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AbstractThe service-learning and civic engagement trends within higher education are designed to address historic harms, but bring another set of concerns around extraction. While much research has lionized the impact of higher education hiring and spending on community, we build upon a smaller line of research that recognizes the harms of civic engagement and attempts to address them. We use a post-extractive approach that seeks to move university-community relationships toward more equitable and sustainable partnerships. We reflect upon our organizing of the Public Administration Theory Network (PATNET) conference and the successes and failures of our attempt to create a conference that limited extraction and invested in community. We describe our attempts to address critiques of extraction by hosting the conference in community spaces and paying organizations to be our hosts. In doing so we utilized literature on workarounds that avoid bureaucratic red tape. The workarounds addressed the challenges of paying grassroots community organizations—which limited the impact of our conference in building sustainable relationships with the university. We discuss how creating workarounds for subawards and contractor payments to community organizations could help to address the exclusion caused by these processes. [ABSTRACT FROM AUTHOR]

This paper explores the growing complexity of detecting and differentiating generative AI from other AI interventions. Initially prompted by noticing how tools like Grammarly were being flagged by AI detection software, it examines how these popular tools such as Grammarly, EditPad, Writefull, and AI models such as ChatGPT and Microsoft Bing Copilot affect human-generated texts and how accurately current AI-detection systems, including Turnitin and GPTZero, can assess texts for use of these tools. The results highlight that widely used writing aids, even those not primarily generative, can trigger false positives in AI detection tools. In order to provide a dataset, the authors applied different AI-enhanced tools to a number of texts of different styles that were written prior to the development of consumer AI tools, and evaluated their impact through key metrics such as readability, perplexity, and burstiness. The findings reveal that tools like Grammarly that subtly enhance readability also trigger detection and increase false positives, especially for non-native speakers. In general, paraphrasing tools score low values in AI detection software, allowing the changes to go mostly unnoticed by the software. However, the use of Microsoft Bing Copilot and Writefull on our selected texts were able to eschew AI detection fairly consistently. To exacerbate this problem, traditional AI detectors like Turnitin and GPTZero struggle to reliably differentiate between legitimate paraphrasing and AI generation, undermining their utility for enforcing academic integrity. The study concludes by urging educators to focus on managing interactions with AI in academic settings rather than outright banning its use. It calls for the creation of policies and guidelines that acknowledge the evolving role of AI in writing, emphasizing the need to interpret detection scores cautiously to avoid penalizing students unfairly. In addition, encouraging openness on how AI is used in writing could alleviate concerns in the research and writing process for both students and academics. The paper recommends a shift toward teaching responsible AI usage rather than pursuing rigid bans or relying on detection metrics that may not accurately capture misconduct. [ABSTRACT FROM AUTHOR]

Background: With artificial intelligence (AI) increasingly revolutionising medicine, this study critically evaluates the integration of large language models (LLMs), known for advanced text processing and generation capabilities, in medical ethics education, focusing on promoting virtue. Positing LLMs as central to mimicking nuanced human communication, it examines their use in medical education and the ethicality of embedding AI in such contexts. Method: Using a hybrid approach that combines principlist and non-principlist methodologies, we position LLMs as exemplars and advisors. Results: We discuss the imperative for including AI ethics in medical curricula and its utility as an educational tool, identify the lack of educational resources in medical ethics education, and advocate for future LLMs to mitigate this problem as a "second-best" tool. We also emphasise the critical importance of instilling virtue in medical ethics education and illustrate how LLMs can effectively impart moral knowledge and model virtue cultivation. We address expected counter-arguments to using LLMs in this area and explain their profound potential to enrich medical ethics education, including facilitating the acquisition of moral knowledge and developing ethically grounded practitioners. Conclusions: The study involved a comprehensive exploration of the function of LLMs in medical ethics education, positing that tools such as ChatGPT can profoundly enhance the learning experience in the future. This is achieved through tailored, interactive educational encounters while addressing the ethical nuances of their use in educational settings. [ABSTRACT FROM AUTHOR]

Despite the growing interest in emotions in engineering education, empirical research on incorporating them into engineering ethics education is limited. Therefore, we designed this experimental study to assess how different methods for integrating compassion into engineering ethics cases influenced the intensity of compassion associated with the protagonists of the cases. We utilised modified versions of the Engineering and Science Issues Test (ESIT) cases, employing three methods to intensify compassion associated with the cases' protagonists: (i) implicit induction, (ii) explicit expression, and (iii) through the description of severe consequences. The participants (n = 415), predominantly engineering students (90%), were divided into one control group and three experimental groups. Results indicated that all three methods increased the intensity of compassion in the cases. However, the implicit method had a relatively weaker impact than the other two methods which had similar effects on the intensity of compassion. Other emotions did not seem to be impacted by the changes. This study provides valuable insights into effective methods to increase the intensity of compassion in engineering ethics cases without affecting other emotions. [ABSTRACT FROM AUTHOR]

The present study explores the major reasons for committing plagiarism, as reported in published literature. One hundred sixty-six peer-reviewed articles, which were retrieved from the Scopus database, were carefully examined to find out the research studies conducted to explore the most common reasons for academic cheating among students and researchers in different disciplines in higher education. An analysis of collected literature reveals that 19 studies were conducted to identify the perceived reasons of committing plagiarism. Four studies with similar constructs of perceived reasons of committing plagiarism, namely busy schedule, overload of homework and laziness, easy accessibility of electronic resources, poor knowledge in research writing and correct citation and lack of serious penalty, were conducted. The pooled mean and standard deviation of the four studies reveal that easy accessibility of electronic resources (Mean = 3.6, SD = 0.81), unawareness of instructions (Mean = 3.0, SD = 0.89), and busy schedule, overload of homework and laziness (Mean = 2.89, SD = 1.0) are important perceived reasons for committing plagiarism. The study findings could help create an effective intervention and a robust anti-plagiarism policy for academic institutions, administrators, and policymakers in detecting academic dishonesty while emphasizing the value of integrity in academic pursuit. [ABSTRACT FROM AUTHOR]

