Search Results (35)

With the proliferation of online examination systems, protecting academic integrity and reliably detecting cheating have become significant research problems. Current AI-based online monitoring systems can achieve high accuracy by analyzing visual behavioral cues; however, their often black-box nature limits their explainability, reliability, and legal compliance (e.g., GDPR). In contrast, while rule-based approaches are interpretable, they are insufficient for generalizing complex and ambiguous human behaviors. This study proposes an explainable neuro-symbolic framework combining data-driven learning with symbolic reasoning for cheating detection in online exams. The proposed framework comprises three main layers: a neural perceptron layer that generates a suspicious behavior score; a symbolic reasoning layer comprising ANFIS and ILP methods to increase explainability and manage ambiguity; and a neuro-symbolic fusion layer that integrates these two layers. The success of the proposed framework for plagiarism detection was evaluated using a dataset containing visual–behavioral features such as gaze behavior, head pose, hand-object interaction, and device usage, along with the XGBoost method at the neural perceptron layer. Experimental results show that the proposed approach achieves high detection success and supports decision-making using logical rules, thereby reducing false positives. In this respect, the study offers an ethical, transparent, and reliable solution for online exam security. Full article

As artificial intelligence (AI) becomes increasingly integrated into higher education, instructors and institutions face urgent questions about its implications for teaching, learning, and scholarly practice as well as power, agency, and access. This study draws on a critical AI media literacy framework to analyze user-generated discussions in the two largest higher education subreddits on Reddit.com. Through thematic content analysis, I explore faculty perceptions, pedagogical tensions, and imaginative possibilities surrounding AI’s academic role in shaping the current and future landscape of higher education. Findings reveal that discussions of student cheating, AI policies, writing practices, and faculty labor are not merely technical debates but sites where surveillance regimes, accountability structures, and academic precarity are negotiated in real time. Ultimately, I argue that AI in higher education is not simply a technological shift but a structural transformation requiring deliberate, critically informed governance grounded in equity and human agency. Full article

Background: Integrity, encompassing honesty, accountability, and ethical conduct, is a cornerstone of the dental profession, essential for patient trust and safety. Despite its importance, academic dishonesty remains a pervasive issue in dental education globally. This review examines the prevalence, causes, and long-term career implications of academic dishonesty in dental education and explores institutional strategies to cultivate a culture of integrity. Method: The study was conducted using PubMed, Scopus, Web of Science, and Google Scholar to identify studies published between 1970 and 2025 on academic dishonesty in dental education. Search terms included dental students, cheating, plagiarism, and clinical falsification. Eligible studies reported prevalence, drivers, or consequences of dishonest behaviors. Data were extracted and thematically synthesized to highlight common patterns and professional implications. Results: Self-reported data indicate alarmingly high rates of cheating among dental students, ranging from 43% to over 90%. Common forms include exam fraud, plagiarism, and the falsification of clinical records. Key drivers include intense academic pressure, competitive environments, and a perception of weak enforcement. Such behaviors are not merely academic violations—they have profound professional consequences. A history of academic dishonesty can damage a student’s reputation, hinder licensure and credentialing processes, and limit postgraduate opportunities. Crucially, studies indicate that unethical behavior in school can normalize dishonesty, predicting a higher likelihood of future professional misconduct, such as insurance fraud or malpractice, thereby jeopardizing patient care and public trust. Conclusions: Academic integrity is a critical predictor of professional ethical conduct. Dental schools must move beyond punitive policies to implement proactive, multi-faceted approaches. This includes integrating comprehensive ethics curricula, fostering reflective practice, promoting faculty role modeling, and empowering student-led initiatives to uphold honor codes. Cultivating an unwavering culture of integrity is essential not only for academic success but for developing trustworthy practitioners committed to lifelong ethical patient care. Full article

Examinations are typically used by educational institutions to assess students’ strengths and weaknesses. Unfortunately, exam malpractices like cheating and other forms of academic integrity violations continue to present a serious challenge to the evaluation framework because it seeks to provide a trustworthy assessment. Existing methods involving human invigilators have limitations, as they must be physically present in examination settings and cannot monitor all students who take an exam while successfully ensuring integrity. With the developments in artificial intelligence (AI) and computer vision, we now have novel possibilities to develop methods for detecting students who engage in cheating. This paper presents a practical, real-time detection system based on computer vision techniques for detecting cheating in examination halls. The system utilizes two primary methods: The first method is YOLOv8, a top-of-the-line object detection model, where the model is used to detect students in video footage in real time. After detecting the students, the second aspect of the detection process is to apply pose estimation to extract key points of the detected students. For the first time, this paper proposes to measure angles from the geometry of the key points of detected students by constructing two triangles using the distance from the tip of the nose to both eyes, and the distance from the tip of the nose to both ears; one triangle is sized from the distance to the eyes, and the other triangle contains the measurements to their ears. By continually calculating these angles, it is possible to derive each student’s facial pose. A dynamic threshold is calculated and updated for each frame to better represent the body position in real time. When the left or right angle pass that threshold, it is flagged as suspicious behavior indicating cheating. All detected cheating instances, including duration, timestamps, and captured images, are logged automatically in an Excel file stored on Google Drive. The proposed study presents a computationally cheap approach that does not utilize a GPU or additional computational aspects in any capacity. This implementation is affordable and has higher accuracy than all of those mentioned in prior studies. Analyzing data from exam halls indicated that the proposed system reached 96.18% accuracy and 96.2% precision. Full article

