Plagiarism Detection Research Articles

Review of Code Similarity and Plagiarism Detection Research Studies

The foundational technique of code similarity detection, which underpins plagiarism detection tools, has already reached a level of maturity where it can be effectively employed for practical applications, demonstrating commendable performance. However, although the understanding of code clones—referred to as similar codes—has evolved, there has been a noticeable decline in the emergence of novel proposals for code similarity detection techniques. The landscape of code similarity detection techniques is diverse and can be divided based on how codes are represented. Each method, designed to cater to different types of detectable code similarity instances, has distinct advantages and drawbacks. Therefore, the selection of an appropriate method is crucial and is contingent on the specific objectives of the analysis. This paper provides a comprehensive exploration of code similarity detection techniques and illuminates the prevailing trends in plagiarism detection research. It acquaints readers with a spectrum of distinct code similarity detection methods, accompanied by the requisite contextual background knowledge. Additionally, it presents a detailed overview of the trajectory of research trends in plagiarism detection.

Efficient reinforcement learning-based method for plagiarism detection boosted by a population-based algorithm for pretraining weights

Plagiarism detection (PD) in natural language processing involves locating similar words in two distinct sources. The paper introduces a new approach to plagiarism detection utilizing bidirectional encoder representations from transformers (BERT)-generated embedding, an enhanced artificial bee colony (ABC) optimization algorithm for pre-training, and a training process based on reinforcement learning (RL). The BERT model can be incorporated into a subsequent task and meticulously refined to function as a model, enabling it to apprehend a variety of linguistic characteristics. Imbalanced classification is one of the fundamental obstacles to PD. To handle this predicament, we present a novel methodology utilizing RL, in which the problem is framed as a series of sequential decisions in which an agent receives a reward at each level for classifying a received instance. To address the disparity between classes, it is determined that the majority class will receive a lower reward than the minority class. We also focus on the training stage, which often utilizes gradient-based learning techniques like backpropagation (BP), leading to certain drawbacks such as sensitivity to initialization. In our proposed model, we utilize a mutual learning-based ABC (ML-ABC) approach that adjusts the food source with the most beneficial results for the candidate by considering a mutual learning factor that incorporates the initial weight. We evaluated the efficacy of our novel approach by contrasting its results with those of population-based techniques using three standard datasets, namely Stanford Natural Language Inference (SNLI), Microsoft Research Paraphrase Corpus (MSRP), and Semantic Evaluation Database (SemEval2014). Our model attained excellent results that outperformed state-of-the-art models. Optimal values for important parameters, including reward function are identified for the model based on experiments on the study dataset. Ablation studies that exclude the proposed ML-ABC and reinforcement learning from the model confirm the independent positive incremental impact of these components on model performance.

Detecting generative artificial intelligence in scientific articles: Evasion techniques and implications for scientific integrity

BackgroundArtificial intelligence (AI) tools, although beneficial for data collection and analysis, can also facilitate scientific fraud. AI detectors can help resolve this problem, but their effectiveness depends on their ability to track AI progress. In addition, many methods of evading AI detection exist and their constantly evolving sophistication can make the task more difficult. Thus, from an AI-generated text, we wanted to: (1) evaluate the AI detection sites on a text generated entirely by the AI, (2) test the methods described for evading AI detection, and (3) evaluate the effectiveness of these methods to evade AI detection on the sites tested previously. HypothesisNot all AI detection tools are equally effective in detecting AI-generated text and some techniques used to evade AI detection can make an AI-produced text almost undetectable. Materials and methodsWe created a text with ChatGPT-4 (Chat Generative Pre-trained Transformer) and submitted it to 11 AI detection web tools (Originality, ZeroGPT, Writer, Copyleaks, Crossplag, GPTZero, Sapling, Content at scale, Corrector, Writefull et Quill), before and after applying strategies to minimise AI detection. The strategies used to minimize AI detection were the improvement of command messages in ChatPGT, the introduction of minor grammatical errors such as comma deletion, paraphrasing, and the substitution of Latin letters with similar Cyrillic letters (a and o) which is also a method used elsewhere to evade the detection of plagiarism. We have also tested the effectiveness of these tools in correctly identifying a scientific text written by a human in 1960. ResultsFrom the initial text generated by the AI, 7 of the 11 detectors concluded that the text was mainly written by humans. Subsequently, the introduction of simple modifications, such as the removal of commas or paraphrasing can effectively reduce AI detection and make the text appear human for all detectors. In addition, replacing certain Latin letters with Cyrillic letters can make an AI text completely undetectable. Finally, we observe that in a paradoxical way, certain sites detect a significant proportion of AI in a text written by a human in 1960. DiscussionAI detectors have low efficiency, and simple modifications can allow even the most robust detectors to be easily bypassed. The rapid development of generative AI raises questions about the future of scientific writing but also about the detection of scientific fraud, such as data fabrication. Level of evidenceIII Control case study.

