Scientific research in the field of legal studies intersects with numerous other disciplines—such as commerce, medicine, economics, media, politics, and others. These fields are governed by legal rules, such as commercial law, medical law, and economic relations law, among others, which demonstrates that no field exists outside a regulatory legal framework. Therefore, a researcher must recognize that the quality of their legal research is rooted in the depth of their knowledge of the subject matter with which it intersects. Researchers must also understand that the validity of previous research findings is not absolute; acknowledging their relativity expands the researcher’s intellectual horizons. Likewise, researchers should not venerate other scholars, as doing so undermines the nature and quality of their scientific work when they merely repeat previously reached conclusions. The importance of research lies in the researcher’s awareness of the significance of the scholarly pen in analyzing and understanding any issue within the field of legal sciences. The pen is a tool that stimulates the mind to comprehend the philosophy underlying legal rules, both before and after their formulation, from multiple perspectives, for no legal issue admits an absolute understanding—the differences arise from varying intellectual viewpoints. This means that researchers should not sanctify others’ ideas, and that those who admire the works and ideas of others must, at the very least, avoid committing academic plagiarism, as it contradicts the ethics of scientific research established in international and national legislation. This study employs both analytical and descriptive methods to clarify the relationship between intellect and writing in legal philosophy and to elucidate the issue of academic plagiarism.

This qualitative-phenomenological study aims to explore how pre-service BSEd-English students understand academic dishonesty in English writing. It looks to their level of awareness on academic dishonesty and its forms in English writing, their perception on academic dishonesty and its forms, the factors affecting their awareness and perceptions, and the strategies to promote academic integrity and prevent academic dishonesty. A total of 21 participants participated in focus-group discussions. The findings of the study were coded, analyzed, and put into themes using Clark-Braun Thematic Analysis. It was found that pre-service BSEd-English students are aware of the existence of academic dishonesty and its forms in English writing. The common forms identified are plagiarism, improper citation, collusion, contract cheating, unsupported claims and exaggerated results, and self-paraphrasing. While there is awareness on academic dishonesty, the participants perceive it is wrong, serious, deceitful, and both half wrong and half useful. These awareness and perceptions are influenced by factors concerning pressure to succeed and to meet standards, fear of being criticized, laziness, and normalization of engaging to academic dishonesty acts. With these results, it is recommended to develop a comprehensive educational program that goes beyond simply defining academic dishonesty. Teachers must be a model of integrity, especially to their students. Also, clear and consistently enforced policies on academic dishonesty must be implemented. Finally, learning avenues and opportunities must be created to train students to write with integrity.

ChatGPT offers second language writers’ limitless opportunities for engagement with this technology. This exploratory study focuses on Gen-AI academic plagiarism in the context of unsupervised written assessment and pursues the concept of opportunity in traditional academic fraud theory, in an attempt to evaluate its applicability to Gen-AI academic plagiarism. The research questions aimed to gather perceptual and textual observations by departmental faculty regarding their students’ unpermitted use of ChatGPT in written assessments. Thirteen experienced faculty members in the English and Translation Department at a publicly funded university in the Sultanate of Oman completed a questionnaire asking them to identify clues of plagiarism evident in their students’ written work. Additionally, assessment artefacts in the form of 15 student-authored literature reviews were examined in search of these clues. Using an inductive, mixed-methods approach, the analysis drew on faculty members’ growing understanding of the affordances of large language models, coupled with their situated knowledge of their students’ writing abilities in terms of the lexico-grammatical and discoursal features characterising their submitted texts. The findings were summarized in a model which highlighted the interrelationships amongst the various factors leading to writer disengagement principally manifested through language, subject-matter, and behavioural clues. The paper concludes by adopting a utilitarian, pragmatic perspective on academic plagiarism, with a view to transforming these limitations into opportunities for writer engagement and, ultimately, learning.

