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A New Era of Plagiarism the Danger of Cheating Using AI
... Such tools cast doubt on whether it is possible in modern times to enforce fairness in student learning outcomes evaluations. It is thus very important for instructors to understand and know these tools to be effective in their work and in performing student evaluations (Xiao et al., 2022). Researchers agree that plagiarism detection approaches can be grouped into metrics-based, token-based, graph-based, and abstract-based techniques (Humayoun et al., 2022). ...
... Essay Plagiarism detection tools can examine the semantics, syntactic, and stylometric features of an essay. Measurements for behavior similarity, text similarity, or in-code similarity are performed by this tool through numerical values based on either the binomial score, the Kindex score, the Hamming distance, the Max N-gram length, and the Levernshtein score (Xiao et al., 2022). ...
... The natural language processing capabilities of AI have brought about the introduction of models such as Chat GPT -3 which though complex, is capable of performing many Natural language Processing tasks after a few settings. Chat GPT which is a form of OpenAI playground tool, and others such as GitHub Copilot3 can potentially be used in plagiarism and thus cause harm to fair evaluations (Xiao et al., 2022). ...
In present times, intellectual property rights are the central focus of international economies and global market competitions among enterprises due to their important role in fostering cultural prosperity, economic development, and progress in the field of information technology. The advancement in information technology has made the field even more complicated as firms struggle to protect their copyrights in the face of online data explosion, dynamic e-commerce environment, and rising disruptive technologies such as Machine learning and Artificial Intelligence. On the other hand, plagiarism is on the rise in recent times. Students knowingly or unknowingly practice plagiarism daily to meet their stringent academic demands. Information Technology Tools encouraging plagiarism have further aggravated the problem. Intellectual property rights and plagiarism awareness are relatively weak even among scholars. Do intellectual property rights protection and existing plagiarism trends have any effect on the field of information technology research? This paper discusses intellectual property rights and plagiarism with the mindset of information technology research. The paper seeks to shed light on aspects of intellectual property rights and how they affect academic research, in the field of information technology. If after reading this paper a researcher, can take intellectual property rights and plagiarism seriously, then, this research would have achieved its desired outcome.
... For example, the recent GPT-4 model has demonstrated "human-level performance" in various professional and academic exams, notably scoring within the 90 th percentile in the Uniform Bar Exam (OpenAI, 2023a), raising concerns about its potential misuse by students seeking to cheat for better grades. Xiao et al. (2022) note that text-generation models provide students with ways to circumvent plagiarism detectors such as Turnitin, notably in the form of auto-code completion for coding assignments using GitHub's Copilot or answer generation for short essay questions during exams using ChatGPT. Such actions not only hinder student learning (Rudolph et al., 2023), but also results in unfair academic evaluations by undermining genuine learning efforts (Xiao et al., 2022). ...
... Xiao et al. (2022) note that text-generation models provide students with ways to circumvent plagiarism detectors such as Turnitin, notably in the form of auto-code completion for coding assignments using GitHub's Copilot or answer generation for short essay questions during exams using ChatGPT. Such actions not only hinder student learning (Rudolph et al., 2023), but also results in unfair academic evaluations by undermining genuine learning efforts (Xiao et al., 2022). ...
... Some current methods of counteracting AI-facilitated academic dishonesty may cause more harm than good. For example, closed book exams may promote "cramming" culture and fail to assess critical thinking skills (Rudolph et al., 2023), whereas the use of surveillance software during online examinations poses severe ethical and privacy concerns (Xiao et al., 2022). Furthermore, while software to detect AI-generated text exists, they are not without limitations. ...
