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This research was conducted to see if using ChatGPT prompts students to think more deeply through reflection reports. The case study method and qualitative research methodology were used to carry out this study. Five graduate students in the Curriculum and Instruction department at Aksaray University's Social Sciences Institute who were teachers in various subjects and employed at various state school levels participated in the study. It was found that the majority of participants accepted all of the information presented by ChatGPT based on a citation as true, did not feel the need to control data reliability, and could be manipulated by ChatGPT while doing self-evaluation. Additionally, despite the fact that they prepared reflective reports in which they compared their essays with ChatGPT and included questions that prompted them to think critically and reflectively, as well as the fact that they had taken a graduate-level course on the teaching of higher order thinking skills, it was acknowledged that they could not demonstrate the expected performance in using higher order thinking skills other than to a limited extent. The onus should be on educators to pioneer positive examples of how to utilize ChatGPT and provide direction on how to harness its potential, supported by critical thinking, rather than to avoid using it and identify it as a tool to be avoided.

In this paper we describe a framework for teaching students when they should, or should not use generative AI such as ChatGPT. Generative AI has created a fundamental shift in how students can complete their class assignments, and other tasks such as building resumes and creating cover letters, and we believe it is imperative that we teach students when the use of generative AI is appropriate, and when it is not appropriate (i.e., considered cheating). Framework development is based off the 2x2 Product-Market matrix introduced by Ansoff in 1965. Our initial pass at the framework was piloted with colleagues, and then followed with a focus group of students to refine the framework. We then used the framework in an MBA class to test its efficacy and gather qualitative feedback. Using the results, we further refined the framework and then used it to teach two general undergraduate business classes as a rudimentary test of generalizability across students. The qualitative results were positive. The framework helps educators understand when to use, or not use ChatGPT, and provides a way to teach students about the same. We have found that using the framework in class generates interesting discussions about the use of generative AI.

This study delves into the opportunities and challenges associated with the deployment of AI tools in the education sector. It systematically explores the potential benefits and risks inherent in utilizing these tools while specifically addressing the complexities of identifying and preventing academic dishonesty. Recognizing the ethical dimensions, the paper further outlines strategies that educational institutions can adopt to ensure the ethical and responsible use of AI tools. Emphasizing a proactive stance, the paper suggests that by implementing these strategies, schools can harness the benefits of AI tools while mitigating the risks associated with potential misuse. As the adoption of AI tools in education continues to expand, all stakeholders must stay abreast of the latest developments in the field. This knowledge equips educators to navigate the opportunities and challenges posed by AI tools, fostering a learning environment that is both secure and conducive to empowering students to realize their full potential.

This research study focuses on the growing concern of academic misconduct in distance education courses. A quasi-experimental study was conducted to measure the impact of introducing webcam recording software as an online supervision tool for high-stakes exams in two separate online courses (Management and Accounting). Results revealed that overall performance decreased following implementation of the software, and persisted after taking potential confounding factors into account. Additionally, the explanatory power of the regression analysis was higher for scores under supervision, which suggests that cheating was occurring before online supervision was introduced. It can be concluded from this study that online supervision is an effective tool to combat academic dishonesty in distance education courses.

Implementation of online education pedagogy and practice has expanded rapidly at colleges and universities in recent years, most notably in response to COVID-19. This innovative teaching/learning modality provides benefits to both faculty and students through dynamic teaching/learning content, immense flexibility, and technological investments to support teaching and learning. Academic dishonesty in higher education is a persistent concern emphasized and extensively explored in traditional face-to-face courses, less so in online learning environments. The present work, drawing on a large sample of students and faculty (n=1,640) at a Midwestern university, employs an esurvey and both qualitative and quantitative responses on cheating behavior in the emergent area of online courses/online education. Results expose significant faculty and student disagreement and uncertainty about cheating behaviors in the online environment. Academic integrity is essential to fair and equitable high-quality higher education. The stakes are high to better understand the transformative shifts in academic dishonesty occurring in the online educational environment.

Artificial intelligence (AI) first made its entry into higher education in the form of paraphrasing tools. These tools were used to take passages that were copied from sources, and through various methods, disguised the original text to avoid academic integrity violations. At first, these tools were not very good and produced nearly incomprehensible output. However, through the use of generative artificial intelligence and natural language processing, the current engines supporting these tools have become better and more efficient at producing quality output. Recently, the artificial intelligence research company, OpenAI, developed a groundbreaking artificial intelligence engine to drive a conversational chat frontend application called ChatGPT. Backed by an expansive knowledge base that could rival any university library in volume, this AI-driven conversational application can produce well-written, seemingly academic, responses to questions. This paper examines text-based artificial intelligence tools that can be used for both ethical and unethical purposes and what simple methods can be used to recognize AI-generated output whose use may indicate plagiarism.

The release of ChatGPT and other Artificial Intelligence (AI) tools has raised significant and challenging questions around the ethics of artificial intelligence in higher education. The initial concerns regarding AI in higher education centered on academic integrity, and the literature reflects this initial response. However, the multifaceted nature of higher education institutions means that there are many domains that will be ethically impacted by the application and implementation of AI. The purpose of this literature review is to examine these many domains and stakeholders in terms of ethical impact to surface questions, concerns, and concepts that may help practitioners navigate this complex construct of education, technology, and ethics. The literature identifies several implications for practice, including academic integrity, assessment (including the concept of cognicy and the practice of authentic assessment), policies and syllabi, and frameworks, principles, and tools. It concludes with a rumination on how the work of ethics is often accomplished through questions, as well as with the recognition that, at this stage in the evolution of AI, it is difficult to identify black-letter answers. We must be prepared to adapt and evolve even as AI does.

In the realm of language acquisition, the integration of Artificial Intelligence (AI) presents a promising frontier. However, gaps exist in understanding the practical application of AI-driven tools, particularly in second language learning contexts. This study delves into the usability of ChatGPT, an advanced AI language model, within the domain of second language acquisition. This paper synthesizes existing literature on ChatGPT's multifaceted capabilities, its effectiveness, its associated challenges, and the potential solutions to these challenges in language learning environments. This review demonstrates ChatGPT's substantial potential in enhancing language learning outcomes, including fostering learner autonomy, improving motivation, and developing language proficiency. Yet, nuanced challenges such as preserving academic integrity and difficulties in crafting effective prompts emerge as crucial considerations. To address these issues, possible solutions including enhancing AI literacy among learners and educators are discussed. This paper sheds light on the complex dynamics of AI-assisted language education, urging ongoing research and refinement to fully utilize these technologies in enhancing second language learning.

