The rise of generative AI in higher education has disrupted our traditional understandings of academic integrity, moving our focus from clear-cut infractions to evolving ethical judgment. In this study, a survey of 401 students from major U.S. universities provides insight into how beliefs, behaviors, and policy awareness intersect in shaping how students interact with AI-assisted writing. The findings indicate that students’ ethical beliefs—not institutional policies—are the strongest predictors of perceived misconduct and actual AI use in writing. Policy awareness was found to have no significant effect on ethical judgments or behavior. Instead, students who believe AI writing is cheating were found to be substantially less likely to view it as ethical or engage with it. These findings suggest that many students do not treat AI use in learning activities as an extension of conventional cheating (e.g., plagiarism), but rather as a distinct category of academic conduct/misconduct. Rather than using punitive models to attempt to punish students for using AI, this study suggests that education about AI ethics and the risk of AI overreliance may prove more successful for curbing unethical AI use in higher education.

In response to the increasing complexity and sophistication of cyber threats, particularly those enhanced by advancements in artificial intelligence, traditional security methods are proving insufficient. This paper provides an overview of the zero-trust cybersecurity framework, which operates on the principle of “never trust, always verify” to mitigate vulnerabilities within organizations. Specifically, this paper examines the applicability of zero-trust principles in environments where large volumes of information are exchanged, such as schools and libraries, highlighting the importance of continuous authentication (proving who users are within the network), least privilege access (providing only access to what users specifically need), and breach assumption (assuming a breach has or will occur and thus operating to limit the spread through the use of multiple checkpoints throughout the network). The analysis highlights avenues for future research that may help preserve the security of vulnerable organizations.

The rise of generative AI in higher education has disrupted our traditional understandings of academic integrity, moving our focus from clear-cut infractions to evolving ethical judgment. In this study, 401 students from major U.S. universities provide insight into how beliefs, behaviors, and policy awareness intersect in shaping how students interact with AI-assisted writing. The findings indicate that students’ ethical beliefs – not institutional policies – are the strongest predictors of perceived misconduct and actual AI use in writing. Policy awareness was found to have no significant effect on ethical judgments or behavior. Instead, students who believe AI writing is cheating were found to be substantially less likely to view it as ethical or engage with it. These findings suggest that many students do not treat AI use in learning activities as an extension of conventional cheating (e.g., plagiarism), but rather as a distinct category of academic conduct/misconduct. Rather than using punitive models to attempt to punish students for using AI, this study suggests that education about AI ethics and the risk of AI overreliance may prove more successful for curbing unethical AI use in higher education.

In the contemporary landscape of academia, the integration of artificial intelligence (AI) has significantly altered the dynamics of academic research and writing. This study delves into the perceptions of academic misconduct among university students in the age of AI. Through a structured online survey, participants will articulate their perspectives on various activities and their classification as academic misconduct, as well as the severity of such transgressions. By probing into students' beliefs and attitudes, the research seeks to uncover potential disparities in the understanding and interpretation of academic misconduct within higher education settings. The advent of AI technologies has revolutionized the academic environment, providing students with unparalleled access to information and writing assistance. However, this paradigm shift has also brought forth heightened concerns surrounding academic integrity, particularly with regards to plagiarism. Moreover, the widespread adoption of grammar-checking tools such as Grammarly adds further layers of complexity, as the outputs sometimes resemble those generated by AI, blurring the lines between original work and automated assistance. Utilizing quantitative research methods, this study employs an online survey platform to collect data on student perceptions of academic misconduct. The insights collected from this investigation aim to contribute to a nuanced understanding of the multifaceted nature of academic integrity in the era of AI. Ultimately, the findings seek to inform educational policies and practices, fostering a cohesive and informed approach to upholding academic standards across diverse higher education institutions.

The emergence of AI writers has significantly affected and reshaped the environment of academic research and paper writing in the world. Many university students relying on these AI tools for searching information and assisting their writing. However, there will be a difference existed in the utilization and perceptions of AI writers between American and international students. To find out potential difference, this study seeks to delve into the underlying factors that may contribute to this contrast. Employing the Technology Acceptance Model (TAM) as a theoretical framework, which investigates users' acceptance and adoption of technology, the research employs a mixed-method approach comprising surveys and interviews conducted among students from both American and international contexts. By integrating TAM into the methodology, the study aims to investigate the motivations to use of ChatGPT, an AI writing tool, and examine how linguistic and cultural differences influence the user experience of AI writers. Through this approach, the research sheds light on the nuanced ways in which AI writers are integrated into the academic workflows of students from diverse backgrounds. By comparing and contrasting the perspectives and practices of American and international students, the study will provide valuable insights into the interplay between technology, language, and culture in the educational domain. Ultimately, this research will contribute to a deeper understanding of the complex dynamics shaping the adoption and usage of AI writers among higher education students worldwide.

The integration of AI generated content into academic research and paper writing has revolutionized scholarly practices, granting students unprecedented access to information and writing support. However, this advancement has also raised serious concerns regarding plagiarism and academic integrity, leading to the emergence of AI detection services. Despite the proliferation of these tools, questions persist about the reliability and consistency of the services, posing challenges to upholding academic integrity. Additionally, the use of grammar check programs like Grammarly by students has introduced further complexities, as their outputs may sometimes resemble those produced by AI writers, blurring distinctions between original work and automated assistance. This research poster investigates and compares the perceptions and attitudes of students and instructors towards AI writers and AI writing detection programs. By examining the perceived usefulness, trustworthiness, and efficiency of AI detection services in preventing plagiarism and promoting academic integrity, the research seeks to uncover divergent attitudes and perceptions between these two groups. Employing quantitative research methods, including surveys with students and instructors, this study delves into the actual use of AI writing detection services. Specifically, by exploring and comparing the perceptions and attitudes towards these technologies between the two groups, the research aims to uncover potential gaps and areas for improvement. Ultimately, the findings of this study aim to inform the development of strategies and guidelines to enhance AI writing detection services, thereby fostering a culture of academic integrity in the digital age.