Generative Artificial Intelligence (GenAI), such as ChatGPT, is reshaping educational paradigms by offering unparalleled benefits and introducing challenges, particularly academic integrity. This study investigates teaching laboratory practices (traditional, recorded, remote, simulation and virtual), considered an academic safe haven due to its authenticity, and examines how assessments align with learning objectives. This should reinvigorate interest in expanding laboratory learning opportunities. However, unsupervised laboratory reports, a dominant assessment type, present significant cheating risks – intensified by GenAI. Given the scant literature on laboratory assessments and their primary focus on cognitive objectives, little guidance is available regarding how to assess non-cognitive objectives. This studies innovative approach utilises a reflective survey with 134 international academic staff to explore how each assessment type can verify cognitive, psychomotor, and affective learning objectives. We introduce a ‘Words of Estimative Probability’ heatmap to visualise the likelihood of verifying specific learning objectives, providing a snapshot to guide academics in holistic assessment design. This study advocates for diverse assessments, which mitigate GenAI risks and foster comprehensive skill development. This research equips educators to design secure, effective laboratory education in STEM disciplines, ensuring alignment with evolving academic and technological landscapes by offering a framework for improving assessment validity, integrity, and adaptability. [ABSTRACT FROM AUTHOR]

Recent media and literature have focused on the high incidence of academic integrity breaches among international students. However, the studies that reached this conclusion primarily took place in the Americas, Oceania, or the Far East. Most of these studies also rely on students’ voluntary self-reporting of academic dishonesty and generally have small sample sizes. Our study examines the UK context and investigates the occurrence of academic integrity violations within the international student group as compared to domestic students. The research examines a large sample of 12,066 student submissions, with 1406 individual breaches of academic integrity over a period of 3 years (2021–2023). These breaches are classified based on the intensity of the breach as negligence, malpractice, or misconduct. The findings indicate that international students are more likely to engage in malpractice as compared to negligence and misconduct. These findings provide the necessary data-backed information and insights for the co-construction of policies to uphold academic integrity in UK universities. [ABSTRACT FROM AUTHOR]

The aim of the present study has been to analyze the relationship between the frequency of using ChatGPT for academic purposes and the level of plagiarism, as well as to determine if this relationship is moderated by a series of student-centered variables, such as their academic performance, workload, time management, amotivation, hypercompetitiveness, cheating culture, and unawareness of the consequences of plagiarism. For this purpose, a total of 507 Spanish university students participated, responding to a series of scales to measure the previously mentioned constructs. The results reveal, first, that there is a correlation between the frequency of using ChatGPT for academic purposes and plagiarism, but the causality between these variables is almost nonexistent, regardless of moderating variables. Likewise, it is observed that cheating culture and amotivation did turn out to be significant variables in predicting plagiarism. These results confirm that a higher frequency of using ChatGPT for academic purposes does not necessarily lead to a higher likelihood of plagiarism, as ChatGPT can be used academically for responsible purposes. Furthermore, it is certain personal variables of university students, such as being involved in a cheating culture or experiencing amotivation, that cause the likelihood of plagiarism to increase. [ABSTRACT FROM AUTHOR]

Background: Evaluating professional values is crucial to developing effective strategies for integrating them into professional performance and clinical education. A standard questionnaire is an instrument that can be used to evaluate professional values. This study aimed to assess the validity and reliability of the Nurses Professional Values Scale-Revised (NPVS-R) among nursing students in the Persian language. Methods: This methodological and cross-sectional study aimed to evaluate the validity and reliability of the Persian version of the NPVS-R among nursing students. Following a back-translation process, the Persian version was subjected to face, content, and construct validity assessments. Construct validity was examined using exploratory factor analysis (EFA) on a sample of 439 nursing students during their internships at 16 public universities of medical sciences in Iran between April and July 2019. Additionally, convergent and divergent validity were assessed on a separate sample of 105 nursing students. The reliability of the instrument was assessed using both internal consistency measures and the test–retest method. Furthermore, the psychometric properties of the questionnaire were analyzed using Samejima's Gradual Response Model. Data analysis was performed using SPSS (version 19) and MPLUS (version 5.1). Results: All components had a content validity ratio greater than 0.6 and a content validity index of 1, indicating excellent content validity. Exploratory factor analysis reduced the items to 25, with five factors explaining 52.26% of the variance. The scale exhibited strong convergent validity (r = 0.73, p < 0.001) and satisfactory divergent validity (r = 0.12, p = 0.21; r = 0.07, p = 0.47). Test–retest reliability was excellent, with an intraclass correlation coefficient (ICC) of 0.92. Cronbach's alpha coefficients ranged from 0.53 to 0.83 for the factors and 0.91 for the entire scale, showing good internal consistency. IRT analysis confirmed that alpha parameters for all items were statistically significant and at an acceptable level. Thus, the Persian NPVS-R, with 25 items and 5 factors, is a valid and reliable tool for nursing students. Conclusions: The Persian NPVS-R, comprising 25 items and 5 factors, demonstrated excellent validity and reliability. This scale serves as a standardized tool for evaluating the professional values of nursing students, suitable for application in educational and clinical settings to enhance nursing practice and education. [ABSTRACT FROM AUTHOR]

AbstractThis article presents two strategies for integrating GenAI into assessments: the first strategy involves using the GenAI Integration Rubric to guide ethical AI usage, while the second strategy focuses on the Experimentation Outcome Template to document lessons learned and drive continuous improvement through Monitoring & Evaluation (M&E). These strategies provide practical implications for academic institutions seeking to integrate GenAI into assessments, supporting the reskilling of students while maintaining academic integrity. [ABSTRACT FROM AUTHOR]