The rapid growth of online education, especially since the pandemic, is presenting educators with numerous challenges. Chief among these is concern about academic dishonesty, especially on unproctored online exams. Students cheating on exams is not a new phenomenon. The topic has been discussed and debated within institutions of higher learning, and significant levels of cheating have been reported in the academic literature for over sixty years. Much of this literature, however, has focused on student behavior in a classroom utilizing proctored, in-class exams. Grades on exams usually determine most of a student's final grade in a course, and GPAs are used by employers and graduate schools to indicate a student's subject matter mastery. As more conventional colleges and universities expand their online course offerings it is natural to wonder if academic dishonesty is more prevalent in online classes than in face-to-face classes. In particular, are students more likely to cheat when no one is watching (i.e., on unproctored assessment assignments) than they do when someone is watching (i.e., on proctored assessment assignments)? The purpose of this study is to investigate whether students cheat more on unproctored online exams than they do on proctored in-classroom exams, and if so, is there any pattern to their cheating behavior. Our findings are derived from careful empirical analysis of 741 undergraduate students who completed three unproctored online exams, several collaboration-encouraged assignments, and a proctored in-class comprehensive final exam in the same course with the same instructor. Additionally, we collected demographic and human capital data for every student. Using bivariate and regression analysis, we find significant evidence of more cheating on unproctored online exams than on proctored in-class exams even though students were given stern honor code violation warnings. Moreover, we discover that student cheating increased with each unproctored online exam, implying that students learn how to cheat as they become more familiar with taking online assessment assignments. Finally, we find that students with certain demographic and human capital characteristics tend to cheat more than others. This research strongly supports the use of proctoring for all evaluation assignments in online classes to ensure that grades in these classes properly reflect student aptitude as opposed to merely reflecting their ability to cheat.

This study explores the integration of Artificial Intelligence (AI) in high school education, focusing on its implications for student motivation and learning through the framework of Self-Determination Theory. As AI technologies like ChatGPT4 become more prevalent in educational settings, their potential to enhance learning by catering to students' needs for competence is significant. However, this investigation also highlights the challenges associated with AI misuse, which can undermine students' autonomy and relatedness, leading to academic dishonesty and superficial learning. The research underscores the importance of balancing technological advancements with ethical engagement and intrinsic motivation. Through qualitative analysis, including interviews and thematic analysis of student and teacher feedback, the study reveals a nuanced picture of AI's role in education. It suggests that while AI offers considerable benefits, its integration requires careful consideration of ethical use, digital literacy, and the cultivation of intrinsic motivation. The findings advocate for educational policies and practices that not only leverage AI's potential to enrich learning experiences but also address the challenges posed by its misuse. This study contributes to the ongoing discourse on technology in education, emphasizing the need for a balanced approach to AI integration that supports ethical standards and promotes a meaningful educational experience.

Artificial Intelligence (AI) and related technologies have a long history of being used in education for motivating learners and enhancing learning. However, there have also been critiques for a too uncritical and naïve implementation of AI in education (AIED) and the potential misuse of the technology. With the release of the virtual assistant ChatGPT from OpenAI, many educators and stakeholders were both amazed and horrified by the potential consequences for education. One field with a potential high impact of ChatGPT is programming education in Computer Science (CS), where creating assessments has long been a challenging task due to the vast amount of programming solutions and support on the Internet. This now appears to have been made even more challenging with ChatGPT's ability to produce both complex and seemingly novel solutions to programming questions. With the support of data collected from interactions with ChatGPT during the spring semester of 2023, this position paper investigates the potential opportunities and threats of ChatGPT for programming education, guided by the question: What could the potential consequences of ChatGPT be for programming education? This paper applies a methodological approach inspired by analytic autoethnography to investigate, experiment, and understand a novel technology through personal experiences. Through this approach, the authors have documented their interactions with ChatGPT in field diaries during the spring semester of 2023. Topics for the questions have related to content and assessment in higher education programming courses. A total of 6 field diaries, with 82 interactions (1 interaction = 1 question + 1 answer) and additional reflection notes, have been collected and analysed with thematic analysis. The study finds that there are several opportunities and threats of ChatGPT for programming education. Some are to be expected, such as that the quality of the question and the details provided highly impact the quality of the answer. However, other findings were unexpected, such as that ChatGPT appears to be "lying" in some answers and to an extent passes the Turing test, although the intelligence of ChatGPT should be questioned. The conclusion of the study is that ChatGPT have potential for a significant impact on higher education programming courses, and probably on education in general. The technology seems to facilitate both cheating and enhanced learning. What will it be? Cheating or AI-enhanced learning? This will be decided by our actions now since the technology is already here and expanding fast. [Note: The publication year (2023) shown in the citation on the PDF is incorrect. The correct publication year is 2024.]

This quantitative study is designed to help educational institutions and instructors make informed decisions regarding the use of online proctoring software. The researchers studied the impact of proctoring software in online courses by comparing the final grades of two groups of online, undergraduate students who took the same online course with the same professor who administered virtually the same content, with and without proctoring software. The overall sample included 252 students in six different undergraduate courses. When regressing all six courses together, the data did not show that the addition of proctoring software created a significantly lower course grade. The researchers then regressed the data of each individual undergraduate course, with the addition of independent variables, which had a mixed-bag of results. Interestingly, an undergraduate business course showed that the use of proctoring software reduced course grades while other courses produced interesting significant findings relative to gender and attendance status.