Measuring Policy Diffusion in Federal Systems: The Case of Legalizing Cannabis in Canada under Time Constraints

Abstract Existing studies of policy diffusion rely on quantitative or qualitative methods depending on the number of cases and the policy at hand. Studies of diffusion in Canada, for instance, almost exclusively use qualitative techniques due to the limited number of subnational units. In this article, we explore whether machine learning techniques can complement qualitative approaches in these contexts. In 2015, the Canadian federal government decided to impose the legalization of cannabis and gave the provinces and territories a short time frame to develop and implement legislation. Previous qualitative research on this case found that within-province policy development was more salient than interprovincial diffusion. Using a plagiarism detection software, we find limited evidence of exact matches between provincial legislation, but a cosine score approach reveals significant similarities across provinces. These results suggest that computational and qualitative techniques together should be used where possible to identify and analyze policy diffusion in certain contexts.

Tracing the Proliferation of Socialist Realism Doctrine in Latvian Periodicals: Case Study of "Literature and Art" and "The Flag"


 
 
 The paper presents the results of a study on the dissemination of socio-political and aesthetic ideas of Socialist Realism doctrine in the Latvian periodicals Literature and Art (LitArt) and The Flag. Several programmatic, ideologically saturated articles that were published in The Flag in the 1940s were compared to the rest of the corpus to explore the proliferation and persistence of similar ideas in the course of following decades. Authors have employed methodologies commonly used for plagiarism detection: fingerprinting and the comparison of document similarity based on word embeddings and document similarity measures. In particular, three perspectives were used to examine the similarity and reuse of texts: comparison of matching 5-grams processed by the winnowing algorithm and comparison of documents based on the TF-IDF and Doc2Vec embeddings and cosine similarity metrics. To facilitate the analysis, the results were loaded in the open-source version of Neo4j graph database. The findings were further explored and evaluated qualitatively to identify the distribution of direct citations, frequently reused phrases and most similar documents.
 
 

Trends in Seed Priming Research in the Past 30 Years Based on Bibliometric Analysis.

Seed priming (SP) treatments are widely used in agriculture and restoration to improve seed germination and seedling vigor. Although there exists a considerable amount of scientific literature on SP, it has seldom undergone visual and quantitative analyses. To gain insights into the patterns observed in SP research over the last three decades, we conducted a bibliometric analysis using the Science Citation Index-Expanded (SCI-E) database, aiming to minimize the similarity score in plagiarism detection. This analysis offers a thorough examination of yearly publications, temporal patterns in keyword usage, the top-performing journals, authors, institutions, and countries within the field of SP. Our research findings suggest a steady annual increase of 10.59% in the volume of SP publications, accompanied by a significant upward trajectory in the average citations received per paper annually. According to the analysis of keywords, it was found that "priming" and "germination" emerged as the most frequently used terms in the field of SP research. Seed Science and Technology ranked first among the top journals, and Plant Physiology had greater influence in the field of SP in terms of number of citations. The majority of the top 10 productive institutions were situated in developing countries. In addition, these nations exhibited the highest volume of published works and citations. Our analysis revealed a shift in research focus within the field of SP over the past three decades, transitioning from agricultural science to encompass plant science and environmental science. With the growing recognition of SP's research across different disciplines, there exist abundant prospects for international and interdisciplinary partnerships, collaborative organizations, and progress in this field.

