate and graduate students at the ESPRIT School of Engineering and School of Business. The student sample was based on stratified and convenience sampling, while the faculty sample was based on a consensus sampling approach. Contribution: To the best of our knowledge, this is the first reported study that contributes to understanding the KAP of students and faculty towards ChatGPT, as well as the readiness of faculty to effectively adopt AI-driven conversational models. Furthermore, our research contributes to the body of knowledge by taking Vygotsky’s (1978) principle of social interaction and its role in promoting cognitive development to a new level by hypothesizing that if students were to acquire the competencies to actively engage with AI-driven chatbots in meaningful discussions and collaborative conversations, they might be able to develop some higher-order thinking skills further. Findings: Our results indicated that faculty demonstrated a higher level of ChatGPT knowledge than students and that more than 40% of surveyed students and faculty expressed some trust in the reliability of ChatGPT’s responses, a perception that does not align with reality. Faculty attitude towards ChatGPT was comparatively more reserved compared to that of students and showcased varying opinions. Furthermore, the surveyed faculty showcased a more negative perception of ChatGPT than students, and they expressed a greater degree of skepticism. Our research revealed that 63.4% of surveyed faculty reported that they lack the requisite training and resources to integrate ChatGPT into their pedagogical practices. Recommendations for Practitioners: HEIs should take appropriate measures to enhance students’ and faculty’s knowledge, attitude, and perception regarding ChatGPT to stimulate ethical, meaningful, innovative, and engaging interactions and learning experiences. Recommendation for Researchers: Our study has shed light on some moderating factors that shape the acceptance of AI-driven conversational models and some adoption barriers. It delves into the perceptions, biases, and misconceptions held by both students and faculty, thereby providing a basis for future investigations on the effective integration of AI-driven conversational models in higher education. Impact on Society: This research provides new insights that can harness the potential merits of ChatGPT in enhancing students’ learning while mitigating potential pitfalls. It suggests facilitating open forums and dialogues among students, faculty, employers, and other key stakeholders to debate the impact of AI-driven conversational models on students’ learning and faculty’s teaching and assessment. Future Research: We invite researchers to conduct cross-cultural studies on this topic while also taking into consideration a qualitative research design approach. Future research can also test the hypothesis that AI-driven conversational models inhibit critical thinking by facilitating the passive consumption of information.

In this study, the authors investigate the relationship between human computer interface design and users’ security perception. The authors hypothesize that effective human computer interface design has a positive impact on security perception. To test this hypothesis, they use the seven design elements of the customer interface (7Cs) as a basis of the reference framework for effective interface design. Hypothesis testing was examined through an empirical study involving 247 subjects. Research reveals that human computer interface design significantly affects the perceived security of e-commerce portals. Further analysis of the results highlights that the top HCI factors that influence security perception are permanent working links, demos and online help tools, information accuracy, and easy website navigation. Therefore, this study suggests that applying good user interface design guidelines at the storefront can be an effective technique for enhancing user security perception and increasing trust and purchase intention.

Unmanned aerial vehicles (drones) have gained increased popularity as their innovative uses continue to expand across various fields. Despite their numerous beneficial uses, drones have unfortunately been misused, through many reported cases, to launch illegal and sometimes criminal activities that pose direct threats to individuals, organizations, public safety and national security. These threats have recently led law enforcement agencies and digital forensic investigators to pay special attention to the forensic aspects of drones. This important research topic, however, remains underexplored. This study aims to further explore drone forensics in terms of challenges, forensic investigation procedures and experimental results through a forensic investigation study performed on a Parrot AR drone 2.0. In this study, the authors present new insights on drone forensics in terms of forensic approaches, access to drone's digital containers and the retrieval of key information that can assist digital forensic investigators establish ownership, recuperate flight data and gain access to media files.

Considers tandem queues with correlated arrivals and joining interference, which model a portion of a virtual circuit at the access node in an ATM network. The authors focus the main analysis on a two-node tandem network, which they model as a discrete-time queueing system. They derive a functional equation relating the joint generating function of this system between two consecutive slots. The functional equation is then transformed into a suitable form, which enables the derivation of the steady-state joint generating function of the contents of the queues. From this, any moment of the queue length at each node can be extracted. They also give explicit expressions for the average time delay at each node as well as for the total average delay in the network. They illustrate the solution technique by some numerical examples.