Higher education institutions (HEI) are increasingly challenged by plagiarism, which threatens their academic standards and integrity. This is due to the fact that students have access to an overwhelming amount of information online, making it easier for them to copy and paste without giving proper credit or attribution. Additionally, the prevalence of technology has made it easier to copy and paste without detection. The study aimed to analyze the trends in plagiarism in academic writing by using bibliometric analysis, which is a technique that can measure and analyze the use of published documents. This analysis was used to identify any potential changes in the prevalence of plagiarism in academic writing over time and to identify potential factors that could be influencing it. The data is extracted from Scopus database with Boolean and proximity search strategy and found 579 related journal articles. There are tabulations of results ranging from descriptive analysis, citation analysis, co-citation analysis, and co-word analysis. The descriptive findings revealed that 'academic integrity', despite not being included in the search string, was the most commonly occurring word with 284 indicating a significant relationship between plagiarism in academic writing in HEIs. In addition, citation analysis for the top ten documents was presented. The findings of co-citation analysis highlight factors, new trends of academic integrity, and mitigation plans against plagiarism, while co-word analysis are thematically presented in three prominent clusters which is academic misconduct study, ethics and academic integrity. In most prominent articles cited by many authors, contract cheating is the new trend of plagiarism. As a result, it sheds light on the fact that academic integrity issues go beyond a new phenomenon that needs to be taken seriously. This study has both theoretical and practical implications for minimizing future plagiarism risks as well as identifying and recommending potential solutions to the problem.

During the COVID pandemic, universities around the globe had to move not only their content delivery online, but also their assessments. Due to COVID causing significant upheaval in Higher Education (HE), this enforced experiment also afforded an opportunity to reflect on traditional, invigilated, closed book exams (ICBE) resulting in research and advice in this area. A systematic review of this academic and grey literature was performed concentrating on maths heavy physics examinations to investigate what guidance is given to examination writers, educators who prepare students for exams and HE examinees themselves. The literature review results were divided into: Advice for examiners who need to provide an uinvigilated open book exam (UOBE), discussions on cheating, advice for students and case studies. It was found that ICBEs were good at examining lower order cognitive skills, e.g. recall and understanding, but higher order skills, such as analysing and synthesising, are better examined with access to a larger range of resources. Guidance on making academic misconduct more difficult also suggested using higher order thinking skills in exam questions as responses to these types of tasks are more individual and getting outside help may be more difficult in a time constrained UOBE. Furthermore, literature encouraged reflection on the motivation for cheating and suggested that overly demanding assessment may encourage students to seek inappropriate help. The advice for students highlighted the need to prepare as thoroughly for a UOBE as they would for a traditional exam. Probably the thrust should change from pure memorization to students preparing their notes so that they can efficiently access their material to locate relevant parts for synthesis during a UOBE. Some of the case studies used statistical methods to investigate comparability of grades between UOBEs and ICBEs and some of the studies found them comparable, so a large shift of results may be due to other factors rather than the exam type. Other studies describe their approach and include stakeholder reflections. The main recommendation to exclude lower cognitive skills can pose a problem for maths heavy exams as they mainly assess how well an examinee has mastered these skills before building on them. However, it seems advisable to climb higher up Bloom's taxonomy if possible. Also, it may be conceivable to break up exams into shorter sections that require individual uploading before access to the next part is granted to reduce the possibility of outside help. Furthermore, individualised maths type problems could be achievable by using different data sets for a question. Student advice should highlight the differences between UOBEs and ICBEs so that they can prepare appropriately.

Purpose: Flexible learning is a delivery modality associated with positive outcomes, but its use at the height of the COVID-19 pandemic has been associated with dark student experiences, such as poor interest and dishonesty among students. To understand how and why many tertiary students lost their motivation and became disengaged in pandemic-era flexible learning, this qualitative research was designed. Methodology: A total of 27 tertiary students in five separate groups volunteered to be interviewed. A focus group discussion protocol was developed based on the propositions of Self-Determination Theory (SDT) and this protocol produced about eight hours of narrative data in audio form. The researcher performed thematic analysis to make sense of the transcriptions of the data, and the initial codes and themes were subjected to an external audit for purposes of methodological integrity. Findings: Eight need-thwarting behaviors of key social agents emerged, such as instructors' rigid and negligent behaviors, peers' indifferent and selfish behaviors, and burdensome behaviors of people at home. These behaviors were tied to participants' cognitive appraisals of psychological need frustration that emerged in eight themes, which comprised having problems balancing school and home obligations, believing that outputs were misevaluated, and feelings of disconnection from peers. These appraisals could be related to seven themes of motivation and engagement issues reported by participants, notably amotivation, poor concentration, low effort, and dishonesty. Significance: The findings highlight the importance of addressing these need frustrations in order to improve tertiary students' motivation and engagement in academic tasks delivered through flexible learning in higher education.

This study examines the perceptions and responses of Vietnamese teachers of English as a Foreign Language (EFL) to academic integrity concerns that arise from the use of AI, specifically chatbots like ChatGPT, in foreign language education. The study employed an open-ended survey to collect data from 31 Vietnamese EFL teachers who were asked to share their views on AI-based academic dishonesty, identify perceived causes, outline consequences for students engaging in AI-based plagiarism, and articulate their pedagogical responses to the issue. The study found that teachers primarily attributed students' AI-driven plagiarism to a deficiency in original ideas, poor learning attitudes and motivation, and students' linguistic competencies. The over-reliance on AI was identified as a hindrance to the development of knowledge and skills such as critical thinking and language proficiency. In response to academic dishonesty, teachers advocated for increased regulations, the implementation of AI-based plagiarism detectors, and education on responsible AI use. The findings underscore the importance of adapting language teaching pedagogies and assessments to incorporate personalised learning and process-oriented teaching approaches that support critical thinking and genuine learning motivation. The insights derived from this research contribute to a deeper understanding of EFL educators' perspectives, offering valuable input for the development of policies and practices aimed at promoting academic integrity in the AI era. [Note: The page range (28-47) shown on the PDF is incorrect. The correct page range is 28-48.]

Data obtained through questionnaires sometimes respond to the items presented by social norms, so sometimes they do not suit themselves. High social desirability (SD) in non-cognitive measurements will cause item bias. Several ways are used to reduce item bias, including freeing respondents from not writing their names or being anonymous, explaining to the participants to respond to each statement honestly, as they are or according to themselves, and responding to the questionnaire online or offline. This research aims to prove that several methods can minimize the possibility of item bias SD and academic dishonesty (AD). The research was carried out with an experimental study using a factorial design. There were 309 respondents who were willing to be involved in this research. Data analysis was carried out using multivariate ANOVA. The research results show differences for all variables, Self-Deceptive Enhancement (SDE), Impression Management (IM), and AD in the anonymous group. There are differences in AD in the groups that provide a complete explanation and do not explain, and there is an interaction between the average AD based on the anonymous and explanation group.

