CodeDive: A Web-Based IDE with Real-Time Code Activity Monitoring for Programming Education

As large language models (LLMs) continue to advance, they have garnered widespread public attention and extensive application across numerous industries and academic disciplines. The proliferation of LLMs has sparked considerable research interest, with studies primarily focusing on their technical characteristics and specific application scenarios. However, systematic research examining the impact of LLMs on academic integrity remains relatively scarce. Academic integrity is of paramount importance in the biomedical field. Therefore, this paper aims to examine both the opportunities and challenges that LLMs present to academic integrity in the biomedical field, and proposes solutions for optimizing the beneficial applications of LLMs. From a positive perspective, LLMs offer substantial benefits to researchers by enhancing research efficiency, improving research quality, and facilitating the generation and dissemination of academic insights. However, they also present numerous challenges, including the potential for promoting academic misconduct, generating content inaccuracies or ambiguous expressions, introducing bias and fairness concerns, compromising peer review mechanisms, facilitating the dissemination of misinformation, and undermining higher education - all of which demand careful attention. To address these issues, we propose solutions and feasible strategies centered on ten core dimensions: establishing policies and regulatory guidelines, enhancing AI literacy and application capabilities, developing and improving relevant technical tools, establishing human-AI collaboration models, reforming peer review procedures and academic evaluation systems, promoting international cooperation and standardization, increasing transparency and strengthening disclosure, reinforcing professional ethics education, and advancing artificial intelligence detection technologies. Overall, while LLMs undoubtedly pose challenges for maintaining academic integrity, their potential for positive impact remains promising. It is anticipated that with technological advancement and improved ethical standards, LLMs will ultimately preserve and strengthen academic integrity.

This paper explores the academic integrity considerations of students’ use of Artificial Intelligence (AI) tools using Large Language Models (LLMs) such as ChatGPT in formal assessments. We examine the evolution of these tools, and highlight the potential ways that LLMs can support in the education of students in digital writing and beyond, including the teaching of writing and composition, the possibilities of co-creation between humans and AI, supporting EFL learners, and improving Automated Writing Evaluations (AWE). We describe and demonstrate the potential that these tools have in creating original, coherent text that can avoid detection by existing technological methods of detection and trained academic staff alike, demonstrating a major academic integrity concern related to the use of these tools by students. Analysing the various issues related to academic integrity that LLMs raise for both Higher Education Institutions (HEIs) and students, we conclude that it is not the student use of any AI tools that defines whether plagiarism or a breach of academic integrity has occurred, but whether any use is made clear by the student. Deciding whether any particular use of LLMs by students can be defined as academic misconduct is determined by the academic integrity policies of any given HEI, which must be updated to consider how these tools will be used in future educational environments.

This viewpoint article first explores the ethical challenges associated with the future application of large language models (LLMs) in the context of medical education. These challenges include not only ethical concerns related to the development of LLMs, such as artificial intelligence (AI) hallucinations, information bias, privacy and data risks, and deficiencies in terms of transparency and interpretability but also issues concerning the application of LLMs, including deficiencies in emotional intelligence, educational inequities, problems with academic integrity, and questions of responsibility and copyright ownership. This paper then analyzes existing AI-related legal and ethical frameworks and highlights their limitations with regard to the application of LLMs in the context of medical education. To ensure that LLMs are integrated in a responsible and safe manner, the authors recommend the development of a unified ethical framework that is specifically tailored for LLMs in this field. This framework should be based on 8 fundamental principles: quality control and supervision mechanisms; privacy and data protection; transparency and interpretability; fairness and equal treatment; academic integrity and moral norms; accountability and traceability; protection and respect for intellectual property; and the promotion of educational research and innovation. The authors further discuss specific measures that can be taken to implement these principles, thereby laying a solid foundation for the development of a comprehensive and actionable ethical framework. Such a unified ethical framework based on these 8 fundamental principles can provide clear guidance and support for the application of LLMs in the context of medical education. This approach can help establish a balance between technological advancement and ethical safeguards, thereby ensuring that medical education can progress without compromising the principles of fairness, justice, or patient safety and establishing a more equitable, safer, and more efficient environment for medical education.