Academic dishonesty hinders the accurate evaluation of student learning in higher education. Previous research indicates that both Honesty-Humility and descriptive social norms predict dishonest behavior. Here, we investigated the role of seriousness—the moral weight of dishonest acts—as an underlying dimension of academic dishonesty which can influence the predictive quality of both determinants. We demonstrate that Honesty-Humility has a stronger negative association with serious behaviors, whereas descriptive social norms show a stronger positive association with non-serious behaviors. We investigated a sample of 856 German university students who completed two surveys during a semester in a nationwide study. Using multiple methodologies (i.e. cross-validation with an independent sample, multidimensional scaling, cross-validation with judgments of perceived seriousness), we show that seriousness can be validly measured by assessing the self-reported prevalence of behaviors. This measurement approach is valuable as it does not require additional effort by the researcher. As predicted, structural equation modeling showed that Honesty-Humility was more strongly associated with serious behaviors, while descriptive social norms were more strongly associated with non-serious behaviors. Our findings underscore the theoretical significance of seriousness as a fundamental dimension of academic dishonesty, contributing to a more nuanced understanding of this broad construct. Furthermore, we discuss how recognition of seriousness of dishonest behaviors can inform the development of more targeted and potentially more effective prevention strategies in higher education contexts. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic had a profound impact on education, forcing many teachers and students who were not used to online education to adapt to an unanticipated reality by improvising new teaching and learning methods. Within the realm of virtual education, the evaluation methods underwent a transformation, with some assessments shifting towards multiple-choice tests while others attempted to replicate traditional pen-and-paper exams. This study conducts a comparative analysis of these two types of evaluations, utilizing real data from a virtual semester during the COVID-19 pandemic at an Ecuadorian institution. It aims to assess the impact of transitioning from one evaluation method to the other, revealing fundamental structural differences. These differences can lead to disparities that unfairly advantage or disadvantage certain student groups based on the evaluation method used. Beyond identifying the causes of these discrepancies, the study reveals that, for the specific case and dataset analyzed, the shift to virtual education led to a significant and abrupt increase in passing percentages. Moreover, under one specific type of evaluation, there is a possibility that a minimum of 21.1% of students may have passed a course due to cheating or other forms of academic dishonesty, while at least 5.5% could have failed that course despite possessing the necessary capabilities. [ABSTRACT FROM AUTHOR]

AbstractAs higher education grapples with ensuring assessment validity in an increasingly AI-populated time, institutions and educators are working to establish appropriate boundaries for AI use. However, little is known about how students and teachers conceptualize and experience these boundaries in practice. This study investigates how students and teachers navigate the line between acceptable and unacceptable AI use in assessment, drawing on a thematic analysis of qualitative interviews with 19 students and 12 staff at a large Australian university informed by social boundary theory. The titular metaphor of ‘drawing a line’ emerged organically from both students and staff in our interviews, revealing ongoing struggles to understand and articulate what counts as appropriate. We found that students frequently construct their own individually unique and often complex ethical frameworks for AI use. Teachers, meanwhile, report significant emotional burden and professional uncertainty as they attempt to understand and communicate what is appropriate to their students. Our analysis suggests that assessment policies for AI ought to move beyond simple prohibitions or permissions and begin to address three critical dimensions: the feasibility of enforcement, the preservation of authentic learning, and the emotional wellbeing of teachers and students. [ABSTRACT FROM AUTHOR]

This research explores the impact of the unforeseen global crisis on education and assessment practices, investigating plagiarism rates through a comprehensive analysis of 25,864 written assignments from 42 academic institutions in 2019, 2020, and 2023 before, during, and after crises, such as pandemics. Utilising the theory of planned behaviour comparing undergraduate plagiarism rates before, during, and after the COVID-19 pandemic using an automated plagiarism detection tool (Originality). The comparison traverses academic fields, disciplines, institutional rankings, and written language assessments. Contextualising this phenomenon is crucial for a comprehensive understanding and targeted interventions against undesirable behaviour. The results show that before and after COVID-19, plagiarism rates were similar, while during COVID-19, plagiarism rates augmented. Furthermore, assignments written in English contained higher plagiarism rates than those in Arabic and Hebrew. We discuss post-pandemic plagiarism rates bounce-back in higher education on the one hand and concern about artificial intelligence writing tools on the other hand. We also discuss plagiarism among multilingual students. The study concludes with practical implications and suggestions for future research in the evolving landscape of academic integrity. [ABSTRACT FROM AUTHOR]

AbstractThe integration of generative AI (GenAI) in higher education is transforming teaching, learning, and research, offering opportunities for innovation and efficiency. However, its widespread adoption faces challenges related to ethical considerations, data privacy, intellectual property, and compliance with evolving legal frameworks. Universities are cautiously adopting GenAI, focusing on maintaining academic integrity while exploring new ways to integrate AI into assessments and student learning. This article focuses on University College London’s (UCL) approach to GenAI and offers insights from the practices of other universities too. The article emphasizes the need for secure environments in GenAI integration and outlines steps to accelerate the process, such as acquiring licenses, forming working groups at faculty and institutional levels, and sharing best practices through standardized templates. It highlights the importance of cross-institutional collaboration, including libraries, and adaptable policies for the responsible use of GenAI, ensuring ethical practices and innovation. Recommendations focus on balancing innovation with responsibility in higher education’s adoption of GenAI. [ABSTRACT FROM AUTHOR]

This paper examines the concept of academic integrity. Drawing on Calhoun's social perspective of integrity and on MacIntyre's goods-based view of practice, we propose to understand acting with academic integrity as standing before others and with others, firmly but non-dogmatically, to protect the integrity of academic practice and, therefore, its internal goods –which are not readily specifiable but nevertheless related to growth, learning, and knowledge production and sharing. This view differs from others that take academic integrity as compliance with rules and procedures of academic institutions, or as a general virtue closely related to honesty, that simply happens to be applied in academic settings. Some implications of our view include the fact that not cheating or breaking rules of academic institutions is not enough for acting with academic integrity, the need to be aware of and collectively resist internal and external –including societal– pressures and threats, and the demands on academic institutions and organisations to promote democratic climates that allow all their members to speak up, and to favour democratic-educative over causal-objective approaches –such as incentive-based and cheating-proof designs– to protect the integrity of academic practice. [ABSTRACT FROM AUTHOR]

AbstractPurposeMethodResultConclusionA systematic review was conducted to describe various viewpoints expressed in speech-language pathology literature over four decades concerning ethics knowledge in the profession.The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were employed. Our sample search encompassed peer-reviewed journal articles sourced from electronic databases including MEDLINE, CINAHL, ERIC, MasterFILE Premier, E-Journals, Africa-Wide Information, and Academic Search Premier, spanning the period from 1980 to 2022.The initial search of articles identified 139 papers. After applying rigorous inclusion and exclusion criteria, 60 full-text papers were deemed suitable for inclusion. We conducted a qualitative, deductive analysis of the relevant data and findings. Studies primarily concentrated on the speech-language pathologist’s roles in rehabilitation/management and education, research, and administration. A predominant focus of these papers revolved around moral judgement, a fundamental component of moral behaviour.Findings suggest an evolving landscape within the speech-language pathology discipline, with an increased focus on moral judgement, professional competency, and rehabilitation/management. The findings highlight a consistent need for further development and emphasis on ethics in education, research, administration, and advocacy/consultation roles. This underscores the necessity for ongoing ethical education and support to ensure practitioners are well-equipped to navigate the complex ethical landscape of their professional practice. [ABSTRACT FROM AUTHOR]