Background/purpose: The integration of ChatGPT at Ho Chi Minh City University of Technology and Education (HCMUTE) aims to transform teaching and learning dynamics. This research evaluates ChatGPT's impact on student learning behaviors, exploring its potential to enhance educational outcomes while addressing ethical concerns. Materials/methods: A mixed-methods approach was used, involving 73 first and second-year students from HCMUTE selected through stratified random sampling. Data were collected via electronic surveys and semi-structured interviews with 15 faculty members. Quantitative data were analyzed using IBM's SPSS, while qualitative data were thematically analyzed. Results: ChatGPT is widely adopted by students for learning and information searches, with many also finding it reliable for academic tasks such as idea generation and assignment completion. However, concerns about overreliance were noted, which may hinder independent thinking and critical evaluation skills. Issues related to academic dishonesty, reduced creativity, and ethical problems such as plagiarism and information security were also identified. Conclusion: ChatGPT significantly impacts student learning behaviors. Whilst it offers benefits in accessibility and efficiency, its integration requires a balanced approach in order to maximize benefits whilst mitigating risks. This study highlights the need for stringent policies to ensure ethical usage and to promote independent learning. Future research should focus on the long-term impacts of AI tools on educational outcomes and ethical implementation frameworks.

Three preregistered studies examined whether 5-year-old children cheat consistently or remain honest across multiple math tests. We observed high consistency in both honesty and cheating. All children who cheated on the first test continued cheating on subsequent tests, with shorter cheating latencies over time. In contrast, 77% of initially honest children maintained honesty despite repeated failure to complete the tests successfully. A brief integrity intervention helped initially honest children remain honest but failed to dissuade initially cheating children from cheating. These findings demonstrate that cheating emerges early and persists strongly in young children, underscoring the importance of early prevention efforts. They also suggest that bolstering honesty from the start may be more effective than attempting to remedy cheating after it has occurred.

Online and computer-assisted learning have become widespread in the rapidly evolving education landscape. However, these learning modalities uniquely challenge academic integrity, escalating the potential for academic cheating. This systematic review used thematic and narrative syntheses to examine the relationships and the effects of self-doubt and self-regulation on academic cheating in online and computer-assisted learning environments. It involved a sample of 93 peer-reviewed empirical studies published between 1998 and 2023 across five electronic databases adhering to the principles of the PRISMA framework. Findings reveal that different emotional, motivational, and cognitive factors act as primary mediators, while the individual, situational, and environmental factors were significant moderators. These findings underscore the context-dependent and inconsistent effects of self-doubt and self-regulation on academic cheating. Notably, while self-doubt and self-regulation exert opposing influences on academic cheating, other factors, such as gender, culture, performance, feedback, peer pressure, and proctoring, significantly modify these effects. Hence, the relationship between self-doubt, self-regulation, and academic cheating in online and computer-aided learning is thus intricate and dynamic, depending upon various individual, situational, and contextual elements that shape students' motivation, emotions, and cognition. Therefore, this study contributes to the broader discourse on online and computer-aided learning by offering strategies to prevent and reduce academic cheating. Recommendations include promoting self-regulation, reducing self-doubt, focusing on specific mediators and moderators, and utilizing different resources and techniques to measure and identify academic cheating. The results underline the importance of a concerted, multi-faceted approach to upholding academic integrity in the era of digital learning.

The popularity of Internet usage and the diverse and rich information available online make it easier for students to engage in academic misconduct, especially Internet plagiarism (IP). Therefore, this study investigated the IP intention of Chinese students and analyzed the influence of gender, educational level, achievement goals (AG), low self-control, acceptance, and plagiarism experience on it. A total of 551 students from high school to university participated in this study. It was found that male students had more IP experience than female students, and college students had more IP experience and higher IP intentions than high school students. Correlation analysis showed that all variables in this study were related to each other. Regression analysis showed that AG, low self-control, plagiarism acceptance, and past experience jointly predicted IP intention with a high level of predictive explanatory power (64%). Specifically, students with a dominant mastery approach had lower IP intentions, whereas students with a more impulsive personality, more acceptance of IP, and more IP experience had higher IP intentions. Considering these findings, this article puts forward suggestions to reduce IP.

High-stakes examinations enjoy widespread use as summative assessments in higher education. We review the arguments for and against their use, across seven common themes: memory recall and knowledge retention; student motivation and learning; authenticity and real-world relevance; validity and reliability; academic misconduct and contract cheating; stress, anxiety and wellbeing; and fairness and equity. For each theme, we evaluate empirical evidence for the perceived pedagogical benefits and pedagogical drawbacks of high-stakes examinations. We find that relatively few of the perceived academic benefits of high-stakes examinations have a strong evidence base. Support for their use is largely rooted in opinion and pragmatism, rather than being justified by scientific evidence or pedagogical merit. By contrast, there is substantial evidence for pedagogical drawbacks of high-stakes summative examinations. We conclude that the current heavy reliance on high-stakes final examinations in many university subjects is poorly justified by the balance of empirical evidence.

This study presents a corpus analysis of academic integrity policies from Higher Education Institutions (HEIs) worldwide, exploring how they address the issues posed by technological threats, such as Automated Paraphrasing Tools and generative-artificial intelligence tools, such as ChatGPT. The analysis of 142 policies conducted in November and December 2022, and May 2023 reveals a gap regarding the mention of AI and associated technologies in the available academic integrity policies. Despite the growing prevalence of these tools in the 6-month period since the release of ChatGPT, no HEIs had produced revised academic integrity policies. Content analysis of 53 guidance documents produced by HEIs suggests an overall positive focus of Gen AI tools, yet advises caution. This study suggests a modification to Bretag et al.'s (Int J Educ Integr 7, 2011) exemplary academic integrity model, introducing "Technological Explicitness" -- emphasizing the need to include explicit guidelines about new technologies in academic integrity policies. These results underscore the urgent need for HEIs to revise their academic integrity policies, considering the evolving landscape of AI and its implications for academic integrity. This paper argues for a multifaceted approach to deal with the issues of integrating technology, education, policy reform, and assessment restructuring to navigate these challenges while upholding academic integrity.

