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The purpose of this study is to examine the mechanism of benevolent leadership on employee cheating behavior through two paths – employee uncertainty and perceived acceptability of norm violation – and also reveal the possible dark side of benevolent leadership. Meanwhile, the moderating effects of leader behavioral integrity in the cognition dual path process are also discussed.

This study invites 383 employees and their superiors in seven Chinese enterprises as the research objects and conducts a paired survey at three time points, and then Mplus 7.4 software is used to analyze the empirical data.

The results are shown as follows. Benevolent leadership plays a positive role on uncertainty and perceived acceptability of norm violation. Uncertainty and perceived acceptability of norm violation mediate the relationship between benevolent leadership and cheating behavior, respectively. Leader behavioral integrity moderates the positive role of benevolent leadership on uncertainty and perceived acceptability of norm violation. Leader behavioral integrity moderates the indirect effect of benevolent leadership on employees' cheating behavior through uncertainty and perceived acceptability of norm violation.

This study reveals the mechanism behind the negative role of benevolent leadership through the cognition reaction of employees to benevolent leadership and broadens the research scope of benevolent leadership. Meanwhile, it provides some practical inspiration for leaders to effectively use the benevolent leadership style and restrain employees' cheating behavior.

The use of generative artificial intelligence (genAi) language models such as ChatGPT to write assignment text is well established. This paper aims to assess to what extent genAi can be used to obtain guidance on how to avoid detection when commissioning and submitting contract-written assignments and how workable the offered solutions are.

Although ChatGPT is programmed not to provide answers that are unethical or that may cause harm to people, ChatGPT’s can be prompted to answer with inverted moral valence, thereby supplying unethical answers. The authors tasked ChatGPT to generate 30 essays that discussed the benefits of submitting contract-written undergraduate assignments and outline the best ways of avoiding detection. The authors scored the likelihood that ChatGPT’s suggestions would be successful in avoiding detection by markers when submitting contract-written work.

While the majority of suggested strategies had a low chance of escaping detection, recommendations related to obscuring plagiarism and content blending as well as techniques related to distraction have a higher probability of remaining undetected. The authors conclude that ChatGPT can be used with success as a brainstorming tool to provide cheating advice, but that its success depends on the vigilance of the assignment markers and the cheating student’s ability to distinguish between genuinely viable options and those that appear to be workable but are not.

This paper is a novel application of making ChatGPT answer with inverted moral valence, simulating queries by students who may be intent on escaping detection when committing academic misconduct.

For researchers and sector agencies around the world, poor academic integrity is seen as a significant risk to universities (Bretag & Harper, 2017; QAA, 2020; TEQSA, 2019). Given the existential threat that acts of academic misconduct are deemed to pose to higher education, interest in ways of developing academic integrity is correspondingly high and often invokes the concept of values (Macfarlane et al., 2014; Morris, 2018). However, to ascribe to academic integrity, the status of a self-evident, perhaps universal, value of contemporary higher education is contentious on many levels. This chapter takes in turn three of the most common ways in which students and scholars infringe on the principles of academic integrity – plagiarism, collusion, and contract cheating – and explores what the prohibiting of each of these acts reveals about the values of contemporary higher education. It argues that, far from neutral or universal, the values of academic integrity appear both normative and culturally specific, promoting a particular conception of higher education which risks excluding large sections of the global population. To counter this, the notion of threshold values – borrowing from Meyer and Land's (2005) notion of the “threshold concept” – is proposed, identifying the development of shared values as crucial to ensuring that contemporary, globalized universities are inclusive and accessible spaces.

This study aims to address a systematic literature review (SLR) using bibliometrics on the relationship between academic integrity and artificial intelligence (AI), to bridge the scattering of literature on this topic, given the challenge and opportunity for the educational and academic community.

This review highlights the enormous social influence of COVID-19 by mapping the extensive yet distinct and fragmented literature in AI and academic integrity fields. Based on 163 publications from the Web of Science, this paper offers a framework summarising the balance between AI and academic integrity.

With the rapid advancement of technology, AI tools have exponentially developed that threaten to destroy students' academic integrity in higher education. Despite this significant interest, there is a dearth of academic literature on how AI can help in academic integrity. Therefore, this paper distinguishes two significant thematical patterns: academic integrity and negative predictors of academic integrity.

This study also presents several contributions by showing that tools associated with AI can act as detectors of students who plagiarise. That is, they can be useful in identifying students with fraudulent behaviour. Therefore, it will require a combined effort of public, private academic and educational institutions and the society with affordable policies.

This study proposes a new, innovative framework summarising the balance between AI and academic integrity.

Academic integrity in tertiary education is a global concern. This chapter describes academic integrity in Australian universities and proposes an “it takes a village” framework to guide universities toward a re-evaluation of academic integrity education. It takes a village to raise a child – a child needs role models and positive influences from multiple people for healthy growth and development. With regard to academic integrity, the parallel is that the entire university community needs to be involved to foster development of students of integrity. The institution and its community need to provide structures, multiple positive and effective learning experiences, and clear guidelines to support both staff and students. In this chapter, we argue that academic integrity needs to be seen as a complex system, one in which everyone involved has responsibility to develop and maintain a culture of integrity and one which supports a student throughout their academic journey.