Applications driven by large language models (LLMs) are reshaping higher education by offering innovative tools that enhance learning, streamline administrative tasks, and support scholarly work. However, their integration into education institutions raises ethical concerns related to bias, misinformation, and academic integrity, necessitating thoughtful institutional responses. This article explores the evolving role of LLMs in midwifery higher education, providing historical context, key capabilities, and ethical considerations. Using insights from a US-based midwifery program, it highlights strategies for responsible LLM integration, including faculty development, classroom applications, and policy updates. The discussion addresses challenges such as mitigating bias, preventing plagiarism, and fostering critical thinking while ensuring that LLM-fueled applications remain a tool rather than a substitute for student learning. Practical approaches, including faculty training and student guidance, offer a replicable framework for leveraging LLM tools in professional health education while maintaining academic standards and equity.

The rapid advancement of large language models (LLMs) has enabled the generation of coherent essays, making AI‐assisted writing increasingly common in educational and professional settings. Using large‐scale empirical data, we examine and benchmark the characteristics and quality of essays generated by popular LLMs and discuss their implications for two key components of writing assessments: automated scoring and academic integrity. Our findings highlight limitations in existing automated scoring systems when applied to essays generated or heavily influenced by AI, and identify areas for improvement, including the development of new features to capture deeper thinking and recalibrating feature weights. Despite growing concerns that the increasing variety of LLMs may undermine the feasibility of detecting AI‐generated essays, our results show that detectors trained on essays generated from one model can often identify texts from others with high accuracy, suggesting that effective detection could remain manageable in practice.

This article examines the contemporary opportunities and limitations of using large language models(LLMs), including ChatGPT, in higher education and scientific research. It outlines the technologicalfoundations of LLMs, highlighting their capabilities for context-aware dialogue, language synthesis,automated assessment, and personalization of learning pathways, including applications in languagelearning and intercultural communication that can support learner autonomy and communicativecompetence. The study emphasizes the potential of AI to enhance the quality of education, supportpedagogical decision-making, and improve the management of educational processes. At the sametime, key risks are identified, including informational biases, reliance on training data, the potentialgeneration of inaccurate content, threats to privacy, and challenges to academic integrity. Ethicalconsiderations are discussed, focusing on algorithmic transparency, data security, researcheraccountability, and the prevention of discriminatory effects. The article also presents key strategies foraddressing these challenges, including the development of information and ethical literacy, theestablishment of transparent university policies, clarification of scientific publication requirements,and implementation of guidelines for responsible LLM use. The study concludes that effectiveintegration of LLMs into academic environments requires a balanced combination of innovativepotential and ethical safeguards to ensure the integrity of education and scientific research. Keywords: large language models (LLMs), higher education, personalized learning, academic integrity, artificial intelligence, digital literacy, sustainable development, ethical AI, communication

Research on academic integrity used to focus more on student character and behaviour. Now this research includes wider viewing of this issue as a current teaching and learning challenge which requires pedagogical intervention. It is now the responsibility of staff and institutions to treat the creation of a learning environment supporting academic integrity as a teaching and learning priority. Plagiarism by simply copying other people’s work is a well-known misconduct which undermines academic integrity; moreover, technological developments have evolved plagiarism to include the generation and copying of computer-generated text. Automated paraphrasing tool (APT) websites have become increasingly common, offering students machine-generated rephrased text that students input from their own or others’ writing. These developments present a creeping erosion of academic integrity under the guise of legitimate academic assistance. This also has implications for arrival of large language model (LLM) generative AI tools. In accessing these sites, students must discern what is a legitimate use of the tool and what may constitute breaching academic integrity. This study critically analysed the text from five online paraphrasing websites to examine the discourses used to legitimise and encourage APT use in both appropriate and inappropriate ways. We conceptualised these competing discourses using Sheep and Wolf metaphors. In addition, we offer a metaphor of the Educator as a Shepherd to become aware of APT website claims and assist students to develop critical language awareness when exposed to these sites. Educators can assist students with this through knowledge of how these sites use language to entice users to circumvent learning.

