Search Results (25)

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

The rapid advancement of artificial intelligence (AI) has introduced new challenges, particularly in the generation of AI-written content that closely resembles human-authored text. This poses a significant risk for misinformation, digital fraud, and academic dishonesty. While large language models (LLM) have demonstrated impressive capabilities across various languages, there remains a critical gap in evaluating and detecting AI-generated content in under-resourced languages such as Turkish. To address this, our study investigates the effectiveness of long short-term memory (LSTM) networks—a computationally efficient and interpretable architecture—for distinguishing AI-generated Turkish texts produced by ChatGPT from human-written content. LSTM was selected due to its lower hardware requirements and its proven strength in sequential text classification, especially under limited computational resources. Four experiments were conducted, varying hyperparameters such as dropout rate, number of epochs, embedding size, and patch size. The model trained over 20 epochs achieved the best results, with a classification accuracy of 97.28% and an F1 score of 0.97 for both classes. The confusion matrix confirmed high precision, with only 19 misclassified instances out of 698. These findings highlight the potential of LSTM-based approaches for AI-generated text detection in the Turkish language context. This study not only contributes a practical method for Turkish NLP applications but also underlines the necessity of tailored AI detection tools for low-resource languages. Future work will focus on expanding the dataset, incorporating other architectures, and applying the model across different domains to enhance generalizability and robustness. Full article

The rapid growth of online education has raised significant concerns about identifying and addressing academic dishonesty in online exams. Although existing solutions aim to prevent and detect such misconduct, they often face limitations that make them impractical for many educational institutions. This paper introduces a novel online education integrity system utilizing well-established statistical methods to identify academic dishonesty. The system has been developed and integrated as an open-source Moodle plug-in. The evaluation involved utilizing an open-source Moodle quiz log database and creating synthetic benchmarks that represented diverse forms of academic dishonesty. The findings indicate that the system accurately identifies instances of academic dishonesty. The anticipated deployment includes institutions that rely on the Moodle Learning Management System (LMS) as their primary platform for administering online exams. Full article

This systematic literature review rigorously evaluates the impact of Generative AI (GenAI) on academic integrity within higher education settings. The primary objective is to synthesize how GenAI technologies influence student behavior and academic honesty, assessing the benefits and risks associated with their integration. We defined clear inclusion and exclusion criteria, focusing on studies explicitly discussing GenAI’s role in higher education from January 2021 to December 2024. Databases included ABI/INFORM, ACM Digital Library, IEEE Xplore, and JSTOR, with the last search conducted in May 2024. A total of 41 studies met our precise inclusion criteria. Our synthesis methods involved qualitative analysis to identify common themes and quantify trends where applicable. The results indicate that while GenAI can enhance educational engagement and efficiency, it also poses significant risks of academic dishonesty. We critically assessed the risk of bias in included studies and noted a limitation in the diversity of databases, which might have restricted the breadth of perspectives. Key implications suggest enhancing digital literacy and developing robust detection tools to effectively manage GenAI’s dual impacts. No external funding was received for this review. Future research should expand database sources and include more diverse study designs to overcome current limitations and refine policy recommendations. Full article

The integration of Artificial Intelligence (AI) in education has transformed academic learning, offering both opportunities and challenges for students’ development. This study investigates the impact of AI technologies on students’ learning processes and academic performance, with a focus on their perceptions and the challenges associated with AI adoption. Conducted at the National University of Science and Technology POLITEHNICA Bucharest, this research involved second-year students who had direct experience with AI-enhanced learning environments. Using purposive sampling, 85 participants were selected to ensure relevance. Data were collected through a structured questionnaire comprising 11 items as follows: seven closed-ended questions assessing perceptions, usage, and the effectiveness of AI tools; and four open-ended questions exploring experiences, expectations, and concerns. Quantitative data were analyzed using frequency and percentage calculations, while qualitative responses were subjected to thematic analysis, incorporating both vertical (individual responses) and horizontal (cross-dataset) approaches to ensure comprehensive theme identification. The findings reveal that AI offers significant benefits, including personalized learning, improved academic outcomes, and enhanced student engagement. However, challenges such as over-reliance on AI, diminished critical thinking skills, data privacy risks, and academic dishonesty were also identified. The study underscores the necessity of a structured framework for AI integration, supported by ethical guidelines, to maximize benefits while mitigating risks. In conclusion, while AI holds immense potential to enhance learning efficiency and academic performance, its successful implementation requires addressing concerns related to accuracy, cognitive disengagement, and ethical implications. A balanced approach is essential to ensure equitable, effective, and responsible learning experiences in AI-enhanced educational environments. Full article

