Search Results (17)

This study examines the ethical problems of using artificial intelligence (AI) applications in higher education, focusing on activities performed by university educators. Drawing on Slovak legislation that defines educators’ responsibilities, the study classifies their activities into three categories: teaching, scientific research, and other (academic management and self-directed professional development). From standpoint of methodology, a thematic review of 42 open-access, peer-reviewed articles published between 2022 and 2025 was conducted across the Web of Science and Scopus databases. Relevant AI applications and their associated ethical issues were identified and thematically categorized. Results of this study show that AI applications are extensively used across all analysed areas of university educators’ activities. Most notably used are applications that are generative language models, editing and paraphrasing tools, learning and assessment software, management and search tools, visualizing and design tools, and analysis and management systems. Their adoption raises ethical concerns which can be thematically grouped into six categories: privacy and data protection, bias and fairness, transparency and accountability, autonomy and oversight, governance gaps, and integrity and plagiarism. The results provide universities with a structured analytical framework to assess and address ethical risks related to AI use in specific academic activities. Although the study is limited to open-access literature, it offers a conceptual foundation for future empirical research and the development of ethical, institutionally grounded AI policies in higher education. Full article

Universities and journals increasingly rely on software tools for detecting textual overlap of a scientific text with the previously published literature to detect potential plagiarism. Although software outputs need to be carefully reviewed by competent humans to verify the existence of plagiarism, university and journal staff, for various reasons, often erroneously interpret the degree of plagiarism based on the percentage of textual overlap shown in the similarity report. This is often accompanied by explicit recommendations to the author(s) to paraphrase the text to achieve an “acceptable” percentage of overlap. Here, based on the available literature and real-world examples from similarity reports, we provide a classification with extensive examples of phrases that falsely inflate the similarity index and argue the futility and dangers of rephrasing such statements just for the sake of reducing the similarity index. The examples provided in this paper call for a more reasonable assessment of text similarity. To fully endorse the principles of academic integrity and prevent loss of clarity of the scientific literature, we believe it is important to shift from pure bureaucratic and quantificational view on the originality of scientific texts to human-centered qualitative assessment of the manuscripts, including the software outputs. Full article

This study investigates whether analyzing the code comments available in the source code can effectively reveal functional similarities within software. The authors explore how both machine-readable comments (such as linter instructions) and human-readable comments (in natural language) can contribute towards measuring the code similarity. For the former, the work is relying on computing the cosine similarity over the one-hot encoded representation of the machine-readable comments, while for the latter, the focus is on detecting similarities in English comments, using threshold-based computations against the similarity measurements obtained using models based on Levenshtein distances (for form-based matches), Word2Vec (for contextual word representations), as well as deep learning models, such as Sentence Transformers or Universal Sentence Encoder (for semantic similarity). For evaluation, this research has analyzed the similarities between different source code versions of the open-source code editor, VSCode, based on existing ESlint-specific directives, as well as applying natural language processing techniques on incremental releases of Kubernetes, an open-source system for automating containerized application management. The experiments outlines the potential for detecting code similarities solely based on comments, and observations indicate that models like Universal Sentence Encoder are providing a favorable balance between recall and precision. This research is integrated into Project Martial, an open-source project for automatic assistance in detecting plagiarism in software. Full article

Over the past few decades, researchers have put their effort and paid significant attention to the authorship attribution field, as it plays an important role in software forensics analysis, plagiarism detection, security attack detection, and protection of trade secrets, patent claims, copyright infringement, or cases of software theft. It helps new researchers understand the state-of-the-art works on authorship attribution methods, identify and examine the emerging methods for authorship attribution, and discuss their key concepts, associated challenges, and potential future work that could help newcomers in this field. This paper comprehensively surveys authorship attribution methods and their key classifications, used feature types, available datasets, model evaluation criteria and metrics, and challenges and limitations. In addition, we discuss the potential future research directions of the authorship attribution field based on the insights and lessons learned from this survey work. Full article