In this literature review paper, we provide an analysis of foundation models, which are AI models trained on broad data that can adapt to various tasks. The paper explores the background, capabilities, and limitations of foundation models, focusing on text and image generation. It discusses the factors contributing to the success of foundation models, including scale and novel AI model architectures. We examine the capabilities of foundation models in different domains, including plaintext generation, software applications, and art generation, and provide concrete examples to illustrate their potential applications. We further discuss the limitations of foundation models, particularly in terms of output quality, coherence, generation of up-to-date content, multilingual capabilities, and associated costs. Moreover, we critically examine the concerns associated with foundation models, including misuse in general and academic settings, bias, accessibility, copyright, consent, and data privacy. To mitigate the risks and challenges posed by foundation models, we propose a range of mitigations. These include adapting foundation models for detection of misuse, reform of educational processes, the inclusion of diverse actors during model training and evaluation, investment and development in public AI infrastructure, the establishment of reasonable laws surrounding AI-generated works, better adherence to data privacy laws, as well as the development of proper cloaking and attribution systems for AI generated artwork. Overall, foundation models hold significant potential for transformative breakthroughs in AI technology, but their responsible development, deployment, and use require careful consideration of their limitations and implementation of effective mitigation strategies.
... Oravec (2023) and Bubas (2023) highlight the potential of AI-generating chatbots and conversational AI systems such as ChatGPT to facilitate academic dishonesty, with the latter also discussing the impact of the COVID-19 pandemic on cheating in online assessments. While Xiao (2022) underlines the threat posed by AI tools that enable plagiarism, Oravec (2022) expresses concerns about the implementation of AI-based cheating detection systems and underlines the need for rigorous testing and evaluation. Collectively, these studies highlight the need for proactive steps to address the misuse of AI in education. ...
... However, it is nearly impossible for a human to detect the use of AI in an online assessment. Furthermore, as Xiao (2022) notes, using AI tools to detect plagiarism is also vulnerable to attacks from AI-based tools. In addition, students may be more likely to cheat if they know their scores are calculated by or with the help of AI. ...
Generative AI (GenAI) systems pose new challenges in academic dishonesty. Students may be tempted to use GenAI systems to cheat and submit content in assignments and projects that they did not create themselves. This points to the need for schools to focus on strong deterrent measures as well as informative and enhancing practices. Academic dishonesty is not limited to students. The use of GenAI in academia raises two extreme ethical problems. These problems relate to the ethical problems of the end user and the ethical problems in the development of this technology. The ethical use of GenAI technologies should be achieved in a way that respects human rights and takes into account user concerns. This new technology requires a rethinking of teaching methods as well as assessment and evaluation. It is recommended that policymakers, students and faculty work collectively and take responsibility for adopting ethical values in the process of integrating GenAI into academia.
... f. Automating Administrative Tasks: Grading, managing attendance, and detecting plagiarism are just a few of the many educational processes that may be automated by artificial intelligence (AI), according to Nazaretsky et al. (2022), Kammüller and Satija (2023) and Xiao et al. (2022, November). By automating these tasks, AI technology enables educators to devote their time and energy to more engaging and individualized training techniques. ...
This study was conducted to analyze the integration of Artificial Intelligence (AI) in educational practices over the past decade (2015-2024). Artificial Intelligence is rapidly and continuously reshaping teaching and learning. This research was conducted to explore how AI has increased paradigm shifts from inactive to dynamic learning, from vogue to personalized education, and from teacher-centered to student-centered. A mixed-method approach was employed to explore the implications of AI on teaching dynamics and student outcomes. Qualitative data were collected through literature review, surveys, interviews, and focus group discussions which were processed and analyzed by using NVivo, however, quantitative data were collected by employing a questionnaire. The findings indicate trends, and practices in vogue, and provide guidelines for teachers, officials, and shareholders on the successful integration of AI into learning. It is recommended that further research be conducted to find out further insight and depth to maximize AI's worth use in pedagogy.