The rapid adoption of Generative Artificial Intelligence (GAI) in higher education has introduced new ethical challenges, particularly concerning students' academic misconduct. While prior research has linked personality traits to unethical behavior, little is known about how different combinations of personality traits shape students' misuse of GAI. This study integrates the HEXACO model and the Dark Triad framework to examine the configurational effects of personality on GAI-related academic misconduct. A total of 864 university students completed questionnaires. Using fuzzy-set Qualitative Comparative Analysis, we identified multiple configurations leading to both high and low levels of GAI misconduct. No single trait is sufficient to explain GAI-related academic misconduct. Rather, high misconduct consistently emerged from configurations characterized by low Honesty-Humility and Conscientiousness combined with high Machiavellianism or Psychopathy. In contrast, low misconduct was associated with configurations combining high Honesty-Humility, Conscientiousness, and Agreeableness with low levels of Dark Triad traits. This study demonstrates that personality traits interact synergistically rather than independently to shape individuals' ethical or unethical engagement with AI technologies. Moral restraint is sustained by both the presence of virtues and the absence of exploitative tendencies. The findings support the idea that moral integrity and self-regulation constitute foundational safeguards against unethical use of technology. These findings align with self-regulatory theories of academic dishonesty, reinforcing that individuals high in honesty and conscientiousness are less likely to rationalize or justify academic misconduct even when new technological affordances make it easier. The study therefore advances theoretical understanding by integrating personality frameworks within a configurational paradigm and offers practical insights for developing personality-informed ethics education.

As artificial intelligence (AI) becomes embedded in academic workflows, concerns are increasing about researchers developing psychological and behavioral dependence on AI systems. Although AI tools can significantly enhance research efficiency and analytical capabilities, excessive or uncritical reliance may lead to cognitive disengagement, emotional over attachment, and maladaptive academic practices. Yet, the literature still lacks a validated instrument that can systematically assess AI dependency within scholarly contexts. This study addresses this gap by developing a multidimensional scale that draws on the affect, behavior, and cognition (ABC) model as the foundational structure of psychological response, incorporates basic psychological needs theory (BPN) to explain motivational dynamics, and is ultimately grounded in the I-PACE model, which provides a comprehensive framework for understanding the development of dependency-related behaviors. The scale was developed using a rigorous mixed-methods design implemented across a five-stage sequence that encompassed systematic item generation, expert review for content adequacy, and extensive empirical evaluation with a large academic sample. Findings from exploratory and confirmatory factor analyses converged to support a stable three-factor structure consisting of fifteen theoretically coherent items. The instrument demonstrated strong psychometric integrity, including high internal consistency, robust convergent and discriminant validity, and meaningful predictive validity, particularly in relation to attitudes linked to academic research misconduct. By establishing a validated measure of AI dependency within research contexts, this study advances current debates in digital ethics and responsible AI use and offers an empirically grounded tool for strengthening research integrity practices in higher education.

The introduction of modern Artificial Intelligence (AI) has dominated every sector of society. As the technology continues to advance, there is a growing push for the development of AI literate skills. However, not everyone is progressing at the same rate, which can put some individuals at a disadvantage once they enter the job market. Hence, it is critical that college students acquire the competencies needed to be competitive in today’s workforce. However, students’ familiarity and comfort using AI tools can hinge on their field of study, and criminal justice majors represent one group that may be particularly vulnerable. Engagement with the technology might also vary by campus type resulting from disparities in federal funding. As such, the current study analyzed survey data completed by criminal justice students at a Historically Black College and University (HBCU), a population potentially at high risk for delayed integration of AI technologies. The analyses revealed minimal engagement with AI tools among survey participants. Additionally, students’ gender, perceived benefits and drawbacks to using AI, concerns about academic misconduct and prior negative experiences involving AI use, influenced their engagement with the technology. Implications of these findings are discussed.

Late 2022 saw the release of ChatGPT, the first of many powerful generative artificial intelligence (GenAI) tools that quickly transformed the way people work and study. As Japanese universities were cognizant of GenAI’s increasing societal impact and its potential application in educational endeavors, they overwhelmingly left the responsibility for establishing and enforcing GenAI and other digital tools (DTs) usage policies in individual courses to instructors. Allowing instructors this free hand in determining GenAI’s role in their courses seems both sound and appropriate, as students primarily engage with course objectives, materials, and pedagogies through their instructors. Moreover, instructors are best positioned to monitor and discourage students’ misuse of GenAI/DTs. However, it is argued here that given GenAI’s unprecedented power and allure, leaving GenAI policy monitoring solely to instructors is not only ineffectual as a cheating deterrent but also burdensome, as it imposes numerous additional demands on instructors’ time and energy. The current article aims to support this argument by providing analyses spanning a ten-year period of course failures resulting directly from academic misconduct through GenAI/DT misuse in English-only research reports submitted by Japanese university students enrolled in an English language lecture course. Analyses reveal a dramatic increase in such course failures post-ChatGPT launch, specifically indicating the ineffectiveness of individual instructors in establishing and monitoring course-level GenAI policies. The subsequent additional burdens experienced by the course instructor are discussed, and a call is made for increased institutional support.