Citation Culture in the Scientific Text and Verification of Borrowings

The scientific library historically serves the purposes of education, assists scientific research and integration of information into the body of knowledge. Without the qualitative maintenance of these auxiliary processes, the development of science is impossible. The library retains the status of a keeper of verified and systematized knowledge. In modern conditions, the process of searching and integrating information is simple and accessible. However, freedom and simplicity of obtaining facts and using various tools to compile integral language constructions entails a number of negative effects, including illegal borrowing, falsification and irresponsible use of text generation systems for scientific publications. The purpose of the article is to analyze the problems of citation culture changes caused, among other things, by the emergence of new technologies for artificial text generation. The main provisions of the article are based on the observation and experience of examining scientific manuscripts in order to detect illegal borrowings accumulated by a group of specialists in the Russian State Library over the past decade. This activity is not limited solely by the instrumental detection of plagiarism, but it is also focused on comparison of various citation practices in scientific papers. The problems of citation analysis, the influence of citation culture on the process of research verification are considered. Special attention is paid to the tools for artificial text generation that are actively included in the practice of writing academic texts.

An Innovative Strategy to Anticipate Students’ Cheating: The Development of Automatic Essay Assessment on the “MoLearn” Learning Management System

A quick and effective learning assessment is needed to evaluate the learning process. Many tools currently offer automatic assessment for subjective and objective questions; however, there is no such free tool that provides plagiarism detection among students for subjective questions in a Learning Management System (LMS). This research aims to create an automatic essay assessment in MoLearn LMS that can check students’ answers. At the same time, the LMS can also anticipate online cheating. This research employed two methods comprising (1) System Development Life Cycle (SDLC) waterfall model and (2) Latent Semantic Analysis (LSA) method. The former was used to design and create essay assessment applications, and the latter was employed to check the essay's answer and automatically detect students’ plagiarism. The result showed that MoLearn LMS was successfully working on detecting students’ plagiarism, leading to better LMS innovation. Automatic essay assessment accelerates grading time by about 8.04 times that of manual grading, which takes 5.52 seconds per question.

Full-privacy secured search engine empowered by efficient genome-mapping algorithms.

Since the 90s, keyword-based search engines have been the only option for people to locate relevant web content through a simple query comprising one to a few keywords. These free or paid services operate by storing users' search queries and preferences for personal profiling and targeted ads delivery, while user-uploaded articles for plagiarism detection can further be stored as part of service providers' expanding databases for profit. In short, it has never been an option for users to search the web without revealing their queries, some of which can be sensitive, to search engine providers. Here we demonstrate that an internet search, provided with the entire article as a query, can be correctly carried out without revealing users' query content by an irreversible encoding scheme and an efficient FM-index search routine that is generally used in the next generation sequencing (NGS) of human genomes. In our solution, Sapiens Aperio Veritas Engine (S.A.V.E.), every word in the query is encoded into one of 12 "amino acids", constituting a pseudo-biological sequence (PBS) at users' local machines. The PBS-mediated plagiarism detection is carried out by users' submission of locally encoded PBSs through our cloud service to locate identical duplicates in the collected web contents, currently including all the English and Chinese Wikipedia pages and Open Access journal articles, as of April 2021, which had been encoded in the same way as the query. It is found that PBSs with a length longer than 12, comprising a combination of more than 12 "amino acids", can return correct results with a false positive rate <0.8%. S.A.V.E. runs at a similar genome-mapping speed as Bowtie and is >5 orders faster than BLAST. Functioning in both regular and in-private search modes, S.A.V.E. provides a new option for efficient internet search and plagiarism detection in a compressed search space where users' confidential contents can never be revealed. We hope the reported algorithm and implementation could introduce a new paradigm for future privacy-aware search engines. S.A.V.E. is currently running at https://dyn.life.nthu.edu.tw/SAVE/.

STUDENTS' PERCEPTION OF PLAGIARISM: CASE STUDY OF ACADEMIC INTEGRITY FORMATION

The issue of maintaining academic integrity and preventing plagiarism is in the focus of attention of higher education institutions. The consideration of these issues have been actualized under the conditions of Covid-19, because with the transition to a distance form of education, the temptation of dishonest use of data from the Internet by students has increased. Higher education institutions are developing various strategies to prevent plagiarism in student works. This study presents an example of the formation of academic integrity during graduate studies at the National University of Life and Environmental Sciences of Ukraine. The need has been updated and the methodology for applying an educational approach (a distinction is made between educational and educational approaches) to the use of text-matching software (the Unicheck plagiarism detection tool built into the LMS Moodle has been used) for preventing plagiarism in student papers has been developed. During the approbation of the developed methodology in the process of studying the discipline of the students' choice "Scientific communication in graduate research", a positive correlation has been found between the pedagogical intervention and students' understanding of ethical academic practice. As a result of the survey of 146 graduate students (80 students from the intervention group and 64 students from the control group), the effectiveness of the application of the author's method has been proven: a significant difference has been determined between the point marks regarding students' confidence in preventing plagiarism and their practical confirmation among students who worked according to the author's method (the intervention group, indicators are significantly higher) and those who were trained using the educational approach (informing according to the policies and procedures determined by the university). It is suggested that the use of a combination of enlightening and educational approaches based on which pedagogical support for the independent use of text-matching software in the process of studying individual disciplines or modules is based on a change in the attitude of students to the use of a software tool for detecting plagiarism from an evaluation tool in favor of receiving feedback, which can help higher education institutions improve academic integrity.