Keywords: Artificial Intelligence, Teaching Practices, Analysis
... Ethical considerations include data security, safety risks associated with autonomous technologies, and fairness in decision-making processes [6]. In the context of researching the ethical dimensions of using AI tools for scientific purposes, the study focuses on tools that can detect plagiarism in scientific papers [20]. Information experts, especially librarians specializing in science, play a vital role in identifying the most effective tools and educating users, such as scientists and students, on how to recognize and utilize these tools correctly. ...
Due to the emergence and increased development of Artificial Intelligence (AI), research in general has been significantly impacted, particularly in the field of scientific theories and models. The purpose of this study is to analyze the acceptance of both AI tools and traditional methodologies used in research. Moreover, conclusions about the respondents' perception and openness to using AI tools in research regarding gender, age and current academic position are discussed. Another goal is to compare the level of satisfaction from both the AI tools and the traditional research methods. A questionnaire-based survey was carried out between February and March 2024, and it included students and teaching staff at the University North in Croatia. The novelty of this research is mirrored in the scarcity of such empirical studies encompassing the academic community in Croatia.
... This rise of AI technologies has significantly increased the risks of plagiarism in academic settings, particularly as students leverage tools like ChatGPT to generate assignments that evade traditional detection methods. Research indicates that existing plagiarism detection algorithms are often ineffective against AI-generated content, as these tools can produce text that appears original and is difficult to flag as plagiarized (Xiao et al., 2022). For instance, a study found that even when AI-generated assignments were tested against reputable detection software like Turnitin, they often returned acceptable similarity levels after minor paraphrasing (Steponenaite, 2023). ...
Integrating artificial intelligence (AI) into academic research has sparked a significant discourse surrounding its ethical implications and potential benefits. This paper explores the complex relationship between AI-generated content and academic integrity, highlighting the challenges of the blurring lines between assistance and academic dishonesty. As educational institutions increasingly adopt AI tools, the necessity for scholars and students to reevaluate the boundaries of originality becomes paramount. The ethical considerations surrounding AI in academic writing encompass property, accuracy, and integrity issues, necessitating a commitment to ethical citation practices to uphold scholarly standards. Moreover, while AI can enhance writing quality and streamline research processes, it also raises concerns about unintentional plagiarism and the authenticity of original thought. The reliance on AI tools may lead to derivative outputs, complicating the distinction between genuine creativity and plagiarism. To address these challenges, educational institutions must implement robust training programs that promote the ethical use of AI, ensuring that students can responsibly integrate AI contributions into their work. Case studies demonstrate that when used effectively, AI can augment academic performance and foster deeper engagement with learning materials, illustrating its potential as a valuable educational resource. Ultimately, this paper advocates for a balanced approach that embraces the benefits of AI while maintaining a strong commitment to ethical scholarship, thereby shaping a future where technology enhances rather than undermines academic integrity.
... Additionally, students' ability to access a wide sea of knowledge due to the internet has made it extremely enticing for some to indulge in unethical methods such as copy-pasting or paraphrasing without proper reference (Comas-Forgas & Sureda-Negre, 2010). Traditional plagiarism detection tools, such as Turnitin and other anti-plagiarism software, have proved beneficial, but they frequently fail to detect increasingly sophisticated kinds of plagiarism, such as AI-generated material (Xiao et al., 2022). In fact, ChatGPT is capable of producing text that closely mimics human writing, increasing educators' difficulties in combating academic dishonesty (Fitria, 2023). ...
The purpose of this research is to gain a complete understanding of how students and faculty in higher education perceive the role of AI tools, their impact on academic integrity, and their potential benefits and threats in the educational milieu, while taking into account ways to help curb its disadvantages. Drawing upon a qualitative approach, this study conducted in-depth interviews with a diverse sample of faculty members and students in higher education, in universities across Lebanon. These interviews were analyzed and coded using NVivo software, allowing for the identification of recurring themes and the extraction of rich qualitative data. The findings of this study illuminated a spectrum of perceptions. While ChatGPT and AI tools are recognized for their potential in enhancing productivity, promoting interactive learning experiences, and providing tailored support, they also raise significant concerns regarding academic integrity. This research underscores the need for higher education institutions to carefully navigate the integration of AI tools like ChatGPT. It calls for the formulation of clear policies and guidelines for their ethical and responsible use, along with comprehensive support and training. This study contributes to the existing literature by presenting a comprehensive exploration of the perceptions of both students and faculty regarding AI tools in higher education, through a qualitative rich approach. By delving into the intricate dynamics of ChatGPT and academic integrity, this study offers fresh insights into the evolving educational landscape and the ongoing dialogue between technology and ethics.