In recent years, the number of similar software products with many common parts has been increasing due to the reuse and plagiarism of source code in the software development process. Pattern matching, which is an existing method for detecting similarity, cannot detect the similarities between these software products and other programs. It is necessary, for example, to detect similarities based on commonalities in both functionality and control structures. At the same time, detailed software analysis requires manual reverse engineering. Therefore, technologies that automatically identify similarities among the arge amounts of code present in software products in advance can reduce these oads. In this paper, we propose a representation earning model to extract feature expressions from assembly code obtained by statically analyzing such code to determine the similarity between software products. We use assembly code to eliminate the dependence on the existence of source code or differences in development anguage. The proposed approach makes use of Asm2Vec, an existing method, that is capable of generating a vector representation that captures the semantics of assembly code. The proposed method also incorporates information on the program control structure. The control structure can be represented by graph data. Thus, we use graph embedding, a graph vector representation method, to generate a representation vector that reflects both the semantics and the control structure of the assembly code. In our experiments, we generated expression vectors from multiple programs and used clustering to verify the accuracy of the approach in classifying similar programs into the same cluster. The proposed method outperforms existing methods that only consider semantics in both accuracy and execution time. Full article

Research Aims. This research project investigates what are the major benefits and risks of Chinese students using ChatGPT for academic activities. Also, the project assesses how, if applicable, should ChatGPT be regulated in Chinese academic settings in order to maintain academic integrity and ethical standards. Methodology. The collection of primary data from relevant newspaper articles serves as the foundation of this research project. Here, the content analysis is used for primary data collection. A combination of keywords [“ChatGPT” AND (“China” OR “Chinese”) AND (“students” OR “student”)] were typed on the Google news search engine on 12 April 2023. A sum of 40 newspaper articles were deemed eligible for data analysis. Both qualitative and quantitative data were extracted and analyzed. Findings. The opinion of using ChatGPT to fulfil academic responsibilities has been polarized in China. The conservative camps worry that students are using ChatGPT to commit academic cheating. However, some Chinese educators believe AI-powered technologies should be incorporated into academic learning as AI-enabled writing tools can help improve the quality of academic outputs. A major concern that Chinese educators hold, to date, is plagiarism violations by students as an act of academic cheating. Most newspaper articles presented the use of ChatGPT in Chinese academic settings neutrally. Newspaper articles published in March 2023 contained more positive and negative word(s) about the use of ChatGPT in academic learning. Conclusions. Given the benefits ChatGPT can provide and the near-infeasibility of massively ban the use of AI-powered software, more regulations should be set up in Chinese academia. Teachers have to guide students on how to fact-check the details provided by AI and add citations and references accordingly in their coursework. Moreover, teachers should guide students on how to ask AI-powered software questions systematically and creatively, in order to maximize the intellectual outputs generated from ChatGPT. Full article

The purpose of this study was to critically examine the feedback obtained from higher education instructors regarding the implementation of the emergency remote assessment practices that took place within a university in the Republic of Cyprus, in order to identify the strengths and weaknesses of the changes that took place. This was essential since the abruptness of the pandemic did not always allow for smooth transitions during the introduction of these changes. Therefore, the results of this survey study that was based on an online questionnaire identified certain aspects of the assessment adaptations that were evaluated as positive (e.g., the use of e-invigilation software), and other aspects that were not as positive (e.g., performing oral examinations after the written test). However, the results also revealed that cheating and plagiarism were issues that concerned the instructors, as were the technological problems that were faced. All these results are discussed holistically at the end of this article in order to guide further research and decision making regarding online assessments. Full article

Online learning has made it possible to attend programming classes regardless of the constraint that all students should be gathered in a classroom. However, it has also made it easier for students to cheat on assignments. Therefore, we need a system to deal with cheating on assignments. This study presents a Watcher system, an automated cloud-based software platform for impartial and convenient online programming hands-on education. The primary features of Watcher are as follows. First, Watcher offers a web-based integrated development environment (Web-IDE) that allows students to start programming immediately without the need for additional installation and configuration. Second, Watcher collects and monitors the coding activity of students automatically in real-time. As Watcher provides the history of the coding activity to instructors in log files, the instructors can investigate suspicious coding activities such as plagiarism, even for a short source code. Third, Watcher provides facilities to remotely manage and evaluate students’ hands-on programming assignments. We evaluated Watcher in a Unix system programming class for 96 students. The results showed that Watcher improves the quality of the coding experience for students through Web-IDE, and it offers instructors valuable data that can be used to analyze the various coding activities of individual students. Full article