Plagiarism in Secondary School: Minor Inaction with Major Consequences

Introduction. The ease of searching and copying someone elseʼs text on almost any topic is one of the most important reasons for the prevalence of plagiarism in academic papers. While plagiarism detectors have become a common tool for universities, such systems are practically not used in secondary schools. It is considered to be a serious problem, that high school graduates are unprepared for the academic papers’ originality requirements. Universities are making efforts to overcome this lack of readiness. The authors study the attitude of teachers and learners of the Russian secondary schools toward the problem of plagiarism. Materials and Methods. An anonymous online survey of teachers (n = 2,286) and learners (m = 4,555) of secondary schools in the Vologda, Kaliningrad and Lipetsk regions of the Russian Federation was conducted. The survey was carried out through the Google Forms service. The quantitative and qualitative analysis of the obtained data was carried out. Results. Learners acquire the habit of plagiarism already in secondary school. Secondary school learners are loyal to copying someone elseʼs text without reference to the source; although they partially admit that by doing so they do wrong. Teachers are less tolerant to this and ready to respond to plagiarism in learners’ papers by lowering marks/grades or by requiring learners to redo the work. Teachers do not always clearly understand which borrowings can be considered legitimate and correct, and which ones are not, they cannot explain this to their learners, cannot instill in them the necessary academic integrity, and cannot establish clear criteria for works evaluation.The use of plagiarism detection systems in schools is unsystematic and it is often the result of the initiative of individual teachers. There is also no methodology for using plagiarism detection systems in secondary schools. Discussion and Conclusion. The conducted studies for the first time provided actual confirmation of the hypotheses. It means that it is necessary to include plagiarism detection systems in the toolkit used in secondary school. We have obtained evidence, that it is necessary to implement such tools in the secondary education system step by step, as well as to create conditions, so that school teachers can acquire relevant skills. The materials of the article will be useful to scientists who study the problems of secondary and high education, managers of the secondary education system at all levels, teachers, lecturers and learners of pedagogical universities.

AI-based plagiarism detectors: Plagiarism fighters or makers?

Plagiarism damages the biomedical academic publication domain. Artificial intelligence (AI) is a rising hope in academic plagiarism hunting. However, new AI-based tools are available online to assist with plagiarising! This article presents plagiarism throughout history, especially in medicine, and the promises of AI to detect a new type of plagiarism, namely Aigiarism. The danger of the above-mentioned AI-based services to help in paraphrasing copied texts is also highlighted, including some proposed solutions.

Copy-Past Culture: Examining the Causes and Solutions to Source Code Plagiarism

In an era marked by the increasing digitization of society, the issue of source code plagiarism has emerged as a persistent concern. This research paper delves into the problem of source code plagiarism within educational settings, exploring its implications, potential remedies, and the associated hurdles in implementing these solutions. Source code plagiarism involves the unauthorized copying of code without proper attribution, and it has been on the rise in educational institutions due to various contributing factors. This paper sheds light on the educational system's pressures, time constraints, lofty expectations, and the allure of quick completion that make source code plagiarism appealing to students. Furthermore, it highlights the lack of understanding among students regarding academic integrity and citation methods, exacerbating the problem. Source code plagiarism not only hampers students' intellectual development and problem-solving skills but also undermines the fairness of assessments, posing grading challenges for educators. Nevertheless, there are several potential solutions. While proactive methods focus on prevention through education and policy, reactive methods employ AI-driven plagiarism detectors for detection. However, these solutions are not without their challenges, such as the issue of false positives in plagiarism detection and the potential adversarial response from students. In conclusion, source code plagiarism is a growing problem in modern society that can not be avoided any longer. Potential solutions to source code plagiarism should be taken into account while considering their withdrawals. Computer science and programming courses should foster a sense of integrity to avoid source code plagiarism and develop new generations of coders for the future.