... In this context, educational institutions should neither prohibit the use of AI tools nor ignore the growing potential of such tools. The key issue here is to recognise the potential value of AI tools in the teaching and learning processes (Xiao, Chatterjee, & Gehringer, 2022). ...
After years of development in the background, Artificial Intelligence (AI) has burst onto the global stage thanks to open tools for generating textual, visual, auditory, and audiovisual content. In this emerging context, AI is not only emerging as a technological phenomenon but also as a catalyst for innovation in the artistic and educational fields. Although we are only at the dawn, AI is rapidly evolving and leading us towards a revolution, opening a new field of possibilities in creative domains that will transform current aesthetic, procedural, and authorial conceptions. Its potential as a creative tool is currently limited to being a support that facilitates obtaining results of great formal quality and style quickly, but without human intervention based on clear objectives, it becomes an empty generator. Artistic Education must embrace this technology not as an intruder or rival, but as a tool to be known and integrated as another means of creation, developing skills that allow students not only to use these tools effectively but also to reflect on their implications in society and culture. Promoting a conscious, responsible, safe, and ethical use that ensures a critical stance towards generative AI. Understand that it is not a creative tool. It is for creators.
... The present generation encompasses sophisticated and adaptable ICT systems, which contain applications of AI. The utilization of AI applications has, in several aspects, introduced a novel era characterized by increased instances of plagiarism and academic dishonesty (Xiao et al., 2022). ...
Artificial intelligence (AI) is now widely utilized in a variety of industries, including education sector. Now more than ever, AI is being put to use in many fields, including the classroom. Nevertheless, it is anticipated that more challenges and impediments will arise in AI in the forthcoming years. One prominent concern pertains to an excessive dependence on AI, potentially resulting in issues such as academic dishonesty, intellectual theft, and insufficient educational development. Thus, the nominal group technique (NGT) was employed to present conclusions and recommendations from experts on managing academic dishonesty in the era of AI. The results of the study indicated that there are 14 strategies that can be used to make assignments more resistant to academic dishonesty in the age of AI. The findings of this study hold significance in generating numerous other strategies. Educators ought to explore new approaches to ensure assignments' continued relevance and efficacy in the era of artificial intelligence.
... Practical approaches are also called to reliably detect misuse cases. Recent research has varying degrees of success in developing plagiarism detection tools to identify machine-generated content (Xiao et al., 2022). For instance, GPTZero (https://gptzero.me), ...
This study offers suggestions for the ethical and responsible use of these technologies in educational settings by comprehensively addressing the effects of artificial intelligence (AI) in the field of education, its potential negativities and the concept of cyberloafing. Artificial intelligence is revolutionizing learning processes by offering innovative solutions such as providing individualized learning experiences, monitoring student performance, optimizing learning processes and personalizing teaching materials. However, along with these positive effects, it also carries various risks such as data privacy violations, ethical issues, reduced human interaction between teachers and students, systematic biases and inequality among students.
Cyberloafing has taken on a new dimension with the proliferation of artificial intelligence technologies. Cyberloafing refers to students' use of digital tools for extracurricular or personal entertainment purposes when they should be using them for educational purposes. In particular, the use of AI-based chatbots and creative content tools by students for noneducational activities can lead to negative consequences such as a decrease in academic achievement and distraction. However, AI also has the potential to monitor and limit such behaviors.