The current study aimed to explore effective strategies for preventing cheating in online examinations by surveying students to determine their cheating strategies. A total of 406 Iranian students at BA, MA, and PhD levels in four programs, including English language teaching, English literature, Linguistics, and English language translation, participated in this study using a convenient sampling technique. The sample was drawn from 83 universities across all 31 provinces of Iran. The researchers developed a 30-item questionnaire and a 4-item interview to collect data. The results revealed that searching in PDFs and using social media groups were the most commonly used cheating strategies. To prevent this form of academic dishonesty, teachers used various strategies, the most effective being the use of conceptual questions and one-by-one oral exams with turned-on webcams. The main reason for students to cheat was their desire to perform better. The implications of this study are relevant for educational stakeholders, particularly teachers, and students, in promoting academic integrity.

This study conducted on a sample of 295 Dutch and Italian undergraduate and graduate students aims to investigate how psychopathic personality traits (meanness, boldness and disinhibition) may lead to cheating behavior, and to study whether there are correlations between psychopathic traits, motivation, moral disengagement, the perception of seriousness of academic dishonesty and frequency of academic dishonesty to try to better understand what causes students to cheat and engage in dishonest conduct. Results confirmed the key role of psychopathic traits, particularly the disinhibition aspect in predicting academic dishonesty. In addition, it was shown that students' perceptions of what constitutes academic dishonesty and what does not are also important in predicting the frequency of dishonest behavior. Furthermore, the role of motivation and moral disengagement in predicting and mediating the relationship between traits of psychopathy and academic dishonesty were analyzed through mediation and regression analysis.

Research on improving engineering skills in students advocates for high-quality teaching practices as well as the implementation of digitally enhanced management systems, such as e-Learning. Furthermore, COVID-19 led to several changes in education, such as switching drastically from face to face to emergency remote and later hybrid teaching. This study has focused on the proposal, the application and the evaluation of a new e-Learning teaching method. "FastTest PlugIn" has been used for the creation of large sets of questions, to develop parameterised and individualised exercises in Moodle. The method has been applied since COVID-19, considering different types of teaching: completely remote, hybrid and face to face. It has been implemented in some courses of the bachelor's and master's degree in engineering at the University of Cadiz (Spain). The academic performance and students' feedback on the method have been obtained. During remote teaching, students presented lower scores than before the pandemic. However, as students became familiar with the method and at least face-to-face exams were carried out, the scores and students' perception of the difficulty of the courses improved. The main novelty of this method is the reliability of creating different exercises for a consistent level of difficulty; the ability to avoid dishonest actions of students; and the time saved by instructors, as exercises are automatically corrected. This study provides the advantages and benefits of digitally enhancing university teaching. Furthermore, it is the first to investigate the integration of "FastTest PlugIn" to enhance teaching quality and engineering skills.

Online teaching has gained more momentum since the outbreak of the COVID-19 pandemic. While this mode offers many benefits, one major concern is maintaining academic integrity, as online instruction can provide more opportunities for cheating. This study aimed to explore students' attitudes toward cheating in online assessments (OAs) and any potential differences based on gender and nationality. Since our purpose was to perform a cross-cultural examination of cheating behaviors in an academic environment, we conducted the study in culturally diverse countries. The participants were 629 university students from Iran, Romania, and Lebanon. They completed a questionnaire about academic integrity in OAs. The results showed that 60% of the participants had no negative views on cheating in OAs, 58.5% admitted to cheating in OAs themselves, and 85% viewed OAs as less reliable than in-person assessments. During OAs, the most common ways of cheating included using notes on paper, relying on course materials, and sharing answers through social media and messaging apps. The main motivations for cheating included stress, time constraints, and the desire to achieve a higher grade, while factors that deterred cheating included moral and social stigma and the rights of other students. The study found no significant difference in attitudes toward cheating in OAs between male and female participants, but there were significant differences between students of different nationalities (p < 0.05).

Cheating in online exams without face-to-face proctoring has been a general concern for academic instructors during the crisis caused by COVID-19. The main goal of this work is to evaluate the cost of these dishonest practices by comparing the academic performance of webcam-proctored students and their unproctored peers in an online gradable test. With this aim in mind, we carried out a randomized field experiment using a simple video surveillance system through Google Meet during an online closed-book final exam of an Introduction to Microeconomics course. Given that all conditions except for webcam monitoring were identical, differences in between-subjects scores are attributed to academic dishonesty. After controlling for potential confounding factors, including gender, academic degree, instructor, previous score and whether students were repeaters or not, we found that those students who were proctored via webcam obtained statistically significant lower scores in the final exam than those who were not using this surveillance system with a low level of invasiveness. Inspection of the potential factors behind these differences in scores suggests that the poorer performance of proctored students is more related to academic dishonesty than to reasons involving anxiety or heterogeneity factors.

The proliferation of artificial intelligence (AI) technology has brought both innovative opportunities and unprecedented challenges to the education sector. Although AI makes education more accessible and efficient, the intentional misuse of AI chatbots in facilitating academic cheating has become a growing concern. By using the indirect questioning technique via a list experiment to minimize social desirability bias, this research contributes to the ongoing dialog on academic integrity in the era of AI. Our findings reveal that students conceal AI-powered academic cheating behaviors when directly questioned, as the prevalence of cheaters observed via list experiments is almost threefold the prevalence of cheaters observed via the basic direct questioning approach. Interestingly, our subsample analysis shows that AI-powered academic cheating behaviors differ significantly across genders and grades, as higher-grade female students are more likely to cheat than newly enrolled female students. Conversely, male students consistently engage in academic cheating throughout all grades. Furthermore, we discuss potential reasons for the heterogeneous effects in academic cheating behavior among students such as gender disparity, academic-related pressure, and peer effects. Implications are also suggested for educational institutions to promote innovative approaches that harness the benefits of AI technologies while safeguarding academic integrity.