Abstract Academic misconduct appeal services have quietly emerged within China's education marketplace, with commercial agencies promoting themselves on social media to assist international students facing misconduct hearings. While existing research on academic integrity has emphasized prevention and detection, far less attention has been paid to what occurs after an allegation is raised and students enter formal appeal processes. This study examines the nature of these appeal services, how they operate in digital environments, and how platform logics shape their organization and visibility. Using BERTopic modelling, qualitative content analysis and digital ethnography, we analysed 996 promotional texts. We find that these agencies operate in a regulatory and ethical grey zone, ranging from sanction mitigation to claims of defending wrongly accused students. They strategically mobilize platform logics of visibility, credibility and scalability, packaging appeal support across the student lifecycle and translating cases into metrics such as success rates. We conceptualize this process as self‐platformization, through which commercial actors reorganize educational assistance in alignment with platform economies. By foregrounding the post‐violation stage, the study identifies blind spots in current integrity education and calls for a platform‐aware institutional response that strengthens procedural guidance, transparency and post‐violation learning support.

ABSTRACT In recent years, the rapid growth in global research output has been accompanied by an increasing challenge of research integrity. This study, taking China as an example, systematically examines the drivers, barriers, and governance strategies of research integrity through policy text analysis and in‐depth interviews, revealing a complex network of problems composed of institutional pressure, alienation of evaluation systems, and technical challenges. It finds the culture of ‘publish or perish’ resulting from a quantitative assessment system, uneven distribution of research resources, and lack of enough academic ethics education to be the main barriers to research integrity. Although the government has established a multi‐level governance system, policy implementation still faces challenges. With the rapid development of AI technology, new forms of academic misconduct pose challenges to the traditional regulatory model. Based on the research findings, this paper proposes suggestions to promote research integrity, ranging from passive compliance to active self‐discipline.

Synthesizing qualitative evidence on the experiences and perceptions of students and academics regarding generative AI usage in academic research, this review illuminates the ethical dimensions amidst growing application of AI tools like ChatGPT, Claude, and Gemini. By exploring data privacy, bias, and scholarly integrity concerns, it informs policy development and fosters responsible research practices involving generative AI. To evaluate the ethical considerations and integrity experiences of undergraduate and graduate students, academics, and faculty when using generative AI in research activities within higher education institutions worldwide. Searches were conducted in April 2024 across databases including Web of Science, Scopus, and PubMed with no language restrictions. Study selection involved title/abstract screening then full-text review. Critical appraisal assessed quality and relevance. Data extraction captured study details like populations and methods. A meta-aggregation approach synthesized findings into statements. Confidence was assessed for methodological robustness. Qualitative studies involving undergraduate/graduate students and academics/faculty at higher education institutions globally were included. Phenomena of interest were experiences and perceptions of ethics and integrity when using AI in research. Studies had to be conducted in academic research contexts using qualitative methods like phenomenology, grounded theory, ethnography, and action research. From 562 records, 30 studies were included representing diverse countries. Methodological quality was mixed; 55.9% rated high quality. Sixty findings were categorized into 10 areas then synthesized into 4 meta-aggregative flowcharts: 1) Need for ethical AI guidelines and policies; 2) AI impact on student learning and critical thinking; 3) AI detection and accuracy limitations; 4) Broader ethical and societal implications. Ambiguities exist around defining AI-related academic misconduct. While enhancing learning efficiency, AI raises concerns about diminished critical thinking from over-reliance. Robust AI detection and accuracy methods are needed. A holistic approach considering societal impacts is crucial for ethical AI integration in academia. Recommendations include developing clear policies, educational interventions, and rigorous longitudinal research. Systematic Review Registration: This review protocol was registered on the Open Science Framework and can be accessed using the following DOI: https://doi.org/10.17605/OSF.IO/AD6TR.