This article investigates the role of artificial intelligence (AI) in libraries, focusing on applications and ethics. Libraries use AI to streamline operations, enhance user experiences, and manage resources; however, mass adoption raises ethical and professional dilemmas such as bias and dis/misinformation, transparency concerning development and operations, and academic integrity. AI discourse at library conferences reveals a predominant focus on adoption over ethical scrutiny. Discussions at conferences sponsored by the Music Library Association (MLA), Teaching and Learning with AI (TLWAI), the Association of Library and Information Science Education (ALISE), and the Alabama Library Association (ALLA) highlight varying engagement levels with critical issues including bias, transparency, academic integrity, and privacy. This article emphasizes the growing tension between rapidly adopting AI tools to stay relevant and methodically assessing AI's impact prior to adoption. Theoretical perspectives from critical tech theory argue for libraries' potential to moderate the pace and trajectory of technological development through community-centric policies and ethical frameworks. Libraries are capable of championing AI ethics, whereas many other agencies struggle, by balancing innovation with a commitment to public education, patron privacy, and intellectual freedom. Library staff must improve their own AI literacy so that they are prepared to advocate for humanistic technologies. [ABSTRACT FROM AUTHOR]

The dark triad, consisting of narcissism, Machiavellianism, and psychopathy, is a robust predictor of academic misconduct. In this study, we examine if communication about academic integrity topics moderates this relationship. US students (N = 319) recruited via the CloudResearch and Prolific platforms completed an online questionnaire measuring the dark triad traits, SACCIA (Sufficiency, Accuracy, Clarity, Contextualisation, Interpersonal Adaptation) communication, and academic misconduct. Overall, the results provided initial support for the hypothesis: the more SACCIA-conforming the communication was, the weaker was the link between the dark triad and academic misconduct, albeit the interaction term did not always reach statistical significance. The findings suggest a potential avenue for reducing socially undesirable behavior among individuals high on the dark triad. [ABSTRACT FROM AUTHOR]

Despite continuous discussions on Generative Artificial Intelligence (Gen AI) ethics in academia, limited studies examine the perceptions of students or the application of ChatGPT with non-traditional students. We report an exploratory single case of a two-week orientation offered to non-traditional students entering online bachelor's degree programs. Students that participated in the orientation course completed a survey and a "short research-based reflection paper" using the pre-developed prompt discussing the relationship between academic dishonesty and workplace ethics including the ethical use of Gen AI. Based on open coding of 63 selected essays, we identified four major themes in student reflections on the ethical use of Gen AI: (1) threats to integrity and the learning process; (2) benefits and efficiency; (3) perceived inevitability of Gen AI; and (4) shared responsibility to govern the ethical use of Gen AI in academic life. The purpose of this study was to provide insights to researchers and practitioners in higher education on designing writing activities that can alleviate threats to academic integrity while promoting benefits of Gen AI into the classroom and workplace. [ABSTRACT FROM AUTHOR]

Taking the Castel National Heritage site in Israel as an empirical case study, this paper explores how experiential pedagogies at cultural heritage sites can facilitate militarization processes. The 1948 War is a pivotal moment in Israel's history and collective memory. The war has been commemorated in the Israeli landscape for decades, notably at the Castel National Heritage Site. This article provides a critical analysis of the educational strategies at the Castel site, using a multi-method ethnographic approach. Three main results are presented. First, the memorialization of the Castel battle has shifted, from a commemoration-oriented heritage site to an experiential-educational heritage site. Second, cultural heritage is used to evoke emotions among learners that, in turn, facilitate militaristic values. Third, the Castel site contributes to characterizing Israeli society as upholding prominent ethical standards in the context of a 'just war' against a continual existential threat. [ABSTRACT FROM AUTHOR]

Reporting and investigating research misconduct can lead to disciplinary proceedings being initiated, and ultimately to disciplinary sanctions being imposed on convicted scientists. The conversion of research misconduct findings into disciplinary sanctions is poorly understood. This article analyses all the disciplinary decisions handed down on appeal by the Conseil national de l'enseignement supérieur et de la recherche (CNESER) between 1991 and 2023, concerning breaches of research integrity by academics and doctoral students (n = 333). Three findings are highlighted. Firstly, the article describes how the CNESER sanctioned research misconduct even before the notion of research integrity became part of French law, by monitoring scientists' compliance with "deontological rules". Secondly, we show that assessing disciplinary fault involves evaluating a much broader set of circumstances than the mere existence of research misconduct, which can explain why the latter do not result in disciplinary sanctions or lighter sanctions. Thirdly, the research highlights situations where research misconduct is intertwined with other allegations, blurring the relative importance of these motives in the awarding of disciplinary sanctions. The article concludes with a call for greater accessibility to the disciplinary decisions handed down by universities in the first instance, as a key next step in gaining a better understanding of the disciplinary response to research misconduct. [ABSTRACT FROM AUTHOR]

As the ideological pillar of modern Japan's imperial system, the Imperial Rescript on Education was profoundly imprinted on Japan's modern education. But, in fact, multiple alternative proposals for developing modern education were put forth in the mid-Meiji period. One such proposal was the ethics (rinri) education promoted by the then Minister of Education Mori Arinori, whose conception of moral education was best embodied in the two textbooks handpicked by himself for the subject of rinri: Rinrisho, which was compiled by his team, and Fushi Dōtokugaku, which was abridged and translated from German educator Friedrich Wilhelm Fricke's work. These two texts drew criticism because they dismantled the traditional "loyalty and filial piety" framework, and such criticism reveals the logical gap between them and the Rescript. Mori's conception of ethics education as represented by these two textbooks provides valuable intellectual resources for us to visualise new possibilities for moral education. [ABSTRACT FROM AUTHOR]

This study examines the determinants of online academic dishonesty using the theory of planned behavior. We surveyed 1087 college students in Central Philippines and utilized a partial least squares‐structural equation modeling analysis to evaluate a proposed model. Results demonstrate that 10 of the 11 hypothesized relationships are statistically significant. Moreover, the paths leading to online academic dishonesty are significantly supported with a 38.6 percent coefficient of determination. Other significant findings have practical implications for theory and practice in higher education, particularly in online assessment. These insights indicate that educational leaders must recognize the need for targeted interventions to promote ethical behavior and uphold academic integrity. Educators and policymakers can use these insights to develop measures addressing online academic dishonesty. Practitioner Points: Lack of self‐control exhibits inverse variations with attitude and direct variations with subjective norms and perceived behavioral control.Justification exhibits inverse variations with attitude and direct variations with subjective norms and online academic dishonesty.The combined influence of justification and intention explains a significant portion, about 38.6 percent, of the variations of online academic dishonesty. [ABSTRACT FROM AUTHOR]