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

This study presents a systematic review on the integration of ChatGPT in education, examining its opportunities, challenges and future directions. Utilizing the PRISMA framework, the review analyzes 40 peer-reviewed studies published from 2020 to 2024. Opportunities identified include the potential for ChatGPT to foster individualized educational experiences, tailoring learning to meet the needs of individual students. Its capacity to automate grading and assessments is noted as a time-saving measure for educators, allowing them to focus on more interactive and engaging teaching methods. However, the study also addresses significant challenges associated with utilizing ChatGPT in educational contexts. Concerns regarding academic integrity are paramount, as students might misuse ChatGPT for cheating or plagiarism. Additionally, issues such as ChatGPT bias are highlighted, raising questions about the fairness and inclusivity of ChatGPT-generated content in educational materials. The necessity for ethical governance is emphasized, underscoring the importance of establishing clear policies to guide the responsible use of AI in education. The findings highlight several key trends regarding ChatGPT’s role in enhancing personalized learning, automating assessments, and providing support to educators. The review concludes by stressing the importance of identifying best practices to optimize ChatGPT’s effectiveness in teaching and learning environments. There is a clear need for future research focusing on adaptive ChatGPT regulation, which will be essential as educational stakeholders seek to understand and manage the long-term impacts of ChatGPT integration on pedagogy. Full article

The emergence of generative AI has caused a major dilemma—as higher education institutions prepare students for the workforce, the development of digital skills must become a normative aim, while simultaneously preserving academic integrity and credibility. The challenge they face is not simply a matter of using AI responsibly but typically of reconciling two opposing duties: (A) preparing students for the future of work, and (B) maintaining the traditional role of developing personal academic skills, such as critical thinking, the ability to acquire knowledge, and the capacity to produce original work. Higher education institutions must typically balance these objectives while addressing financial considerations, creating value for students and employers, and meeting accreditation requirements. Against this need, this multiple-case study of fifty universities across eight countries examined institutional response to generative AI. The content analysis revealed apparent confusion and a lack of established best practices, as proposed actions varied widely, from complete bans on generated content to the development of custom AI assistants for students and faculty. Oftentimes, the onus fell on individual faculty to exercise discretion in the use of AI, suggesting an inconsistent application of academic policy. We conclude by recognizing that time and innovation will be required for the apparent confusion of higher education institutions in responding to this challenge to be resolved and suggest some possible approaches to that. Our results, however, suggest that their top concern now is the potential for irresponsible use of AI by students to cheat on assessments. We, therefore, recommend that, in the short term, and likely in the long term, the credibility of awards is urgently safeguarded and argue that this could be achieved by ensuring at least some human-proctored assessments are integrated into courses, e.g., in the form of real-location examinations and viva voces. Full article

The advent of generative artificial intelligence (GenAI) has significantly transformed the educational landscape. While GenAI offers benefits such as convenient access to learning resources, it also introduces potential risks. This study explores the phenomenon of learning burnout among university students resulting from the misuse of GenAI in this context. A questionnaire was designed to assess five key dimensions: information overload and cognitive load, overdependence on technology, limitations of personalized learning, shifts in the role of educators, and declining motivation. Data were collected from 143 students across various majors at Shandong Institute of Petroleum and Chemical Technology in China. In response to the issues identified in the survey, the study proposes several teaching strategies, including cheating detection, peer learning and evaluation, and anonymous feedback mechanisms, which were tested through experimental teaching interventions. The results showed positive outcomes, with students who participated in these strategies demonstrating improved academic performance. Additionally, two rounds of surveys indicated that students’ acceptance of additional learning tasks increased over time. This research enhances our understanding of the complex relationship between GenAI and learning burnout, offering valuable insights for educators, policymakers, and researchers on how to effectively integrate GenAI into education while mitigating its negative impacts and fostering healthier learning environments. The dataset, including detailed survey questions and results, is available for download on GitHub. Full article

While research articles on students’ perceptions of large language models such as ChatGPT in language learning have proliferated since ChatGPT’s release, few studies have focused on these perceptions among English as a foreign language (EFL) university students in South America or their application to academic writing in a second language (L2) for STEM classes. ChatGPT can generate human-like text that worries teachers and researchers. Academic cheating, especially in the language classroom, is not new; however, the concept of AI-giarism is novel. This study evaluated how 56 undergraduate university students in Ecuador viewed GenAI use in academic writing in English as a foreign language. The research findings indicate that students worried more about hindering the development of their own writing skills than the risk of being caught and facing academic penalties. Students believed that ChatGPT-written works are easily detectable, and institutions should incorporate plagiarism detectors. Submitting chatbot-generated text in the classroom was perceived as academic dishonesty, and fewer participants believed that submitting an assignment machine-translated from Spanish to English was dishonest. The results of this study will inform academic staff and educational institutions about how Ecuadorian university students perceive the overall influence of GenAI on academic integrity within the scope of academic writing, including reasons why students might rely on AI tools for dishonest purposes and how they view the detection of AI-based works. Ideally, policies, procedures, and instruction should prioritize using AI as an emerging educational tool and not as a shortcut to bypass intellectual effort. Pedagogical practices should minimize factors that have been shown to lead to the unethical use of AI, which, for our survey, was academic pressure and lack of confidence. By and large, these factors can be mitigated with approaches that prioritize the process of learning rather than the production of a product. Full article