This paper aims to center the experiences of three cohorts (n = 40) of Black high school students who participated in a critical race technology course that exposed anti-blackness as the organizing logic and default setting of digital and artificially intelligent technology. This paper centers the voices, experiences and technological innovations of the students, and in doing so, introduces a new type of digital literacy: critical race algorithmic literacy.

Data for this study include student interviews (called “talk backs”), journal reflections and final technology presentations.

Broadly, the data suggests that critical race algorithmic literacies prepare Black students to critically read the algorithmic word (e.g. data, code, machine learning models, etc.) so that they can not only resist and survive, but also rebuild and reimagine the algorithmic world.

While critical race media literacy draws upon critical race theory in education – a theorization of race, and a critique of white supremacy and multiculturalism in schools – critical race algorithmic literacy is rooted in critical race technology theory, which is a theorization of blackness as a technology and a critique of algorithmic anti-blackness as the organizing logic of schools and AI systems.

Tourism recovery after the pandemic has failed to take the path leading to sensitivity and humaneness at destination level. This chapter argues that to open this path, we need to confront the belief that tourists are self-centred, fun-driven and cheating individuals. This view on tourists and more generally human beings is central to the neoliberal understanding of consumers. It has moreover taken a strong grasp on the mind of economists, politicians, academics and the public at large.

To counteract this idea, I call upon Aristotle's discussion of friendship. In Nicomachean Ethics, Aristotle distinguishes between three forms of friendship: of utility, of pleasure, and of goodwill. Utility implies a relationship where people befriend each other in virtue of some good or service that they get or expect to get from each other. Friendships of utility, therefore, imply reciprocation. Friendships of pleasure can also be understood as a form of reciprocal altruism. However, friendships of goodwill are different because they are felt for others for their own sake and not in expectation of a favour in return. Friendships of goodwill include therefore others who may not be able to reciprocate, such as tourists staying only a short time at a destination. Looking through the lens of friendships of goodwill, one could argue that all tourists, including short-stay visitors, will be friendly and caring towards their hosts.

This chapter explores the soundness of friendships of goodwill in the light of more recent research on human nature. It also discusses its implications for our understanding of human beings, the relationship between hosts and guests, and ultimately the opportunity to steer tourism along a more sensitive, human and sustainable path.

The purpose of this paper is to provide a transcript of a dialogue among literacy educators and researchers on the impact of generative aritficial intelligence (AI) in the field. In the spring of 2023, a lively conversation emerged on the National Council of Research on Language and Literacy (NCRLL)’s listserv. Stephanie initiated the conversation by sharing an op-ed she wrote for Atlanta Journal-Constitution about the rise of ChatGPT and similar generative AI platforms, moving beyond the general public’s concerns about student cheating and robot takeovers. NCRLL then convened a webinar of eight leading scholars in writing and literacies development, inspired by that listerv conversation and an organizational interest in promoting intergenerational collaboration among literacy scholars.

As former doctoral students of two of the panel participants, webinar facilitators Grace and Victoria positioned themselves primarily as learners about this topic and gathered questions from colleagues, P-16 practitioners and those outside the field of education to assess the concerns and wonderings that ChatGPT and generative AI have raised. The following webinar conversation was recorded on two different days due to scheduling conflicts. It has been merged and edited into one dialogue for coherence and convergence.

Panel participants raise a host of questions and issues that go beyond topics of ethics, morality and basic writing instruction. Furthermore, in dialogue with one another, they describe possibilities for meaningful pedagogy and critical literacy to ensure that generative AI is used for a socially just future for students. While the discussion addressed matters of pedagogy, definitions of literacy and the purpose of (literacy) education, other themes included a critique of capitalism; an interrogation of the systems of power and oppression involved in using generative AI; and the philosophical, ontological, ethical and practical life questions about being human.

This paper provides a glimpse into one of the first panel conversations about ChatGPT and generative AI in the field of literacy. Not only are the panel members respected scholars in the field, they are also former doctoral students and advisors of one another, thus positioning all involved as both learners and teachers of this new technology.

This study examines the impact of generative artificial intelligence (GenAI), particularly ChatGPT, on higher education (HE). The ease with which content can be generated using GenAI has raised concerns across academia regarding its role in academic contexts, particularly regarding summative assessments. This research makes a unique contribution to the literature by examining university student and staff perceptions of current and future issues pertaining to the role of GenAI in universities.

A qualitative method involving five one-to-one semi-structured interviews with four students and a lecturer explored the ethical and practical issues of GenAI text generation in academia. An inductive thematic analysis was chosen as it provided nuanced insights aligned with the study’s goals.

Use of GenAI was discussed within the context of a range of topics, including perceptions of academic misconduct, authorial integrity and issues pertaining to university policies. Participants universally defined traditional classifications of academic misconduct but were unable to provide clear definitions where the use of GenAI was included for writing summative assessments. Students showed a more open engagement with GenAI, considering it a tool for overcoming obstacles rather than a means to plagiarise. Educators were generally more cautious and less optimistic about the academic role of GenAI. Lack of clear institutional policies surrounding such tools also contributed to ethical ambiguities.

The study highlights diverging perspectives between students and academics, which necessitate a forum for dialogue, ensuring the need to develop clear policies to steer the integration of GenAI in a manner that is beneficial for students and academics.