Environmental pollution, waste of resources, economic recession, political games and other problems force human beings to take the path of sustainable development. Promoting the sustainable development of education has become the mainstream recognition of all countries in the world. Based on the background of sustainable development, this paper studies the evaluation of graduate education. Firstly, the thesis is based on the evaluation of the effect of integrating red gene into graduate education. Secondly, the evaluation index system of the graduate education effect is constructed by using the expert group decision-making method, and the analytic hierarchy process is applied to determine the index weight. Finally, the paper uses the comprehensive index method to get the result of the educational effect evaluation. Through the empirical analysis, the following conclusions are drawn: (1) The evaluation index system of the graduate education effect can be carried out in five aspects: A academic discipline construction, B daily management, C party and league construction, D academic integrity, E curriculum ideology and politics. (2) The index weight of graduate student effect evaluation has typical heterogeneity. The lowest weight value is 18.16%; The highest weight is 24.25%. (3) The average score of graduate education effect evaluation of the university in the survey sample is 1.00996, which is in the interval [1.0,1.1], corresponding to the grade II, indicating a very good educational effect. [ABSTRACT FROM AUTHOR]

Modafinil is a prescription-only drug in most countries. It is mainly used to treat narcolepsy and sleep disorders, but it is also used, without a prescription, as a cognitive enhancer by ~ 10% of UK University students. Previous research has focused on the prevalence of, and motivations for, these behaviours. Here we focused specifically on determining whether students view this behaviour as cheating. We used a scenario-based approach to quantify, and qualitatively understand, student views on this topic. Most students did not view this behaviour as cheating, in part due to similarities with freely available stimulants such as caffeine, and a view that cognitive enhancement does not confer new knowledge or understanding. Although a minority of students did view it as cheating, they also expressed strong views, based in part on basic questions of fairness and access. Few students did not have a view either way. These views remained largely unchanged even when presented with considerations of other moderators of the ethics of cognitive enhancement with modafinil. [ABSTRACT FROM AUTHOR]

The rapid advancement of artificial intelligence (AI) technologies, particularly OpenAI's ChatGPT, has significantly impacted higher education institutions (HEIs), offering opportunities and challenges. While these tools enhance personalized learning and content generation, they threaten academic integrity, especially in assessment environments. This study systematically examines the impact of ChatGPT on academic integrity in HEIs, focusing on exam design, assessment strategies, AI detection tools, and policy frameworks. This research draws from current literature and expert recommendations to identify practical approaches for developing assessments that foster critical thinking and deep cognitive engagement, making them less susceptible to AI-generated content. Key areas explored include the creation of complex, analytical exam formats, deploying advanced AI detection software to counter AI-assisted cheating, and formulating institutional policies that promote the ethical use of AI. This comprehensive framework aims to equip educators and administrators with practical strategies to preserve academic standards while harnessing the potential benefits of AI, ensuring the continued validity of assessments in the AI-driven educational landscape. [ABSTRACT FROM AUTHOR]

Generative artificial intelligence (GenAI) tools are already being implemented for a variety of writing tasks in workplaces, where individual (human) authorship is valued less than the efficient production of text. But policies regarding AI use in higher education continue to prioritize academic integrity, focusing on narrowly defined notions of authorship that do not reflect the realities of workplace writing. Through an analysis of 100 university policies on AI, this article shows how AI tools create a tension for faculty in technical and professional communication who must operate within institutional or departmental policies for AI use but must also prepare writers for workplace authorship. [ABSTRACT FROM AUTHOR]

The article is an exploratory study assessing the stance selected higher education institutes (HEIs) have adopted regarding the usage of generative artificial intelligence (AI) applications in academic research. The HEIs are selected based on purposive sampling in order to showcase different stances they have adopted to curb plagiarism and uphold academic integrity. The article also takes into account the approach of some of the major publishers towards the use of generative AI technologies and the methods in which this usage is cited in academic research. Citing them as authors or in acknowledgement sections equates them to humans but without any liability of ethical conduct towards research, thus impacting academic integrity. The discussion finds its theoretical background in the eight themes of anthropology of technology. Ingold (1997) has identified in which society accepts any new technologies. The article concludes with some common questions for further research that were identified based on the common observations in the policies of HEIs, which are mostly updated on their websites. [ABSTRACT FROM AUTHOR]

Featured Application: The proposed model can contribute to the field in revealing the best performance with the least error rate in deep learning and machine learning applications. It can be useful in classifying and detecting unethical behavior patterns in online distance education exams. This study aims to identify the best deep learning and machine learning models to identify the unethical behavior patterns of learners using distance education exam data of an educational institution. One hundred twenty-nine online exam data were analyzed by the researcher with three different scenarios to reveal the best model performance in regression and classification. For regression and classification, deep neural network (DNN) from deep learning algorithms and support vector machine (SVM), decision trees (DTs), k-nearest neighbor (KNN), random forest (RF), logistic regression (LR), and extreme gradient boosting (XGBoost) algorithms from machine learning algorithms were used. In the regression analysis conducted within the scope of Scenario-1, the model we proposed to detect "cheating" behavior, which is one of the unethical learner behaviors, was found to be a 5-layer DNN model with a test performance success of 80.9%. In the binary classification analysis for Scenario-2, students who "copied" from unethical behaviors were obtained with an accuracy rate of 96.9% by the model established by the 10-layer DNN algorithm we proposed. In the triple classification analysis for Scenario-3 defined in the study, the XGBoost model was found to have the highest accuracy rate of 97.7% for students who "cheated" due to unethical behaviors and the highest performance in all other metric values. In addition, SHAP and LIME methods, which are explanatory methods for the XGBoost model, which is one of the best-performing models, were applied, and the attributes and percentages affecting the model were shared. As a result of this study, it has been shown that the application of the most appropriate layer functions and parameter selection that will increase performance can be effective in estimating complex problems and target values that cannot be solved using classical mathematical models. The proposed models can provide educational institutions with a roadmap and insight in evaluating online examination practices and ensuring academic integrity. Future researchers may need more data sets and different analyses for better performance of the established models. [ABSTRACT FROM AUTHOR]