Large language models (LLMs) have demonstrated remarkable capabilities in text generation, which also raise numerous concerns about their potential misuse, especially in educational exercises and academic writing. Accurately identifying and tracing the origins of LLM-generated content is crucial for accountability and transparency, ensuring the responsible use of LLMs in educational and academic environments. Previous methods utilize binary classifiers to discriminate whether a piece of text was written by a human or generated by a specific LLM or employ multi-class classifiers to trace the source LLM from a fixed set. These methods, however, are restricted to one or several pre-specified LLMs and cannot generalize to new LLMs, which are continually emerging. This study formulates source LLM tracing in a class-incremental learning (CIL) fashion, where new LLMs continually emerge, and a model incrementally learns to identify new LLMs without forgetting old ones. A training-free continual learning method is further devised for the task, the idea of which is to continually extract prototypes for emerging LLMs, using a frozen encoder, and then to perform origin tracing via prototype matching after a delicate decorrelation process. For evaluation, two datasets are constructed, one in English and one in Chinese. These datasets simulate a scenario where six LLMs emerge over time and are used to generate student essays, and an LLM detector has to incrementally expand its recognition scope as new LLMs appear. Experimental results show that the proposed method achieves an average accuracy of 97.04% on the English dataset and 91.23% on the Chinese dataset. These results validate the feasibility of continual origin tracing of LLM-generated text and verify its effectiveness in detecting cheating in student coursework. Full article

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

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

The emergence of generative artificial intelligence (AI) such as ChatGPT has sparked significant assessment concerns within tertiary education. Assessment concerns have largely revolved around academic integrity issues among students, such as plagiarism and cheating. Nonetheless, it is also critical to consider that generative AI models trained on information retrieved from the Internet could produce biased and discriminatory outputs, and hallucination issues in large language models upon which generative AI acts provide made-up and untruthful outputs. This article considers the affordances and challenges of generative AI specific to assessments within tertiary education. It illustrates considerations for assessment redesign with the existence of generative AI and proposes the Against, Avoid and Adopt (AAA) principle to rethink and redesign assessments. It argues that more generative AI tools will emerge exponentially, and hence, engaging in an arms race against generative AI and policing the use of these technologies may not address the fundamental issues in assessments. Full article

In light of the burgeoning influence of LLM AI programs like ChatGPT in a variety of academic contexts and the COVID-19 pandemic’s expansion of virtual classrooms and coursework, the philosophical framing of academic integrity and plagiarism is being re-examined. In concert with these technological changes, students are also facing increasing pressure to succeed in their academic pursuits. Inasmuch as the consequences of failure in these contexts are often dire academically, socially, and financially, we argue that students often weigh the choice to plagiarize not as an ethical issue but as a pragmatic mitigation of risk. Using three salient examples of plagiarism and cheating from higher education in North America as case studies, we explore the pressures and contexts that have influenced the choice to engage in plagiarism and cheating through this pragmatic lens. As an ethical framing of the issue of academic integrity has been less effective in ameliorating plagiarism in this pressurized climate, we propose a way in which educators, administrators and policy makers might approach the issue in this same pragmatic frame. In short, rather than combat plagiarism by teaching its moral repugnance, we propose educators could argue instead that plagiarism and cheating are pragmatically untenable simply because they are detrimental to learning. Full article

There has been a growing interest around the broader effects of psychopathic traits, particularly in relation to deviant behaviors and the notion of so-called “successful psychopathy”. As significant sociocultural touchstones, sporting events are often characterized by competitiveness and a sense of prestige. However, there has been limited attention towards psychopathic traits across recreational, amateur, and elite sports. Accordingly, we conducted a narrative review synthesizing primary observations on this topic, searching keywords in Scopus, APA PsychNet, and PubMed. Twenty-four academic papers were included in our results, which we thematized around demographic groups, namely: athletes and sport-adjacent non-athletes (i.e., coaches and spectators). Based on empirical findings from the reviewed papers, psychopathic traits could have medicolegal and forensic implications in relation to substance use, aggression, and violence. These could intersect with wider issues around doping, cheating, foul play, and have adverse outcomes for fellow participants, team dynamics, and spectators. Interestingly, our review also indicates that psychopathic traits may have correlations with determination and achievement in sport, echoing developing ideas around “successful psychopathy” in other domains. As such, increased awareness from all stakeholders and further multidisciplinary exchanges are vital to better understand the effects of psychopathic traits in sporting frameworks and their wider consequences. Full article