Due to the overall widespread accessibility of electronic materials available on the internet, the availability and usage of computers in education have resulted in a growth in the incidence of plagiarism among students. A growing number of individuals at colleges around the globe appear to be presenting plagiarised papers to their professors for credit, while no specific details are collected of how much was plagiarised previously or how much is plagiarised currently. Supervisors, who are overburdened with huge responsibility, desire a simple way—similar to a litmus test—to rapidly reform plagiarized papers so that they may focus their work on the remaining students. Plagiarism-checking software programs are useful for detecting plagiarism in examinations, projects, publications, and academic research. A number of the latest research findings dedicated to evaluating and comparing plagiarism-checking methods have demonstrated that these have restrictions in identifying the complicated structures of plagiarism, such as extensive paraphrasing as well as the utilization of technical manipulations, such as substituting original text with similar text from foreign alphanumeric characters. Selecting the best reliable and efficient plagiarism-detection method is a challenging task with so many options available nowadays. This paper evaluates the different academic plagiarism-detection methods using the fuzzy MCDM (multi-criteria decision-making) method and provides recommendations for the development of efficient plagiarism-detection systems. A hierarchy of evaluation is discussed, as well as an examination of the most promising plagiarism-detection methods that have the opportunity to resolve the constraints of current state-of-the-art tools. As a result, the study serves as a “blueprint” for constructing the next generation of plagiarism-checking tools. Full article

Medical education refers to education and training delivered to medical students in order to become a practitioner. In recent decades, medicine has been radically transformed by scientific and computational/digital advances—including the introduction of new information and communication technologies, the discovery of DNA, and the birth of genomics and post-genomics super-specialties (transcriptomics, proteomics, interactomics, and metabolomics/metabonomics, among others)—which contribute to the generation of an unprecedented amount of data, so-called ‘big data’. While these are well-studied in fields such as medical research and methodology, translational medicine, and clinical practice, they remain overlooked and understudied in the field of medical education. For this purpose, we carried out an integrative review of the literature. Twenty-nine studies were retrieved and synthesized in the present review. Included studies were published between 2012 and 2021. Eleven studies were performed in North America: specifically, nine were conducted in the USA and two studies in Canada. Six studies were carried out in Europe: two in France, two in Germany, one in Italy, and one in several European countries. One additional study was conducted in China. Eight papers were commentaries/theoretical or perspective articles, while five were designed as a case study. Five investigations exploited large databases and datasets, while five additional studies were surveys. Two papers employed visual data analytical/data mining techniques. Finally, other two papers were technical papers, describing the development of software, computational tools and/or learning environments/platforms, while two additional studies were literature reviews (one of which being systematic and bibliometric).The following nine sub-topics could be identified: (I) knowledge and awareness of big data among medical students; (II) difficulties and challenges in integrating and implementing big data teaching into the medical syllabus; (III) exploiting big data to review, improve and enhance medical school curriculum; (IV) exploiting big data to monitor the effectiveness of web-based learning environments among medical students; (V) exploiting big data to capture the determinants and signatures of successful academic performance and counteract/prevent drop-out; (VI) exploiting big data to promote equity, inclusion, and diversity; (VII) exploiting big data to enhance integrity and ethics, avoiding plagiarism and duplication rate; (VIII) empowering medical students, improving and enhancing medical practice; and, (IX) exploiting big data in continuous medical education and learning. These sub-themes were subsequently grouped in the following four major themes/topics: namely, (I) big data and medical curricula; (II) big data and medical academic performance; (III) big data and societal/bioethical issues in biomedical education; and (IV) big data and medical career. Despite the increasing importance of big data in biomedicine, current medical curricula and syllabuses appear inadequate to prepare future medical professionals and practitioners that can leverage on big data in their daily clinical practice. Challenges in integrating, incorporating, and implementing big data teaching into medical school need to be overcome to facilitate the training of the next generation of medical professionals. Finally, in the present integrative review, state-of-art and future potential uses of big data in the field of biomedical discussion are envisaged, with a focus on the still ongoing “Coronavirus Disease 2019” (COVID-19) pandemic, which has been acting as a catalyst for innovation and digitalization. Full article

The fast pace of development of the Internet and the Coronavirus Disease (COVID-19) pandemic have considerably impacted the educative sector, encouraging the constant transformation of the teaching/learning strategies and more in technological areas as Educational Software Engineering. Web programming, a fundamental topic in Software Engineering and Cloud-based applications, deals with various critical challenges in education, such as learning continuous emerging technological tools, plagiarism detection, generating innovative learning environments, among others. Continual change and even more change with the current digitization becomes a challenge for teachers and students who cannot depend on traditional educational methods. The article presents a sustainable teaching/learning methodology for web programming courses in Engineering Education using project-based learning adaptable to the continuous web technological advances. The methodology has been developed and improved during 9 years, 15 groups, and 3 different universities. Our results demonstrate that the methodology is adaptable with new technologies that might arise; it also presents the advantages of avoiding plagiarism in students and a personalized induction for every specific student in the learning process. Full article