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Academic integrity is a cornerstone of education systems, yet the rise of contract cheating poses significant challenges for higher education institutions. Current approaches to managing contract cheating often lack the comprehensive structure needed to address the complexities of modern cheating methods. The primary objective of this study is to investigate the effectiveness of the proposed Three-Tier Framework (TTF), designed in our previous study to combat contract cheating. The proposed framework comprises three tiers: awareness, monitoring, and evaluation. It engages stakeholders within the system and encourages a proactive and collaborative stance against contract cheating while reinforcing a culture of academic honesty. The evaluation focuses on three key aspects: the clarity of the framework's functions and objectives, the potential challenges in implementing the proposed monitoring process, and the perceived limitations in detecting and mitigating contract cheating through this framework. Supervised and unsupervised assignments are considered, excluding the option of e-proctoring, as some students encountered difficulties setting up necessary tools and software for online exams. Survey results reveal a broad consensus among respondents, who expressed strong confidence in the clarity and effectiveness of the framework and its monitoring procedures. These positive perceptions were consistent across respondents, regardless of their prior experience or familiarity with contract cheating. Although the overall feedback was positive, concerns were raised regarding implementing the framework in current educational settings. Specific challenges cited include tight timelines and the increased workload associated with the new procedures, emphasising a need for additional guidance, training, and institutional support to ensure effective adoption. The proposed framework incorporates an instructor dashboard designed to streamline academic workflow and simplify the monitoring process introduced in this framework. The survey results confirm that the framework can be adopted to address the unique needs of academics and diverse educational environments; however further research is needed to explore its applicability across the broader higher education community. [ABSTRACT FROM AUTHOR]

The debate surrounding the permissibility of generative artificial intelligence (AI) tools in legal education has garnered widespread attention. However, this discourse has largely oscillated between the advantages and disadvantages of generative AI usage whilst failing to fully consider how the uptake of these tools relates to the fundamental objectives of legal education. This article contributes to the current debate by positing that since the primary aim of legal education is the preparation of legal professionals and the development of legal research, generative AI must be holistically integrated into the dominant approaches to legal teaching. This stems from the fact that the legal profession will increasingly rely on generative AI in its daily work. Therefore, AI literacy will emerge as a critical professional skill in the legal realm. Against this background, this article further argues that the integration of AI into the legal curriculum should be addressed by diversifying assessment strategies, emphasizing the importance of academic integrity and making resources on the ethical use of AI available to both students and academic staff in law schools. [ABSTRACT FROM AUTHOR]

As generative artificial intelligence (GenAI) becomes more prevalent in academic circles, this article probes its ethical and practical integration in international business (IB) research. It explores the different phases of the research process where GenAI can be effectively employed, including idea generation, data analysis, and writing and communication. Notably, the article emphasizes the critical importance of upholding responsible and ethical standards when incorporating GenAI into IB research, guaranteeing that its utilization not only contributes to knowledge advancement but also conforms to the highest research integrity benchmarks. [ABSTRACT FROM AUTHOR]

This research aimed to share a personal voice, through a reflexive narrative process, of how the author's experiences as a student, design teacher and academic integrity officer influenced the conceiving and implementation of a student designed university academic integrity campaign. An auto-ethnographic methodology was utilized to share stories, expressing thoughts, feelings, and attitudes, as new knowledge for other universities seeking to engage in academic integrity initiatives. Self-reflective journaling provided key words and phrases that were identified as data and then coded using three Saldaña coding methods (Emotion, In Vivo and Initial). Three key themes emerged: empathy, education, and positive change. It is evident in the key findings that these three themes play a vital role for academic integrity awareness in enabling student engagement, deep learning, redirection of student mindsets, and second chances. These reflections shared a process for fostering behavioural change at student and university levels through academic integrity promotion. This campaign and exhibition were heralded as an example to follow for universities by the Tertiary Education Quality and Standards Agency (TEQSA) of Australia. [ABSTRACT FROM AUTHOR]

This multiple case study aimed to explore and determine the viewpoints of teachers on ChatGPT use among students. To gain a comprehensive understanding of teachers' perspectives, experiences, and attitudes regarding the incorporation of ChatGPT, an advanced natural language processing model, in the learning environments of their students. Using purposive sampling and inclusion criteria, the participating nine (9) teachers were identified. All of them participated in the in-depth interviews. Results revealed the viewpoints of the participants: absence of academic integrity, potential consequence of promoting students' laziness, contrast between traditional method and modern method of learning, may results to declining of critical thinking skills, and viewing ChatGPT as a supplementary tool. Upon reflecting to their viewpoints, they arrived at the following insights: providing performance-based activities and being responsible in using artificial intelligence. The results of this study were deemed significant by the participants, teachers, students, and researchers. [ABSTRACT FROM AUTHOR]

ChatGPT has triggered alarm throughout the higher education community since its release in November 2022. The chatbot has introduced new and complex issues around academic integrity and intensified existing concerns about misconduct practices such as contract cheating. Assessment design has been heralded as a potential solution to address evolving misconduct practices, and there is widespread belief that incorporating authenticity into assessment design is particularly effective. The necessity to deliver ‘employment ready’ graduates has also positioned authentic assessment as a preferred method for fostering the professional skills needed in complex work environments. The development of employability skills, however, is contingent on students being held to the highest academic standards in assessment practices. Promoting authenticity and academic integrity in assessment, therefore, is a continuing priority for higher education. This study employs a narrative literature review to investigate the relationship between authentic assessment and academic integrity, and 20 papers from 2019 to 2023 were chosen for review. This analysis has led to the development of a robust Model for Practice designed to guide the implementation of authentic assessment in a way that improves academic integrity, while simultaneously enhancing student engagement and employability skills, thus informing future assessment practices. [ABSTRACT FROM AUTHOR]