This study makes recommendations such as including AI literacy in curricula for all age
groups, providing comprehensive in-service trainings for teachers, and strengthening ethical and privacy policies. In addition, the importance of teachers' collaborative use of AI technologies is emphasized. Guidelines and strategies for the responsible use of AI in education should be developed and integrated into education in a way that protects students' critical thinking, creativity and ethical values. In this way, artificial intelligence can be considered as an opportunity rather than a threat in education.
This chapter combines evidence from empirical research studies with arguments drawn from philosophy to explore how we conceptualise the role of AI language assistants like ChatGPT in education. We begin with the challenge to existing models of education posed by AI’s ability to pass examinations. We examine again the critique of the idea of AI from Dreyfus and from Searle and the critique of the value of writing from Socrates, to suggest that there may have been much too much focus on the skill of academic writing in education at the expense of the skill of dialogue, a skill which is more fundamental to intellectual development. We then look at the potential of AI for teaching through dialogue and for teaching dialogue itself in the form of dialogic thinking. We ask what it means for a person to enter into dialogue with a large language model. We conclude that dialogic education mediated by dialogues with large-language models is itself a form of collective intelligence which leads us to articulate a vision of individual education as learning how to participate in AI mediated collective intelligence.
This paper aims at revealing the specifics of leadership in higher education by comparing it with leadership in traditional business organizations. As a result of the co-citation analysis (the sample included 3,827 articles retrieved from a Scopus database) the authors were able to narrow down the sample to 6 key studies exerting significant influence on the research landscape. The authors proceeded with analysis through the prism of “leader traits-leader behavior-situational context” framework to enable a comparison of leadership in higher education and traditional business organizations. The analytical view taken on the results revealed that academic environment present specific challenges distinct from traditional business contexts. Within this leadership context an integrative approach – encompassing transactional, transformational, and relational-oriented behaviors – is the most adequate style. The interaction between the leader and the followers within the university environment resembles a partnership (the leader acts as a senior partner) rather than the power relations that are specific to traditional business organizations.
Pair Programming is considered an effective approach to programming education, but the synchronous collaboration of two programmers involves complex coordination, making this method difficult to be widely adopted in educational settings. Artificial Intelligence (AI) code-generation tools have outstanding capabilities in program generation and natural language understanding, creating conducive conditions for pairing with humans in programming. Now some more mature tools are gradually being implemented. This review summarizes the current status of educational applications and research on AI-assisted programming technology. Through thematic coding of literature, existing research focuses on five aspects: underlying technology and tool introduction, performance evaluation, the potential impacts and coping strategies, exploration of behavioral patterns in technological application, and ethical and safety issues. A systematic analysis of current literature provides the following insights for future academic research related to the practice of “human-machine pairing” in programming: (1) Affirming the value of AI code-generation tools while also clearly defining their technical limitations and ethical risks; (2) Developing adaptive teaching ecosystems and educational models, conducting comprehensive empirical research to explore the efficiency mechanisms of AI-human paired programming; (3) Further enriching the application of research methods by integrating speculative research with empirical research, combining traditional methods with emerging technologies.
Plagiarism is a severe issue in academia, and uncertainty in plagiarism detection systems might lead to inconsistent detections. Thus, evaluating the system is essential; however, it is also a test oracle problem as it is challenging to distinguish correct behaviour from potentially incorrect behaviour of the system. To alleviate this challenge, we develop a feasible approach by applying an uncertainty matrix to identify the uncertainty of the plagiarism detection systems and derive metamorphic relations of metamorphic testing from the identified uncertainty for validation. We experimented with three plagiarism detection systems in a classroom scenario where students were hypothesized to use tools to generate answers for assignments. These answers were fed into the systems for validation by comparing the systems’ similarity scores of the tool-generated answers. Results showed that the proposed approach can effectively validate plagiarism detection systems. Future studies can apply this approach to locate uncertainties to enhance systems’ robustness.