Background/Objectives: Numerous studies have examined nursing students’ academic dishonesty; however, there is still a gap in understanding the predictors of such behavior. This study aimed to identify personal (intrapersonal and interpersonal) and contextual factors predicting nursing students’ dishonesty during clinical training. Methods: A two-phase, prospective, predictive study was conducted at a nursing faculty in Croatia. The validated “Mentor Support Evaluation Questionnaire” was used in the first phase to assess students’ evaluations of the quality of mentor support during clinical training. The validated instruments “Optimism/Pessimism Scale” and “Nursing Student Perceptions of Dishonesty Scale” were used in the second phase to examine self-reported dishonesty and its contributing factors. The second phase also investigated the students’ knowledge of the university’s ethical and disciplinary regulations. Results: Of 398 participants, 195 (48.9%) reported engaging in clinical dishonesty. Hierarchical regression analysis identified critical predictors of frequent clinical dishonesty: lack of fear of consequences (β = −0.072), positive attitudes toward dishonesty (β= −0.081), higher incidence of academic dishonesty in the classroom (β = 0.221), lack of knowledge of the university’s regulations (β = −0.349), and low quality of mentor support (β = −0.430). The final model explained 60.7% of the variance in participants’ clinical dishonesty (p < 0.001). Conclusions: The identified predictors indicate that interpersonal factors, i.e., the quality of mentor support, influence students’ clinical dishonesty more than intrapersonal factors (e.g., attitudes or knowledge). Contextual factors (healthcare employment and study overload) were unrelated to clinical dishonesty. This finding can help develop strategies to reduce nursing students’ dishonesty and improve patient safety. Full article

The problem of academic fraud has recently grown and includes plagiarism, the use of cellphones, copying from others, and writing by use of artificial intelligence (AI). The objective of this study was to examine the connection between academic dishonesty, class attendance, self-efficacy, and the use of digital tools. The study focused on higher-education students in Israel and included 121 participants. It was a mixed qualitative and quantitative study based on a structured questionnaire and on the previous literature. Studies showed that academic dishonesty increases when students fail to attend classes, have low self-efficacy, and attend classes remotely via communication platforms such as Zoom. In the current study, 50% of the participants reported that academic dishonesty was perceived as legitimate among their peer students. Preventive measures such as strengthening the students’ self-efficacy during tests and other stressful situations and emphasizing the importance of acquiring professional knowledge and skills may more effectively eliminate fraud than the common method of disciplining wrongdoers. Full article

In higher education, the cultivation of research competency is pivotal for students’ critical thinking development and their subsequent transition into the professional workforce. While statistics plays a fundamental role in supporting the completion of a research project, it is often perceived as challenging, particularly by students in majors outside mathematics or statistics. The connection between students’ statistical proficiency and their research competency remains unexplored despite its significance. To address this gap, we utilize the supervised machine learning approach to predict students’ research competency as represented by their performance in a research methods class, with predictors of students’ proficiency in statistical topics. Predictors relating to students’ learning behavior in a statistics course such as assignment completion and academic dishonesty are also included as auxiliary variables. Results indicate that the three primary categories of statistical skills—namely, the understanding of statistical concepts, proficiency in selecting appropriate statistical methods, and statistics interpretation skills—can be used to predict students’ research competency as demonstrated by their final course scores and letter grades. This study advocates for strategic emphasis on the identified influential topics to enhance efficiency in developing students’ research competency. The findings could inform instructors in adopting a strategic approach to teaching the statistical component of research for enhanced efficiency. Full article