Use prompt to differentiate text generated by ChatGPT and humans

As the Chat Generative Pre-trained Transformer (ChatGPT) achieves increased proficiency in diverse language tasks, its potential implications for academic integrity and plagiarism risks have become concerning. Traditional plagiarism detection tools primarily analyze text passages, which may fall short when identifying machine-generated text. This study aims to introduce a method that uses both prompts and essays to differentiate between machine-generated and human-written text, with the goal of enhancing classification accuracy and addressing concerns of academic integrity. Leveraging a dataset of student-written essays responding to eight distinct prompts, we generated comparable essays with ChatGPT. Similarity scores within machine-generated essays (“within” scores) and between human-written and machine-generated essays (“between” scores) were computed. Subsequently, we used the percentile scores of the “between” scores within the “within” scores distribution to gauge the probability of an essay being machine-generated. Our proposed method achieved high classification accuracy, with an AUC score of 0.991, a false positive rate of 0.01, and a false negative rate of 0.037 in the test set. This validates its effectiveness in distinguishing between machine-generated and human-written essays and shows that it outperforms existing approaches based solely on text passages. This research presents a straightforward and effective method to detect machine-generated essays using prompts, providing a reliable solution to maintain academic integrity in the era of advanced language models like ChatGPT. Nevertheless, the method is not without its limitations, warranting further research to investigate its performance across diverse educational contexts, various prompts, and different model hyperparameters.

How academic librarians combat student plagiarism

Plagiarism is one of Academia's most widely discussed topics, as it is considered severe academic misconduct. Academic librarians can become pioneers on the path to academic integrity and play a tremendously important role in combating student plagiarism. Using a quantitative approach, the present study attempts to investigate academic libraries' plagiarism preventive and detection role from the perspective of public university librarians in Greece. For the study, a five-faceted conceptual framework for the role of librarians was developed and evaluated. Results indicate that librarians have actively taken the lead in raising awareness of plagiarism and providing plagiarism education, support, and counselling. Yet, their participation and contribution to developing institutional plagiarism strategies could have been higher than the other roles. Similarly, the results showed low participation of library professionals in plagiarism detection services, underscoring the need for effective collaboration with other academic stakeholders. Analysis of differences based on demographic factors suggests that individuals with higher levels of education are more closely involved in various roles and activities related to plagiarism management. Implications of the findings are discussed.

Winnowing vs Extended-Winnowing: A Comparative Analysis of Plagiarism Detection Algorithms

Plagiarism is the main problem in the digital world, as people use others’ content without giving prior credit to the creator. Therefore, there should be proper and efficient algorithms to find plagiarized content on the Internet. This research proposes two algorithms: the winnowing algorithm and the extended winnowing algorithm. The winnowing algorithm can only calculate the similarity rate between documents, whereas the extended algorithm can mark the plagiarized text segment in the compared records along with their similarity rates. The similarity rate in both algorithms has been calculated using the Jaccard Coefficient. Although the extended algorithm is beneficial as it provides a text marking feature, it consumes more computation power, which is discussed in this study. There are research works done previously using this approach, but none has compared the algorithms’ performance on small texts. Thus, this research utilizes the Twitter form of data to test these algorithms’ performance, as it contains a maximum of 280 characters. The application proposed to detect plagiarism in tweets has been developed using Python as the backend and React as the front-end technology.

A Quantum Genetic Algorithm for Building a Semantic Textual Similarity Estimation Framework for Plagiarism Detection Applications.

The majority of the recent research on text similarity has been focused on machine learning strategies to combat the problem in the educational environment. When the originality of an idea is copied, it increases the difficulty of using a plagiarism detection system in practice, and the system fails. In cases like active-to-passive conversion, phrase structure changes, synonym substitution, and sentence reordering, the present approaches may not be adequate for plagiarism detection. In this article, semantic extraction and the quantum genetic algorithm (QGA) are integrated in a unified framework to identify idea plagiarism with the aim of enhancing the performance of existing methods in terms of detection accuracy and computational time. Semantic similarity measures, which use the WordNet database to extract semantic information, are used to capture a document's idea. In addition, the QGA is adapted to identify the interconnected, cohesive sentences that effectively convey the source document's main idea. QGAs are formulated using the quantum computing paradigm based on qubits and the superposition of states. By using the qubit chromosome as a representation rather than the more traditional binary, numeric, or symbolic representations, the QGA is able to express a linear superposition of solutions with the aim of increasing gene diversity. Due to its fast convergence and strong global search capacity, the QGA is well suited for a parallel structure. The proposed model has been assessed using a PAN 13-14 dataset, and the result indicates the model's ability to achieve significant detection improvement over some of the compared models. The recommended PD model achieves an approximately 20%, 15%, and 10% increase for TPR, PPV, and F-Score compared to GA and hierarchical GA (HGA)-based PD methods, respectively. Furthermore, the accuracy rate rises by approximately 10-15% for each increase in the number of samples in the dataset.