The emergence of Large Language Models such as GPT3 have caused ripples through academia around the impact of such tools on plagiarism and student assessment. There are claims that these tools will make the traditional assessment approaches obsolete and there has been something of a moral panic across the sector, with some universities already threatening students with academic misconduct hearings should they use these tools. However, we can see if we consider historical literature, that the same concerns were levelled at the widespread advent of search engines and, even further into history, electronic calculators. Rather than panic or try to ban the inevitable, we propose the approaches will have to adapt, but this is not the end of assessment as some have proposed.KeywordsGPTArtificial intelligenceMoral panicAssessmentTechnology adoption
Employing paraphrasing tools to conceal plagiarized text is a severe threat to academic integrity.
To enable the detection of machine-paraphrased text, we evaluate the effectiveness of five pre-trained word embedding models combined with machine learning classifiers and state-of-the-art neural language models.
We analyze preprints of research papers, graduation theses, and Wikipedia articles, which we paraphrased using different configurations of the tools SpinBot and SpinnerChief.
The best performing technique, Longformer, achieved an average F1 score of 80.99% (F1=99.68% for SpinBot and F1=71.64% for SpinnerChief cases), while human evaluators achieved F1=78.4% for SpinBot and F1=65.6% for SpinnerChief cases.
We show that the automated classification alleviates shortcomings of widely-used text-matching systems, such as Turnitin and PlagScan.
To facilitate future research, all data (https://doi.org/10.5281/zenodo.3608000), code (https://github.com/jpelhaW/ParaphraseDetection), and two web applications (https://huggingface.co/jpelhaw/longformer-base-plagiarism-detection) showcasing our contributions are openly available.
Online exam supervision technologies have recently generated significant controversy and concern. Their use is now booming due to growing demand for online courses and for off-campus assessment options amid COVID-19 lockdowns. Online proctoring technologies purport to effectively oversee students sitting online exams by using artificial intelligence (AI) systems supplemented by human invigilators. Such technologies have alarmed some students who see them as a “Big Brother-like” threat to liberty and privacy, and as potentially unfair and discriminatory. However, some universities and educators defend their judicious use. Critical ethical appraisal of online proctoring technologies is overdue. This essay provides one of the first sustained moral philosophical analyses of these technologies, focusing on ethical notions of academic integrity, fairness, non-maleficence, transparency, privacy, autonomy, liberty, and trust. Most of these concepts are prominent in the new field of AI ethics, and all are relevant to education. The essay discusses these ethical issues. It also offers suggestions for educational institutions and educators interested in the technologies about the kinds of inquiries they need to make and the governance and review processes they might need to adopt to justify and remain accountable for using online proctoring technologies. The rapid and contentious rise of proctoring software provides a fruitful ethical case study of how AI is infiltrating all areas of life. The social impacts and moral consequences of this digital technology warrant ongoing scrutiny and study.
Source code plagiarism is a common occurrence in undergraduate computer science education. In order to identify such cases, many source code plagiarism detection tools have been proposed. A source code plagiarism detection tool evaluates pairs of assignment submissions to detect indications of plagiarism. However, a plagiarising student will commonly apply plagiarism-hiding modifications to source code in an attempt to evade detection. Subsequently, prior work has implied that currently available source code plagiarism detection tools are not robust to the application of pervasive plagiarism-hiding modifications. In this article, 11 source code plagiarism detection tools are evaluated for robustness against plagiarism-hiding modifications. The tools are evaluated with data sets of simulated undergraduate plagiarism, constructed with source code modifications representative of undergraduate students. The results of the performed evaluations indicate that currently available source code plagiarism detection tools are not robust against modifications which apply fine-grained transformations to the source code structure. Of the evaluated tools, JPlag and Plaggie demonstrates the greatest robustness to different types of plagiarism-hiding modifications. However, the results also indicate that graph-based tools, specifically those that compare programs as program dependence graphs, show potentially greater robustness to pervasive plagiarism-hiding modifications.