The concept of “diversity dishonesty” has emerged as a pressing concern within highly selective and highly ranked schools of medicine, pharmacy, and public health at elite and highly ranked U.S. universities, particularly in the context of diversity, equity, and inclusion (DEI) efforts. This phenomenon, defined as a lack of sincerity and genuineness in an organization’s commitment to genuine and measurable change regarding diversity, raises significant questions about the authenticity of their endeavors. Organizations often engage in surface-level or performative actions related to diversity, creating the illusion of progress and dedication while failing to enact substantive and meaningful advancements in promoting diversity and inclusivity. This applied research inquiry uses a review of literature, research theories, and research frameworks to delve into the nuanced dynamics of diversity dishonesty, exploring how organizations demonstrate a commitment in form but not in substance. The practice of tokenism, where diverse individuals are hired and prominently featured in organizational materials but are not genuinely valued, emerges as a defining characteristic of diversity dishonesty. Moreover, when questions regarding the authenticity of their commitment arise, organizations may resort to gaslighting minorities, further exacerbating the issue. Recognizing the critical need to address diversity dishonesty, this article comprehensively explores frameworks to understand and combat this phenomenon. It seeks to engage with viable theories, problem-solving approaches, and contextual models that can illuminate the complex interplay of factors contributing to diversity dishonesty. By shedding light on the mechanisms through which elite and highly ranked predominantly White schools of medicine, pharmacy, and public health engage in performative acts without enacting transformative cultural change, this research aims to pave the way for more genuine and impactful DEI efforts and future research in this area. Full article

ChatGPT is a state-of-the-art generative artificial intelligence (AI) chatbot released by OpenAI in 2022. It simulates human conversation and has the capability to generate different texts at various levels of sophistication in near real time depending upon the user’s skill in creating prompts. While concerns have been raised about academic dishonesty and cheating among students, ChatGPT has significant academic potential for education, particularly in the field of language learning. This research explores the potential of ChatGPT in supporting and empowering Chinese language learners (CLLs) whose first language is English to enhance their writing skills, mainly focusing on the research question: Is there a functional relation between Chinese language learners from low‐income families using ChatGPT after school twice a week and improvements in their Chinese writing? Four participants with varying language proficiency levels were recruited, and their data were analyzed using an ABA design. Over three weeks, they utilized ChatGPT twice a week for approximately 20 min each after school. The students’ writing scores, writing samples, and learning reflections were used to triangulate the data and enhance the data’s trustworthiness. The findings indicate that (1) each participant made a noticeable improvement in their Chinese writing scores during the intervention and reversal phases; (2) ChatGPT played a crucial role in correcting errors and facilitating the development of complete sentence structures; and (3) the students expressed a sense of empowerment through their interactions with ChatGPT. These findings highlight that ChatGPT shows promise as a supportive tool for CLLs from low-income families, reducing educational inequality and promoting equitable access to language learning opportunities. Full article

The demand for online education is gradually growing. Most universities and other institutions are faced with the fact that it is almost impossible to track how honestly test takers take exams remotely. In online formats, there are many simple opportunities that allow for cheating and using the use of outside help. Online proctoring based on artificial intelligence technologies in distance education is an effective technological solution to prevent academic dishonesty. This article explores the development and implementation of an online control proctoring system using artificial intelligence technology for conducting online exams. The article discusses the proctoring systems used in Kazakhstan, compares the functional features of the selected proctoring systems, and describes the architecture of Proctor SU. A prototype of the Proctor SU proctoring system has been developed. As a pilot program, the authors used this system during an online university exam and examined the results of the test. According to the author’s examination, students have a positive attitude towards the use of Proctor SU online proctoring. The proposed proctor system includes features of face detection, face tracking, audio capture, and the active capture of system windows. Models CNN, R-CNN, and YOLOv3 were used in the development process. The YOLOv3 model processed images in real time at 45 frames per second, and CNN and R-CNN processed images in real time at 30 and 38 frames per second. The YOLOv3 model showed better results in terms of real-time face recognition. Therefore, the YOLOv3 model was implemented into the Proctor SU proctoring system. Full article