In various fields, including college admission, medical board certifications, and military recruitment, high-stakes decisions are frequently made based on scores obtained from large-scale assessments. These decisions necessitate precise and reliable scores that enable valid inferences to be drawn about test-takers. However, the ability of such tests to provide reliable, accurate inference on a test-taker's performance could be jeopardized by aberrant test-taking practices, for instance, practicing real items prior to the test. As a result, it is crucial for administrators of such assessments to develop strategies that detect potential aberrant test-takers after data collection. The aim of this study is to explore the implementation of machine learning methods in combination with multimodal data fusion strategies that integrate bio-information technology, such as eye-tracking, and psychometric measures, including response times and item responses, to detect aberrant test-taking behaviors in technology-assisted remote testing settings.

The pervasive issue of cheating in educational tests has emerged as a paramount concern within the realm of education, prompting scholars to explore diverse methodologies for identifying potential transgressors. While machine learning models have been extensively investigated for this purpose, the untapped potential of TabNet, an intricate deep neural network model, remains uncharted territory. Within this study, a comprehensive evaluation and comparison of 12 base models (naive Bayes, linear discriminant analysis, Gaussian process, support vector machine, decision tree, random forest, Extreme Gradient Boosting (XGBoost), AdaBoost, logistic regression, k-nearest neighbors, multilayer perceptron, and TabNet) was undertaken to scrutinize their predictive capabilities. The area under the receiver operating characteristic curve (AUC) was employed as the performance metric for evaluation. Impressively, the findings underscored the supremacy of TabNet (AUC = 0.85) over its counterparts, signifying the profound aptitude of deep neural network models in tackling tabular tasks, such as the detection of academic dishonesty. Encouraged by these outcomes, we proceeded to synergistically amalgamate the two most efficacious models, TabNet (AUC = 0.85) and AdaBoost (AUC = 0.81), resulting in the creation of an ensemble model christened TabNet-AdaBoost (AUC = 0.92). The emergence of this novel hybrid approach exhibited considerable potential in research endeavors within this domain. Importantly, our investigation has unveiled fresh insights into the utilization of deep neural network models for the purpose of identifying cheating in educational tests.

The Fraud Diamond theory (rationalization, opportunity, motivation and perceived capability) has been widely used as a framework to explain criminal behavior. However, little is known about its application in an academic context. Research on the relationship between the prevalence of academic fraud (e.g., cheating on exams, plagiarizing essays) and students' perceptions of the severity of such behaviors is also lacking. We surveyed 1,032 university students and asked them whether they engaged in fraudulent behaviors and how severe they thought these behaviors were. The results showed that motivation, rationalization, and perceived capability dimensions of the Fraud Diamond theory are important factors in predicting the prevalence of students' academic fraudulent behavior, but not opportunity. We also found that the more students reported that they engaged in fraudulent academic behaviors, the less severe they perceived those behaviors to be. However, only rationalization emerged as a predictor of perceived severity. The results suggest that the more students are able to find justifications for their fraudulent behavior, the less severe they perceive those behaviors to be. Rationalization seems to explain, therefore, the tendency to disassociate moral values from dishonest behavior. Taken together, the study shows a vicious cycle between engaging in fraudulent academic behavior and the ability to justify these acts. Implications for Higher Education Institutions (HEIs) are discussed.

Academic misconduct is a growing problem in online education. While there are ways to curb academic misconduct in online exams, utilization of technology to proctor online exams in a simple manner in limited-resource settings remain unclear. This study set out to identify a reliable technique for utilizing webcam footage to identify instances of academic dishonesty. Instead of applying component-based feature extraction, this study provides a deep learning-based approach to online exam proctoring that recognizes exam candidates' behaviors directly from video input. By using an online exam dataset consisting of twenty-four test-takers who replicate real-world actions, experimental results show the efficiency of the approach. The article provides a collection of webcam recordings with annotated actions to evaluate the proposed approach. The findings show that a deep learning model using the Slow Only variation has a true detection rate of 78.9%. The visualization module reduces the amount of time invigilators must spend watching videos to capture academic misbehaviors by offering a comprehensive graphical view with details of a candidate's actions during an exam. The study's conclusions will help design an effective and efficient system for online exam proctoring in resource-constrained environments.

Academic integrity is part of the process that explains the communication of information in an ethical manner. Although the prevalence of dishonest acts at university has been noted, it is an aim of the educational system to analyse what motivates them from an age prior to their incorporation into university studies. The aim of this work is to collect the visual-narrative representation of academic plagiarism made by secondary school and university students, as well as to analyse their perception of it and discover the keys that explain this malpractice. A participatory study was carried out, in which two high school students took on the role of co-researchers in training. Information is collected from 178 students from three schools in Spain and Portugal through participatory photography or Photovoice. It is analysed with Maxqda22 software in two stages: (1) deductive analysis of the narratives, identifying categories and thematic codes, in a participatory way with the trainee researchers; and (2) inductive analysis of images and metaphorical expressions. The results allow us to outline three representations of the action of copying: as a punishable act, as a picaresque act and as a quick and easy opportunity for the student. This classification reveals the issues that dominate the discourse of the participants, suggesting the effects and causes that aggravate the commission of plagiarism: the simplicity of the process and the possibility of not being detected.

Automatic grading models are valued for the time and effort saved during the instruction of large student bodies. Especially with the increasing digitization of education and interest in large-scale standardized testing, the popularity of automatic grading has risen to the point where commercial solutions are widely available and used. However, for short answer formats, automatic grading is challenging due to natural language ambiguity and versatility. While automatic short answer grading models are beginning to compare to human performance on some datasets, their robustness, especially to adversarially manipulated data, is questionable. Exploitable vulnerabilities in grading models can have far-reaching consequences ranging from cheating students receiving undeserved credit to undermining automatic grading altogether--even when most predictions are valid. In this paper, we devise a black-box adversarial attack tailored to the educational short answer grading scenario to investigate the grading models' robustness. In our attack, we insert adjectives and adverbs into natural places of incorrect student answers, fooling the model into predicting them as correct. We observed a loss of prediction accuracy between 10 and 22 percentage points using the state-of-the-art models BERT and T5. While our attack made answers appear less natural to humans in our experiments, it did not significantly increase the graders' suspicions of cheating. Based on our experiments, we provide recommendations for utilizing automatic grading systems more safely in practice.