Text-borrowing Practices in LIS Doctoral Theses of South Indian Universities: A Study

This paper reports the findings of the study, which examined the text copying practices prevalent among the Ph.D. theses in Library and Information Science (LIS) submitted to South India’s universities. Ten theses available in the Shodhganga repository have been tested for text-borrowing practices. Turnitin, a plagiarism detection software, has been used to identify the text matches in theses, and the plagiarised portions are manually analyzed with original sources. The findings indicate that LIS doctoral theses borrow words up to 30% without proper citations. Copying text from other sources and presenting without appropriate citation modalities (Sham plagiarism) is found to be common among the South Indian Ph.D. theses. Direct plagiarism type is less compared to inappropriate paraphrasing.

Are paid tools worth the cost? A prospective cross-over study to find the right tool for plagiarism detection

BackgroundThe increasing pressure to publish research has led to a rise in plagiarism incidents, creating a need for effective plagiarism detection software. The importance of this study lies in the high cost variation amongst the available options for plagiarism detection. By uncovering the advantages of these low-cost or free alternatives, researchers could access the appropriate tools for plagiarism detection. This is the first study to compare four plagiarism detection tools and assess factors impacting their effectiveness in identifying plagiarism in AI-generated articles. MethodologyA prospective cross-over study was conducted with the primary objective to compare Overall Similarity Index(OSI) of four plagiarism detection software(iThenticate, Grammarly, Small SEO Tools, and DupliChecker) on AI-generated articles. ChatGPT was used to generate 100 articles, ten from each of ten general domains affecting various aspects of life. These were run through four software, recording the OSI. Flesch Reading Ease Score(FRES), Gunning Fog Index(GFI), and Flesch-Kincaid Grade Level(FKGL) were used to assess how factors, such as article length and language complexity, impact plagiarism detection. ResultsThe study found significant variation in OSI(p < 0.001) among the four software, with Grammarly having the highest mean rank(3.56) and Small SEO Tools having the lowest(1.67). Pairwise analyses revealed significant differences(p < 0.001) between all pairs except for Small SEO Tools-DupliChecker. Number of words showed a significant correlation with OSI for iThenticate(p < 0.05) but not for the other three. FRES had a positive correlation, and GFI had a negative correlation with OSI by DupliChecker. FKGL negatively correlated with OSI by Small SEO Tools and DupliChecker. ConclusionGrammarly is unexpectedly most effective in detecting plagiarism in AI-generated articles compared to the other tools. This could be due to different softwares using diverse data sources. This highlights the potential for lower-cost plagiarism detection tools to be utilized by researchers.

Challenges and Opportunities of Generative AI for Higher Education as Explained by ChatGPT

ChatGPT is revolutionizing the field of higher education by leveraging deep learning models to generate human-like content. However, its integration into academic settings raises concerns regarding academic integrity, plagiarism detection, and the potential impact on critical thinking skills. This article presents a study that adopts a thing ethnography approach to understand ChatGPT’s perspective on the challenges and opportunities it represents for higher education. The research explores the potential benefits and limitations of ChatGPT, as well as mitigation strategies for addressing the identified challenges. Findings emphasize the urgent need for clear policies, guidelines, and frameworks to responsibly integrate ChatGPT in higher education. It also highlights the need for empirical research to understand user experiences and perceptions. The findings provide insights that can guide future research efforts in understanding the implications of ChatGPT and similar Artificial Intelligence (AI) systems in higher education. The study concludes by highlighting the importance of thing ethnography as an innovative approach for engaging with intelligent AI systems and calls for further research to explore best practices and strategies in utilizing Generative AI for educational purposes.