After a long period of stagnation in the last decades of 20th Century Russia started investing into development of IT and engineering education. In 2017 Kazan Federal University launched a novel master program in Intelligent Robotics, which was established based on the analysis of curriculums of world leading universities in robotics field and is being taught in English language. The paper presents result of surveys that were conducted among first year students of Robot Operating System (ROS) course, which serves as a basic backbone of the program. We analyzed dynamics of English language comprehension, self-efficiency, and active learning strategies among students within a semester while highlighting their attitude and motivation to study ROS as a part of robotics education. Special language environment was created during the class that allowed students to obtain positive results of material comprehension in English language. Additionally, a significant increase of motivation in studying robotics was observed among the students.

Large Language Models have completely revolutionized text generation, with significant studies reporting an unprecedented quantity of human-quality content. Still, this super capability comes with some grave risks: the spreading of misinformation at an unprecedented scale, academic plagiarism, and even erosion of trust in written communication. We are therefore developing a robust AI-generated text detection system. Our two-phased approach first involves training a neural network classifier on at first carefully hand-crafted textual features designed to capture subtle variations between human and LLM-generated text, then creating a web application using Next.js that allows a user to easily input text to analyze and receive a clear classification outcome. This project is yet another contribution to combat the spread of misinformation and to the preservation of academic integrity. But most importantly, for the responsible and ethical use of powerful AI technologies. Keywords: AI-Generated Text, Text Detection, Large Language Models, Machine Learning, Academic Integrity, Misinformation, Authenticity

Adoption of pre-trained large language models (LLMs) across an increasingly diverse range of tasks and domains poses significant problems for authorial attribution and other basic knowl­edge organization practices. Utilizing methods from value-sensitive design, this paper examines the theoretical, practical, and ethical issues introduced by LLMs and describes how their use challenges the supposedly firm boundaries separating specific works and creators. Focusing on the implications of LLM usage for higher education, we use hypothetical value scenarios and stakeholder analysis to weigh the pedagogical risks and benefits of LLM usage, assessing the consequences of their use on and beyond college campuses. While acknowledging the unique challenges presented by this emerging educational trend, we ultimately argue that the issues associated with these novel tools are indicative of preexisting limitations within standard entity-relationship models, not wholly new issues ushered in by the advent of a relatively young technology. We contend that LLM-generated texts largely exacerbate, rather than invent from scratch, the preexisting faults that have frequently posed problems to those seeking to determine, ascribe, and regulate authorship attributions. As the growing popularity of generative AI raises concerns about plagiarism, academic integrity, and intellectual property, we advocate for a reevaluation of reductive work-creator associations and encourage the adoption of more expansive authorial concepts.

NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Using Programming Projects in an Operating Systems Course as a Capstone Software Engineering Experience Computer Engineering Technology students at the University of Dayton take two fundamental programming courses teaching the basics of algorithmic problem solving along with the VBA and C++ syntaxes. These courses develop a strong programming foundation for the students; however, they lack the ability to introduce software programming within larger software systems. The final course related to software development is a required operating systems course. This course contains three fundamental goals: to develop the students’ understanding of key operating system concepts, to increase the students’ software engineering capabilities, and to introduce the students to the internal workings of the Linux and Windows operating systems. The operating systems course relies heavily on software programming exercises as a key instrument in teaching students the framework and applications of modern operating systems. The unique aspect of this approach is that the programming projects are designed not as standalone activities, but instead as individual components of a larger software system. Therefore, each programming project focuses not just on learning new syntax related to operating system concepts, but also in how these concepts are relevant to the software system being developed. Students develop programs for both the Microsoft Windows and Linux operating systems, working heavily with their associated application programming interfaces and investigating processes, threads, synchronization, input and output, and scheduling issues. A complete course overview and synopsis of the software programming projects will be presented along with student performance and comments from an end of the semester survey. Introduction The Computer Engineering Technology (CET) Program at the University of Dayton (UD) was started in 1999 and included two software programming courses. These courses predominately focused on teaching the fundamentals of software programming and the syntax associated with the VBA and C++ languages. Likewise, to satisfy the ABET outcome requirements for computer engineering technology programs, the ETC Program at UD also required a new 3 credit lecture course entitled “Concepts & Applications of Operating Systems” to be taken during the students’ Junior year of study.1 This course was structured to provide students hands-on exposure to the internal workings of modern operating systems. During the 2004/2005 academic year, the material being taught in the software programming was evaluated. In accordance with the Computing Curricula 2001 (CC2001) report by the Computer Society of the Institute for Electrical and Electronic Engineers (IEEE-CS) and the Association for Computing Machinery (ACM), the two software programming courses being taught fell within the traditional imperative-first approach. This approach starts students off with software programming activities without any real preparations.2 After careful review, it was found that the student population being served needed to be taught the algorithmic thought process prior to the syntax as many of the students had no prior exposure to software programming and were struggling more with defining the problem solutions than with the specific syntax being taught.3 A modified algorithms-first approach from CC2001 report was applied to these courses. Instead