I argue that wrong of plagiarism does not primarily stem from the plagiarist's illicit misappropriation of academic credit from the person she plagiarized. Instead, plagiarism is wrongful to the degree to which it runs counter to the purpose of academic work. Given that this is to increase knowledge and further understanding plagiarism will be wrongful to the extent that it impedes the achievement of these ends. This account of the wrong of plagiarism has two surprising (and related) implications. First, it follows from this account of the wrong of plagiarism that replication plagiarism might not be an academic wrong at all. (Replication plagiarism consists of the direct quotation or paraphrase of another's work without attribution. The replication plagiarist thus plagiarizes primary sources, purloining for her own benefit the ideas of their authors). Second, even if replication plagiarism is still held to be an academic wrong, it will be a lesser wrong than bypass plagiarism. (Bypass plagiarism occurs when one quotes from, or provides a paraphrase of, a primary source, but although one cites the primary source one did not identify the quotation or provide the initial paraphrase oneself. Instead, one took the quotation, or drew upon an existing paraphrase, from a secondary source--and one did so without citing the secondary source to credit it as the source of one's information about the primary source). Holding that bypass plagiarism is worse than replication plagiarizes reverses the usual assessment of the relative wrong of these two types of plagiarism.

In this study, the levels of knowledge, awareness, and acceptance of research misconduct were investigated among the Pharmacy academics and students in Malaysia. A cross-sectional study using an online questionnaire was carried out. A total of 393 pharmacy academics and students in Malaysia were involved. Perceived research misconduct, as defined in this study as the perception of any research misconduct performed or observed by the respondents at their institution, was captured and further analyzed. The data was analysed using PLS-SEM to assess the hypotheses which were formulated prior to the survey. The findings show that there is a statistically significant positive relationship between the awareness of terminologies regarding research misconduct and perceived research misconduct in the workplace of respondents. However, the acceptance of unethical practices in research demonstrates a negative correlation with perceived research misconduct. Knowledge and awareness regarding research misconduct have no statistically significant relationship with perceived research misconduct in this study. Both awareness of terminologies and acceptance of unethical practices explained a 10.8% variance in perceived research misconduct. Therefore, our study indicates that the awareness, knowledge, and acceptance of research misconduct might not be the main predictors of questionable conduct of research among pharmacy academics and students. Future study on the relationship between other factors which might contribute to research misconduct is highly recommended to investigate the significant contributing factors of irresponsible conduct of research among the Pharmacy academics and students in Malaysia.

The problem of academic dishonesty in general and exam cheating in particular, has been ubiquitous in schools, colleges, and universities around the world. This paper reports on the findings from teachers' and students' experiences and perceptions of exam cheating at Nepali schools, colleges, and universities. In so doing, the paper highlights the challenges of maintaining academic integrity in Nepali education systems. Based on qualitative research design, the study data were collected by employing semi-structured interviews with the teachers and the students. Findings from the study indicated that over-emphasized value given to marks/grades and the nature of exam questions among others were the predominant factors. Our findings contribute to the practical understanding that academic institutions in Nepal have largely failed to communicate the value of academic honesty and integrity to the students of all levels of education despite the increasing prevalence of exam cheating. Therefore, exam cheating requires urgent attention from academic institutions, educators, and education leaders to educate students about the long-term educational and social values of academic honesty and integrity.

Cheating in higher education is a significant problem. The study aims to determine nursing students' attitudes and opinions toward cheating in exams. The type of research is descriptive. The research data were collected in the classroom environment of 716 students in day and evening education programs. The research data were collected using socio-demographic characteristics form, and the Copying Attitude Scale. Descriptive statistics, t-tests, and variance analysis were used for data analyses. The most common method of cheating was receiving answers by talking to the friend sitting next to the student. The rate of cheating was high in both the day and evening education students. The students reported that most of the cheating occurred in final exams and high-credit major courses. Learning type (day/evening education programs) is not effective in cheating. High-credit major courses have increased the fear of failing the course. The most important reason for cheating in exams was the fear of failing a course.

Academic misconduct is a threat to the validity and reliability of online examinations, and media reports suggest that misconduct spiked dramatically in higher education during the emergency shift to online exams caused by the COVID-19 pandemic. This study reviewed survey research to determine how common it is for university students to admit cheating in online exams, and how and why they do it. We also assessed whether these self-reports of cheating increased during the COVID-19 pandemic, along with an evaluation of the quality of the research evidence which addressed these questions. 25 samples were identified from 19 Studies, including 4672 participants, going back to 2012. Online exam cheating was self-reported by a substantial minority (44.7%) of students in total. Pre-COVID this was 29.9%, but during COVID cheating jumped to 54.7%, although these samples were more heterogenous. Individual cheating was more common than group cheating, and the most common reason students reported for cheating was simply that there was an opportunity to do so. Remote proctoring appeared to reduce the occurrence of cheating, although data were limited. However there were a number of methodological features which reduce confidence in the accuracy of all these findings. Most samples were collected using designs which makes it likely that online exam cheating is under-reported, for example using convenience sampling, a modest sample size and insufficient information to calculate response rate. No studies considered whether samples were representative of their population. Future approaches to online exams should consider how the basic validity of examinations can be maintained, considering the substantial numbers of students who appear to be willing to admit engaging in misconduct. Future research on academic misconduct would benefit from using large representative samples, guaranteeing participants anonymity.

Blended classroom assessment (CA) has become commonplace in the post-pandemic era, offering advantages but also presenting challenges for English as a Foreign Language (EFL) teachers. These challenges can impact teachers' emotional experiences, distinct from face-to-face or purely online environments. However, scant research exists on how EFL teachers employ emotion regulation (ER) strategies in blended CA within EFL contexts. This qualitative study examines how eight EFL teachers at a university in southwestern China employed ER strategies in blended CA. Drawing on Gross's ER theory, the study identifies ten intrinsic and extrinsic ER strategies, either antecedent-focused or response-focused, utilized by EFL teachers. These strategies helped teachers maintain resilience amidst challenges posed by blended CA, including low completion rates of online learning tasks prior to face-to-face instruction, interaction in blended CA, blended assessment design, managing the workload of blending online and face-to-face assessment, and addressing issues of plagiarism and cheating. The study enriches our understanding of EFL teachers' emotions in blended CA and underscores the significance of ER competence in assessment literacy. Implications for equipping teachers with strategies to enhance their emotional well-being and resilience in blended CA are also discussed.