Source code plagiarism is a long-standing issue in tertiary computer science education. Many source code plagiarism detection tools have been proposed to aid in the detection of source code plagiarism. However, existing detection tools are not robust to pervasive plagiarism-hiding transformations and can be inaccurate in the detection of plagiarised source code. This article presents BPlag, a behavioural approach to source code plagiarism detection. BPlag is designed to be both robust to pervasive plagiarism-hiding transformations and accurate in the detection of plagiarised source code. Greater robustness and accuracy is afforded by analysing the behaviour of a program, as behaviour is perceived to be the least susceptible aspect of a program impacted upon by plagiarism-hiding transformations. BPlag applies symbolic execution to analyse execution behaviour and represents a program in a novel graph-based format. Plagiarism is then detected by comparing these graphs and evaluating similarity scores. BPlag is evaluated for robustness, accuracy and efficiency against five commonly used source code plagiarism detection tools. It is then shown that BPlag is more robust to plagiarism-hiding transformations and more accurate in the detection of plagiarised source code, but is less efficient than the compared tools.
Digital content is for copying: quotation, revision, plagiarism, and file sharing all create copies. Document fingerprinting is concerned with accurately identifying copying, including small partial copies, within large sets of documents. We introduce the class of local document fingerprinting algorithms, which seems to capture an essential property of any fingerprinting technique guaranteed to detect copies. We prove a novel lower bound on the performance of any local algorithm. We also develop winnowing, an efficient local fingerprinting algorithm, and show that winnowing's performance is within 33% of the lower bound. Finally, we also give experimental results on Web data, and report experience with Moss, a widely-used plagiarism detection service.
Plagiarism Detection Systems play an important role in revealing instances of a plagiarism act, especially in the educational sector with scientific documents and papers. The idea of plagiarism is that when any content is copied without permission or citation from the author. To detect such activities, it is necessary to have extensive information about plagiarism forms and classes. Thanks to the developed tools and methods it is possible to reveal many types of plagiarism. The development of the Information and Communication Technologies (ICT) and the availability of the online scientific documents lead to the ease of access to these documents. With the availability of many software text editors, plagiarism detections becomes a critical issue. A large number of scientific papers have already investigated in plagiarism detection, and common types of plagiarism detection datasets are being used for recognition systems, WordNet and PAN Datasets have been used since 2009. The researchers have defined the operation of verbatim plagiarism detection as a simple type of copy and paste. Then they have shed the lights on intelligent plagiarism where this process became more difficult to reveal because it may include manipulation of original text, adoption of other researchers' ideas, and translation to other languages, which will be more challenging to handle. Other researchers have expressed that the ways of plagiarism may overshadow the scientific text by replacing, removing, or inserting words, along with shuffling or modifying the original papers. This paper gives an overall definition of plagiarism and works through different papers for the most known types of plagiarism methods and tools.
Program assignments are traditionally an area of serious concern
in maintaining the integrity of the educational process. Systematic
inspection of all solutions for possible plagiarism has generally
required unrealistic amounts of time and effort. The “Measure Of
Software Similarity” tool developed by Alex Aiken at UC Berkeley
makes it possible to objectively and automatically check all solutions
for evidence of plagiarism. The authors have used MOSS in several large
sections of a C programming course (MOSS can also handle a variety of
other languages). They feel that MOSS is a major innovation for faculty
who teach programming and recommend that it be used routinely to screen
for plagiarism
A survey of plagiarism detection systems: Case of use with english, french and arabic languages
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- Faical Azouaou
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Evaluating large language models trained on code
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Text and code embeddings by contrastive pre-training
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Language models are few-shot learners
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Language models are few-shot learners
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Tools for detecting plagiarism in online exams
- Ashwini Edward F Gehringer
- Guoyi Menon
- Wang