Plagiarism, the unauthorized reuse of text, fueled by the ease of access to online content, is a pressing concern for academia, publishers, and authors. Paraphrasing, a common tactic in textual plagiarism, compounds the problem further. The automatic detection of paraphrased plagiarism in text documents is a fundamental task in Natural Language Processing (NLP), crucial for maintaining academic integrity and authenticity. This paper presents an extensive investigation into Urdu sentential paraphrased plagiarism detection leveraging advanced Deep Neural Networks (DNNs) and Large Language Models (LLMs). The study builds upon the foundational work and proposes modifications to the Deep Text Reuse and Paraphrased Plagiarism Detection (D-TRaPPD) architecture to incorporate state-of-the-art pre-trained LLMs. The proposed approach, SELLM-D-TRaPPD, integrates various language models, including contextualized sentence embedding-based LLMs, language-agnostic and multilingual transformer-based LLMs, and multilingual knowledge-distilled transformer-based LLMs. We evaluated these models against three benchmark Urdu sentential paraphrase corpora—Urdu Sentential Paraphrase Corpus, Urdu Short Text Reuse Corpus, and Semi-automatic Urdu Sentential Paraphrase Corpus. The results demonstrate the effectiveness of SELLM-D-TRaPPD with LLMs, achieving F1 scores of 92.09%, 96.70%, and 98.23%, respectively. A comparative analysis with existing state-of-the-art methods shows significant performance improvements, establishing SELLM-D-TRaPPD as the new leading approach for Urdu sentential paraphrased plagiarism detection. These findings highlight the value of leveraging advanced neural network architectures and pre-trained LLMs in improving the accuracy and effectiveness of paraphrased plagiarism detection in Urdu, addressing a crucial gap in Urdu NLP research.

Students often get stuck when programming independently, and need help to progress. Existing, automated feedback can help students progress, but it is unclear whether it ultimately leads to learning. We present Step Tutor, which helps struggling students during programming by presenting them with relevant, step-by-step examples. The goal of Step Tutor is to help students progress, and engage them in comparison, reflection, and learning. When a student requests help, Step Tutor adaptively selects an example to demonstrate the next meaningful step in the solution. It engages the student in comparing "before" and "after" code snapshots, and their corresponding visual output, and guides them to reflect on the changes. Step Tutor is a novel form of help that combines effective aspects of existing support features, such as hints and Worked Examples, to help students both progress and learn. To understand how students use Step Tutor, we asked nine undergraduate students to complete two programming tasks, with its help, and interviewed them about their experience. We present our qualitative analysis of students' experience, which shows us why and how they seek help from Step Tutor, and Step Tutor's affordances. These initial results suggest that students perceived that Step Tutor accomplished its goals of helping them to progress and learn.

An operating system course draws from a well-defined fundamental theory, but one needs to consider how more recent advances, not necessarily in the theory itself, can be applied to improve the course and the general body of knowledge of the student. The goal of This work is to show how recent software engineering developments can be introduced to such a course to not only satisfy the theory requirements, but also make the theory more understandable. In particular, This work focuses on how students can effectively learn the Unified Modeling Language, the object-oriented methodology, and the Java programming language in the context of an operating systems course. The goal is to form a systematic software engineering process for operating system design and implementation.

This project studied the instructional effectiveness of using animations to teach thirty-two learning objectives in an undergraduate operating systems course. The animations were created using Macromedia Flash™ and were employed as primary pedagogical tools during classroom instruction. Furthermore, a website for each animation was constructed to allow student accessibility to the animations outside the classroom. Statistical analysis using a paired t-test indicated that inclusion of animations in an undergraduate operating systems course yielded significant overall student achievement gains as measured by pretest and posttest scores. The inclusion of animations also was determined to be effective for approximately one-half of the thirty-two learning objectives when considered individually.