Accountability-era reforms have promulgated a discourse of crisis and a looming threat for public schools. While these threats negatively affect all, this paper focuses on how schools' responses to these threats shape novice Teach for America Corp Members' (CM) socialization. By analyzing nearly 6,000 daily reflections from 38 CMs about their experiences during a high-stakes cheating scandal, we argue that schools' responses indicated a threat rigidity response that allowed what we call the mesosystem of transgressions to occur. Our implications present ways to (a) mitigate threats inflicted on schools through unjust policies and practices and (b) enact equitable public schools.

As elsewhere in the world, academic misconduct is a serious problem in Aotearoa. Yet, beyond the occasional newspaper headline, we know relatively little about the extent of the problem here or the factors associated with it. Consequently, our educational leaders and practitioners are left under-informed as they seek to address the problem and promote academic integrity. To help provide the knowledge and insights needed to craft good policy and best practice, the Research on Academic Integrity in New Zealand (RAINZ) Project--a research collaboration involving eight tertiary institutions--was founded in 2021. In the second semester of 2022, the RAINZ Project launched the first-ever nationwide survey of undergraduate students' perceptions, attitudes, and behaviours related to academic integrity. Results from this survey, which was completed by undergraduates (N = 4493), indicate that most students (approximately two-thirds) reported engaging in at least one form of academic misconduct in the past year. As hypothesised, students' perceptions (of the institutional climate and peer norms) and moral attitudes (related to cheating) were significantly associated with their engagement in academic misconduct. Details of these results as well as their implications for policy and practice are discussed.

This study examines the academic dishonesty tendencies, created metaphors and opinions on the "ethics" and "science and research ethics" and "opinions on the taken course" of prospective teachers who took the science and research ethics course. Embedded mixed design was preferred. Academic dishonesty tendency scale, semi-structured interview form and metaphor form were used as data collection tools. With the two-stage clustering performed, the average scores of the academic dishonesty tendency scale were divided into three clusters. It was found that the majority of prospective teachers have a low level of academic dishonesty tendency. According to the results obtained from semi-structured interviews, teacher candidates have expressed their views on the concept of "ethics" under the categories of instructor, student, and administrator. Their views on the concept of "science and research ethics" have been explained under the categories of determination of the subject, conducting studies, and publication of studies. It has been revealed that the course on science and research ethics contributes to the educational processes, personal development, and professional development of teacher candidates. Different metaphors were developed for the concepts of ethics, and science and research ethics. In addition, the metaphors developed regarding the concept of ethics were generally related to guiding and mentoring. The metaphors developed regarding the concept of science and research ethics are related to the things to be considered in a scientific study process, the way to be followed in scientific studies, and the importance of ethics in scientific studies. [Note: The page range (23-46) shown on the PDF is incorrect. The correct page range is 23-48.]

Academic dishonesty is a major issue in education. Perhaps more so in the online environment where may times students are on their honor to complete exams without the use of the Internet, notes, or other prohibited materials. The age range of 18-24 encompasses the traditional aged student body. The non-traditional students are over the age of 25 and may be married, have children, full time jobs and other obligations. Age and the concomitant responsibilities associated with married life, children and working full time work may have advanced the maturity of these students and may allow them to look at the bigger picture and become less tolerant on issues of academic dishonesty. An analysis of the grade records in business classes at a mid-Atlantic comprehensive university provided the data for this study. All class were online and covered the period from 2017-2021. A total of 455 participants (134 traditional aged students and 321 non-traditional aged students) took either two or three exams each, equating to a total of 1227 test scores being analyzed. To determine if cheating was occurring faculty began utilizing the capabilities of the Respondus Lockdown Browser® which records both sound and video while the student is taking the exam. The results were surprising. Implications for practice are also included.

This research utilizes a unique, validated, multiple-choice exam design that allows researchers to observe and measure the degree to which students copy answers from their peers. Using data collected from the exam, this study investigates whether asking students to sign an honor code at the start of the exam reduced instances of cheating relative to a control group. Empirical results demonstrate that the classroom of students who signed the honor code had less overall copying of answers and also fewer individual students who engaged in cheating. This study contributes to the literature by conducting analysis on a sample that measures actual cheating frequency, as opposed to relying on reported cheating in surveys, and by demonstrating that a positive intervention can effectively discourage unethical behavior.

Letter grading systems in education have been widely accepted as a strong medium to assess the educational performance of students across the world. It has been a successful system for many years because it can motivate students to achieve satisfactory grades in a course. However, recent studies indicate that grades can also foster anxiety and demotivate students to learn. This fact can be challenging because many investigators claim that motivation and student engagement are key to student success, especially in online education. This situation is even more problematic with the recent outbreak of COVID-19. There has been a major shift in the education sector from face-to- face instruction to online instruction. To mitigate the negative effects of the grading system, incorporating a partial student self-grading model in remote learning is proposed. The partial self- grading student model can potentially increase motivation, positive attitude and decrease the temptation to cheat on assignments. Another benefit of this system is that it can create a platform where students and instructors can discuss the grades given, thus creating a two-way learning system instead of a one-way learning system. The powerful impacts and benefits of the partial student self-grading model in online education are explored and discussed.

Scientific and technological advancements over the last three decades have failed to reduce the widespread prevalence of academic dishonesty in higher education, in large part because institutional barriers prevent faculty from adopting existing tools to curb cheating. We conducted a systematic literature review of research on cheating and found that the majority of studies propose new tools without advancing theory or even utilizing existing theory. Although some studies note the systemic nature of academic misconduct, the academic integrity literature requires a robust theoretical framework to model its systemic nature and derive practical strategies. Building on theory from several domains, we propose a systemic model of academic (mis)conduct which predicts group-level effects on students and offers practical guidance for faculty overcoming institutional barriers to curb cheating. We leverage game theory for useful models of systemic, group-level phenomena in this context, and we leverage education reform literature for insights on how to support instructors' adoption of new tools. Our model, the spectrum of academic conduct, identifies trust as a single dimension governing both cheating behaviors and productive learning behaviors. Integrating insights from pedagogy, conflict management, and organizational psychology, we discuss multiple practical strategies to lower students' opportunity, motivation, and rationalization to cheat.