Islamic Religious Education (Pendidikan Agama Islam/PAI) is not merely concerned with the transmission of knowledge but with the transformation of values aimed at shaping moral character. Amid the currents of digitalization and social change, madrasahs are increasingly confronted with academic dishonesty and a weakening sense of responsibility, rendering teacher communication a strategic medium for the internalization of values within students’ everyday experiences. This study seeks to describe the forms and strategies of PAI teachers’ communication in cultivating honesty and responsibility, as well as to explore the meanings attributed by both teachers and students to this process. Employing a qualitative methodology with a phenomenological approach, the research was conducted at MTs Al-Ausath Karanganyar. Participants consisted of 2–3 PAI teachers and 5–6 seventh-grade students selected through purposive sampling. Data were collected through in-depth interviews, passive participant observation, and documentation studies, and were analyzed using the Miles and Huberman interactive model, supported by source triangulation and member checking. The thematic findings reveal four central axes of value-oriented communication: consistent role modeling, dialogical and contextualized advice, habituation accompanied by rational justification, and feedback that restores and sustains relational bonds. This study positions communicative experience as the primary analytical lens, shifting the focus from a mere inventory of techniques toward a shared meaning-making process through which values are enacted, experienced, and negotiated by teachers and students in the classroom.

Academic cheating remains a persistent problem in higher education. This study examines how pressure and opportunity influence academic cheating through the mediation of rationalization. This research aims to explain the relationship between pressure and opportunity. Primary data for this quantitative study were collected using a questionnaire completed by 65 students who had experience taking exams and completing academic assignments. The sample was determined using purposive sampling. The Covariance-Based SEM (CB-SEM) Covariance-Based SEM (CB-SEM) operated with LISREL 8.70 was used to analyze the data. The results show that pressure has a significant effect on academic cheating. Conversely, opportunity does not show a significant effect. Other results show that academic cheating significantly affects rationalization. The mediation test showed that rationalization successfully mediated the relationship between academic cheating and opportunity, but did not successfully mediate the relationship between pressure and opportunity. These findings indicate that efforts to prevent academic cheating need to focus on managing academic pressure and forming ethical rationalization among students, in addition to strengthening the academic control system.

Academic environments are expected to uphold high standards of integrity and professionalism. Yet, sexual misconduct by faculty has been a persistent and deeply concerning issue in U.S. colleges. Furthermore, sanctions for such misconduct remain lenient and inconsistent across colleges, which potentially contributes to victims' exposure to risky environments. Previous scholarly efforts found cultural factors leading to lenient sanctions for sexual misconduct. However, the earlier approaches did not fully examine broad external factors that could affect institutional sanction decisions. College administrations make critical decisions regarding sanctions for faculty sexual misconduct cases. Therefore, understanding factors that influence colleges' decisions is important for sexual misconduct prevention through clearer and consistent policies. To contribute, the current study aims to investigate (a) how U.S. colleges respond to sexual misconduct by the faculty and (b) which external factors are associated with the severity of sanctions. By analyzing the Academic Sexual Misconduct Database (ASMD), the current study found that (a) tenured faculty tend to receive lenient sanctions and (b) non-white faculty tend to receive harsher sanctions. Notably, the seriousness of sexual misconduct is not associated with sanction severity. These findings are consistent with sociological and criminological theories regarding institutional decision-making processes-colleges' decision-making processes follow rational choices based on cost estimation, and it may eventually lead to bias against non-white faculty who commit similar misconduct as white faculty. To promote safer academic environments, the current study recommends that colleges prepare better standards based on the nature of sexual misconduct. Additional implications for future research are also discussed.

ABSTRACT Although students’ achievement goals have been linked to academic dishonesty, more research is needed to clarify mixed findings and identify contextual factors that may influence these linkages. Guided by Expectancy-Value-Cost Theory versus Theory of Planned Behavior predictions, we tested how achievement goals, academic and cheating self-efficacy, injunctive and descriptive norms, and justifying attitudes jointly predict academic dishonesty in a preregistered two-wave study of 856 German university students. Appearance-approach goals, descriptive norms, and justifying attitudes positively related to exam cheating and plagiarism, other associations appeared behavior-specific (work-avoidance related only to plagiarism, cheating self-efficacy related more strongly to exam cheating). No interactions with achievement goals were found, favoring an additive risk structure consistent with the Theory of Planned Behavior. Results point to measurement specificity of both predictor variables and behavior types as one source of mixed findings.