The emergence of generative AI in education introduces both opportunities and challenges, especially in student assessment. This paper explores the transformative influence of generative AI on assessment practices, drawing from recent training workshops conducted with educators in the Global South. It examines how AI can enrich traditional assessment approaches by fostering critical thinking, creativity, and collaboration. The paper introduces innovative frameworks, such as AI-resistant assessments and the Process-Product Assessment Approach, which emphasize evaluating not only the final product but also the student's interaction with AI tools throughout their learning journey. Additionally, it provides practical strategies for integrating AI into assessments, underscoring the ethical use and preservation of academic integrity. Addressing the complexities of AI adoption, including concerns around academic misconduct, this paper equips educators with tools to navigate the intricacies of human-AI collaboration in learning settings. Finally, it discusses the significance of institutional policies for guiding the ethical use of AI and offers recommendations for faculty development to align with the evolving educational landscape. [ABSTRACT FROM AUTHOR]

Existing evidence supports simulation for improving competency in social work education. This evidence has focused almost exclusively on simulation outcomes with little attention given to best practices for the implementation of simulation. We maintain that best practices for implementing simulation require careful attention to a host of ethical challenges. Failing to attend to these challenges not only can compromise the learning experience in simulation but also educators' and students' ability to integrate crucial social justice principles related to anti-oppressive social work practice. In this conceptual paper, we describe and employ a strengths-based, anti-oppressive, intersectional framework to competently engage in best practices for simulation pedagogy. We demonstrate how educators can utilize this framework to mitigate ethical concerns from the beginning when case scenarios are developed to the end when students are debriefed on the simulation experience. We conclude with discussing ongoing work needed in social work education to ensure educators encourage ethical social work practice by first modeling it for students in simulation exercises. [ABSTRACT FROM AUTHOR]

This study explores university educators' perspectives on their alignment with artificial intelligence (AI) ethics, considering activity theory (AT), which forms the theoretical underpinning of this study. To do so, 37 educators from a higher education institution were selected to write their metaphors about AI ethics alignment, out of which 11 attended semi-structured interviews, in which they answered some questions about their AI ethics alignment and narrated some experiences. The study reveals diverse and often contradictory perspectives on AI ethics, highlighting a general lack of awareness and inconsistent application of ethical principles. Some educators metaphorised AI ethics as fundamental but difficult to understand, while others pointed to the difficulties of regulating ethical violations. The findings highlight the need for targeted professional development on AI ethics, collaborative policy making and a multidisciplinary approach to promote ethical use of AI in higher education. This study also calls for stronger alignment between educators' personal ethical standards and institutional norms to reduce AI-related risks in educational settings. [ABSTRACT FROM AUTHOR]

The advancement and application of Artificial Intelligence (AI) has introduced innovative changes in language learning and teaching. In particular, the widespread utilization of various chatbots as foreign language learning partners showcases their remarkable potential contribution to the field. Nevertheless, there are currently few studies that encompass extensive and holistic reviews and analyses of the relevant literature during this period. The study employs bibliometric analysis and a systematic review of representative research to present trends, the current status and future directions of AI research in language teaching and learning, providing language educators, policymakers, and research scholars with visually accessible and comprehensive insights. Results indicate that the field is in its early stages of development, growing rapidly with significant research potential. The study identified the most productive and influential sources, institutions, authors and countries and provided a summary for the most representative papers in the research field. Through keyword analysis, the study delineates the evolutionary progression of AI in the domain of language teaching and learning across different time periods, identifies prevailing research trends and proposes future research directions. Results indicate that influential research in this realm predominantly focuses on refining technological solutions and conducting empirical studies on AI applications in language teaching and learning. This highlights significant interest in the effectiveness of AI in language education and its implementation methods. However, research on the application of AI in language education is still in its infancy. Therefore, the study advocates for increased empirical research on AI's specific applications in language listening, speaking, reading, and writing, as well as the development of more effective pedagogical approaches. Furthermore, the findings reveal a lack of attention given to various concerns and challenges associated with AI utilization in language teaching and learning, such as concerns regarding academic integrity, content authenticity, potential bias, privacy and security issues, and environmental concerns. At present, there is a lack of suitable solutions or regulatory frameworks proposed to address these concerns adequately. [ABSTRACT FROM AUTHOR]

AbstractCurrent AI detectors demonstrate significant technical limitations, including high false-positive and false-negative rates, and show bias against non-native English speakers. We argue that AI detection-focused approaches in writing foster an atmosphere of distrust and anxiety, while collaborative AI integration enhances learning outcomes and student engagement. Successful AI integration in writing requires clear institutional guidelines on AI and AI detection, training for educators and students, and a focus on process over product in writing assessment. By shifting from a policing mindset to a trust-based educational framework, this method aims to promote academic integrity, engagement, and responsible AI use in college writing. [ABSTRACT FROM AUTHOR]

The article presents the results of scientific research on a comprehensive analysis of the problem of compliance with the principles of academic integrity in the context of active use of artificial intelligence tools in the educational and scientific activities of Ukraine and foreign countries. The author emphasizes that the modern vector of education declares the concept of academic integrity as a prerequisite for the formation of an effective system of education and science free from academic fraud and other types of related violations. It is established that the extremely rapid development of artificial intelligence technologies significantly affects all spheres of human activity, creating unique opportunities for learning and research, but at the same time giving rise to new ethical dilemmas. An analysis of the literature on this topic shows that global issues of using artificial intelligence in education and science are the subject of study and active discussion by many domestic and foreign scholars. At the same time, the peculiarities of observing and ensuring academic integrity by students, teachers and researchers in the context of preventing violations related to the misuse of artificial intelligence technologies re main insufficiently studied. The article analyzes the process of implementation of the provisions of international acts, in particular, the Magna Carta of Universities (1988) and the Bucharest Declaration of Ethical Values and Principles of European Higher Education (2004) into the national legislation of Ukraine, as well as the impact of the Bologna Process on the reform of the educational and scientific environment of our country. It is established that Ukraine's desire to comply with the generally recognized European principles of development of the education and science system places increased demands on the implementation of the concept of academic integrity in order to reduce the risks of violations in this area. The author emphasizes the need to adopt special legislation on academic integrity as soon as possible, given the relevance and importance of the issue under study. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) holds immense potential for revolutionizing medical education and healthcare. Despite its proven benefits, the full integration of AI faces hurdles, with ethical concerns standing out as a key obstacle. Thus, educators should be equipped to address the ethical issues that arise and ensure the seamless integration and sustainability of AI-based interventions. This article presents twelve essential tips for addressing the major ethical concerns in the use of AI in medical education. These include emphasizing transparency, addressing bias, validating content, prioritizing data protection, obtaining informed consent, fostering collaboration, training educators, empowering students, regularly monitoring, establishing accountability, adhering to standard guidelines, and forming an ethics committee to address the issues that arise in the implementation of AI. By adhering to these tips, medical educators and other stakeholders can foster a responsible and ethical integration of AI in medical education, ensuring its long-term success and positive impact. Practice points: In the ever-evolving landscape of medical education, the integration of Artificial Intelligence (AI) stands out as a revolutionary innovation with the potential to reshape learning methodologies and advance healthcare practices. However, this transformative journey is impeded by ethical concerns that demand careful attention. This reflects a delicate balance that educators must strike between embracing innovation and ensuring responsible implementation. The twelve provided tips serve as a practical guide, highlighting the complexities involved in incorporating AI ethically. By following these guidelines, educators contribute to shaping a healthcare workforce that is not only technologically proficient but also ethically grounded. [ABSTRACT FROM AUTHOR]