In the context of the COVID-19 pandemic, the importance of online learning has increased. Inherently, the stakes of a sustainable approach to the challenges raised by the wide access to the Internet, the use of readymade solutions to meet didactical tasks, and students’ appetite for plagiarism have become higher. These challenges can be sustainably managed via a procedure aimed at constructively converting students’ appetite for plagiarism (SAP conversion) into a skill of critically approaching relevant materials that are available online. The solutions proposed by the specialized literature concerned with the problem of plagiarism can be grouped into five categories: better trained students, more involved teachers, the use of anti-plagiarism software, clear anti-plagiarism policies, and ethical education of the youths. The SAP conversion procedure is a solution targeting increased involvement on behalf of teachers. Its partial application in the case of the disciplines included in the undergraduate educational program of Sociology conducted by the Transylvania University of Brasov, where students’ evaluation is based on essays, has considerably decreased the amount of student plagiarism. Full article

The near universal use of electronic learning (e-learning) in higher education (HE) today requires that students and teachers are equipped with the requisite digital literacy skills. The small-scale pilot study we report on here explored the views and experiences of EAP (English for Academic Purposes) teachers regarding their development of digital literacy skills, their application of e-learning technology in their teaching, and their perceptions of its value as a learning tool—areas on which there has been little research to date. A convergent parallel mixed methods approach was adopted, in which a survey was administered to the research participants and a follow-up focus group conducted. The data were analysed, with findings revealing that the EAP practitioners surveyed utilised a range of online tools such as video, plagiarism software and corpus linguistics tools. A number of benefits and limitations associated with e-learning were cited by participants, including increased student engagement and motivation, the development of learner autonomy, and the cultural capital it represented in respect of students’ future careers. Meanwhile, the limitations identified included a lack of time for teachers to develop digital literacy and insufficient pre- and in-service training opportunities focused on the effective use of digital technologies and managing technical issues. We conclude with a series of recommendations to facilitate EAP teachers’ development and use of e-learning in their practice. Full article

Many open-source projects are developed by the community and have a common basis. The more source code is open, the more the project is open to contributors. The possibility of accidental or deliberate use of someone else’s source code as a closed functionality in another project (even a commercial) is not excluded. This situation could create copyright disputes. Adding a plagiarism check to the project lifecycle during software engineering solves this problem. However, not all code samples for comparing can be found in the public domain. In this case, the methods of identifying the source code author can be useful. Therefore, identifying the source code author is an important problem in software engineering, and it is also a research area in symmetry. This article discusses the problem of identifying the source code author and modern methods of solving this problem. Based on the experience of researchers in the field of natural language processing (NLP), the authors propose their technique based on a hybrid neural network and demonstrate its results both for simple cases of determining the authorship of the code and for those complicated by obfuscation and using of coding standards. The results show that the author’s technique successfully solves the essential problems of analogs and can be effective even in cases where there are no obvious signs indicating authorship. The average accuracy obtained for all programming languages was 95% in the simple case and exceeded 80% in the complicated ones. Full article

There is a large amount of Information Technology and Communication (ITC) tools that surround scholar activity. The prominent place of the peer-review process upon publication has promoted a crowded market of technological tools in several formats. Despite this abundance, many tools are unexploited or underused because they are not known by the academic community. In this study, we explored the availability and characteristics of the assisting tools for the peer-reviewing process. The aim was to provide a more comprehensive understanding of the tools available at this time, and to hint at new trends for further developments. The result of an examination of literature assisted the creation of a novel taxonomy of types of software available in the market. This new classification is divided into nine categories as follows: (I) Identification and social media, (II) Academic search engines, (III) Journal-abstract matchmakers, (IV) Collaborative text editors, (V) Data visualization and analysis tools, (VI) Reference management, (VII) Proofreading and plagiarism detection, (VIII) Data archiving, and (IX) Scientometrics and Altmetrics. Considering these categories and their defining traits, a curated list of 220 software tools was completed using a crowdfunded database (AlternativeTo) to identify relevant programs and ongoing trends and perspectives of tools developed and used by scholars. Full article