Procrastination Trap: the Hidden Link between Stress, Anxiety, Amotivation and Academic Dishonesty

Artificial intelligence (AI) has rapidly reshaped modern education, with ChatGPT emerging as one of the most influential generative AI tools supporting teaching, learning, and academic administration. This review synthesizes evidence from 65 peer-reviewed studies published since 2022 to evaluate ChatGPT’s educational applications, benefits, constraints, and ethical implications. Findings indicate that ChatGPT enhances personalized learning, academic writing, digital literacy, and instructional efficiency, while offering scalable support for large classrooms. Comparative analyses reveal that ChatGPT demonstrates superior linguistic coherence and reasoning compared to Gemini, Bing Chat/Copilot, and Claude. However, concerns persist regarding hallucinations, academic dishonesty, data privacy, infrastructural disparities, and faculty readiness. The review highlights the need for responsible governance frameworks, AI literacy programs, and equitable institutional policies. Future directions include longitudinal research on learning outcomes, inclusive AI design, cross-cultural adoption patterns, and evolving teacher–student dynamics in AI-augmented environments.

Predictive Influence of Pressure, Opportunity and Rationalization on Academic Dishonesty among Accounting Students

The use of artificial intelligence (AI) in higher education, particularly in academic writing, is growing rapidly and bringing both positive impacts and ethical challenges. On the one hand, AI can assist students in writing essays and scientific papers, but on the other hand, it has the potential to encourage dependence and unethical practices, especially in thesis writing. This study aims to identify the strategies implemented by lecturers in anticipating the unethical use of AI in students' academic writing. This study uses a quantitative approach involving six lecturers from Bumigora University as respondents. Data were collected through a questionnaire and analysed descriptively. The results show that lecturers implement various strategies, including providing guidance on the ethical use of AI, checking and evaluating students' writing, and providing education on academic integrity. These findings indicate that lecturers play a very important role in guiding the responsible use of AI so that this technology can support the learning process without neglecting ethical values and academic honesty.

The rapid growth of online education has made effective proctoring systems essential to maintaining academic integrity and ensuring fair exams and online learning. Conventional proctoring methods like in-person invigilation are not feasible for online exams and e-learning. To address this issue, an automated proctoring method based on computer vision is proposed. This system tracks and analyzes applicant behavior throughout online learning and testing using advanced computer vision algorithms. A webcam and microphone are used by the Automated Proctoring System to continuously monitor the test environment. The Automated Proctoring System uses a webcam and screen recorder to monitor the test environment in real time. Computer vision algorithms are used to identify and monitor the examinee's face, gaze direction, eye movements, body posture, and suspicious activities. Machine learning models identify unusual patterns of behavior, such as continually turning away from the screen or showing many faces in the camera frame. These abnormalities cause the examiners to receive alerts for further inquiry. The proposed method aims to enhance exam security and e-learning by mitigating academic dishonesty, including cheating, impersonation, and unauthorized aids. It uses computer vision to provide a scalable and non-intrusive method of monitoring online assessments and e-learning

The post-pandemic shift in higher education requires students to develop strong metacognitive resilience to adapt effectively to hybrid learning while maintaining academic integrity. This study examines the relationship between metacognitive resilience, adaptive learning ability, and commitment to academic integrity among university students in Banten Province. Using a mixed-methods sequential explanatory design, quantitative data were collected through a survey of 60 purposively selected students, followed by in-depth interviews to enrich the findings. Data were analyzed using descriptive, correlational, and thematic techniques. The results indicate that metacognitive resilience significantly enhances students’ ability to adjust learning strategies and uphold ethical academic behavior in digital learning environments. Students with higher reflective awareness demonstrate greater flexibility in addressing online learning challenges and stronger consistency in maintaining academic honesty. The study highlights the importance of strengthening metacognitive literacy and academic ethics as foundations for sustainable adaptive learning in post-pandemic higher education.