While it is important to research the negative impact of generative artificial intelligence on academic integrity, academics should focus most of their efforts on the opportunities these technologies present for improving pedagogical practices. In this note, I attempt to flip the narrative from one of fear to one of opportunity. I suggest that academics should research the use of generative AI to improve teaching effectiveness and efficiency. I offer various practical suggestions on how these tools can be used to advance pedagogical practices, with specific business law examples. [ABSTRACT FROM AUTHOR]

Distinguishing whether some code portions were implemented by humans or generated by a tool based on artificial intelligence has become hard. However, such a classification would be important as it could point developers towards some further validation for the produced code. Additionally, it holds significant importance in security, legal contexts, and educational settings, where upholding academic integrity is of utmost importance. We present EX-CODE, a novel and explainable model that leverages the probability of the occurrence of some tokens, within a code snippet, estimated according to a language model, to distinguish human-written from AI-generated code. EX-CODE has been evaluated on a heterogeneous real-world dataset and stands out for its ability to provide human-understandable explanations of its outcomes. It achieves this by uncovering the features that for a snippet of code make it classified as human-written code (or AI-generated code). [ABSTRACT FROM AUTHOR]

The emergence of sophisticated AI tools such as ChatGPT, coupled with the transition to remote delivery of educational assessments in the COVID‐19 era, has led to increasing concerns about academic integrity and test security. Using AI tools, test takers can produce high‐quality texts effortlessly and use them to game assessments. It is thus critical to detect these nonauthentic texts to ensure test integrity. In this study, we leveraged keystroke logs—recordings of every keypress—to build machine learning (ML) detectors of nonauthentic texts in a large‐scale writing assessment. We focused on investigating the fairness of the detectors across demographic subgroups to ensure that nongenuine writing can be predicted equally well across subgroups. Results indicated that keystroke dynamics were effective in identifying nonauthentic texts. While the ML models were slightly more likely to misclassify the original responses submitted by male test takers as consisting of nonauthentic texts than those submitted by females, the effect sizes were negligible. Furthermore, balancing demographic distributions and class labels did not consistently mitigate detector bias across predictive models. Findings of this study not only provide implications for using behavioral data to address test security issues, but also highlight the importance of evaluating the fairness of predictive models in educational contexts. [ABSTRACT FROM AUTHOR]

The launch of OpenAI ChatGPT's language-generation model has raised alarms within many sectors, especially the academic sector. Several academicians have urged universities to develop new forms of assessment after the launch of ChatGPT, which solves academic questions in less than a few minutes. Academic cheating is not a new phenomenon, and the use of AI-generated text to cheat on assignments is a new type of cheating that poses unique challenges. This study used the Latent Dirichlet Allocation (LDA) method for topic modeling and the Valence Aware Dictionary for Sentiment Reasoning (VADER) method for sentiment analysis. After data preprocessing, 3870 tweets were still available out of the originally 10,000 tweets that were extracted for the study. The VADER sentiment analysis results revealed that 2013 tweets were categorized as "positive," with the remaining 804 and 1053 tweets categorized as "negative" and "neutral." The analysis's findings indicate that the majority of people have favorable things to say about ChatGPT. As a result, educational institutions can mitigate the disruptive effects of this technology and promote academic integrity by developing clear policies and guidelines and designing assessments that include limited AI-generated text. [ABSTRACT FROM AUTHOR]

This qualitative study explored how neoliberalism contributes to the fragmentation of organizational identity in Saudi universities and the implications of this fragmentation. Semi-structured interviews were conducted with 19 faculty members from a Saudi public university. Thematic analysis revealed that neoliberal policies relating to governance, funding, financial sustainability, marketization, and performance metrics have profoundly impacted Saudi higher education. The findings highlight five key themes: (1) External stakeholders increasingly influence governance and decision-making, diminishing internal voices; (2) Pressure to attract external funding diverts focus from educational mission; (3) Financial sustainability is prioritized over academic goals; (4) Market-driven values eclipse traditional academic values; (5) Performance measures are misaligned with academic priorities. The implications include compromised teaching quality, commercialization of research, loss of academic freedom, and tension between demands for global competitiveness versus local relevance. This fragmentation of organizational identity creates an incoherent institutional vision and direction. Strategies to foster a more cohesive academic environment emphasize fostering shared vision and values, promoting interdisciplinary collaboration, nurturing supportive academic culture, leveraging technology, and continuous self-reflection. The study concludes that neoliberalism has detrimentally impacted Saudi universities' organizational identity. Understanding this influence is critical for developing policies and practices that uphold academic integrity and purpose. [ABSTRACT FROM AUTHOR]

Academic misconduct remains a perennial concern in tertiary education around the globe. Research intended to explain this phenomenon has been conducted for almost 100 years. One of the most cited researchers is Donald McCabe, whose work was rooted in a survey instrument he developed in the late 1980s and distributed to 100,000 + students over the following two decades. Recognizing the need to continue to understand academic misconduct in the 21st century context, the present study updated and validated the original McCabe instrument as an inventory of academic misconduct behaviors, and derived psychometrically sound factors of the same to enable researchers to examine the predictors of these behaviors. Specifically, our updated instrument (named MIAMI: McCabe/ICAI Academic Misconduct Inventory) was shown to have construct validity through associations with the following predictors in a survey of tertiary students (n = 2329): academic integrity climate, peer norms, moral attitudes, and achievement goal structures. The updated survey will facilitate further research that will advance our understanding of academic misconduct and test the efficacy of interventions designed to promote academic integrity. [ABSTRACT FROM AUTHOR]