Both paper-based and computerized exams have a high level of cheating. It is, therefore, desirable to be able to detect cheating accurately. Keeping the academic integrity of student evaluations intact is one of the biggest issues in online education. There is a substantial possibility of academic dishonesty during final exams since teachers are not directly monitoring students. We suggest a novel method in this study for identifying possible exam-cheating incidents using Machine Learning (ML) approaches. The 7WiseUp behavior dataset compiles data from surveys, sensor data, and institutional records to improve student well-being and academic performance. It offers information on academic achievement, student attendance, and behavior in general. In order to build models for predicting academic accomplishment, identifying at-risk students, and detecting problematic behavior, the dataset is designed for use in research on student behavior and performance. Our model approach surpassed all prior three-reference efforts with an accuracy of 90% and used a long short-term memory (LSTM) technique with a dropout layer, dense layers, and an optimizer called Adam. Implementing a more intricate and optimized architecture and hyperparameters is credited with increased accuracy. In addition, the increased accuracy could have been caused by how we cleaned and prepared our data. More investigation and analysis are required to determine the precise elements that led to our model’s superior performance. Full article

The rapid shift from classroom course delivery to online education modalities during the COVID-19 pandemic has had significant impacts on academia. Student loss of face-to-face interaction, the lost social benefits of the educational milieu, and restricted instructor ability to control both the learning environment and assessment process have been significant. The purpose of this paper is to discover if due to the unplanned shift to online course delivery, educators and researchers experienced impacts to academic integrity during the peak of the online shift. A systemic review utilizing the PRISMA methodology of peer reviewed literature published during the period of March 2020 till September 2021 demonstrated that violation types continued to fall within the existing academic integrity constructs of inappropriate information sharing, cheating on exams and assignments, incidents of plagiarism, and falsifying or fabricating information. The results showed that pre-COVID concerns with academic integrity were amplified with previous concerns moving to the forefront. In addition, the rapid shift opened doors for greater opportunity for violations and increased instructor concern especially within the hard sciences and courses with lab-based components. Reinforcing the importance of providing formal academic integrity student and faculty training can be a beneficial intervention to ensure students understand the ethical implications of student behavior and performance during the assessment process. Given the emerging trend pre-COVID that skyrocketed during the pandemic, ensuring academic integrity should remain a key priority for learning institutions. Full article

Online learning has been an integral part of the educational process in universities, particularly during the COVID-19 pandemic. Despite the popularity of online learning, concerns exist over their level of academic integrity. The aim of this study is to investigate students’ attitudes and behavior towards academic dishonesty during online learning. In total, 319 undergraduate health sciences students at a public university took part in the survey. The online self-administered questionnaire was distributed through a social media platform. Data collected were analyzed using the Statistical Package for Social Sciences (SPSS) Version 25.0. Majority of the respondents perceived the indicated behavior as serious cheating. However, most respondents (86.2%) self-report that they have engaged in academically dishonest behaviour at least once for the past one year. Furthermore, approximately 77% (n = 246) of respondent has witnessed act of academic dishonesty among their friends for the past one year. Spearman correlation test revealed no association between students’ attitudes and behavior towards academic dishonesty during online learning. The result of this study, in summary, is that students perceive the indicated behaviors as serious cheating and have engaged in academically dishonest behaviors less frequently. Full article

The COVID-19 pandemic has completely transformed the education sector. Almost all universities and colleges have had to convert their normal classroom teaching to online/remote or hybrid teaching during the COVID-19 pandemic. Online teaching has been found quite useful during an emergency situation. This switch to online teaching forced educators to come out of their comfort zone and learn new tools and techniques for online teaching. It is important, therefore, to analyse the problems faced by educators in online teaching because this has become the new normal. There are several studies identifying the issues faced by educators in online teaching but less is known about the issues faced by Computer Science (CS) educators. In this paper, we perform an exploratory study of the problems, questions, and associated responses from CS educators posted on popular Q&A forums, e.g., CS educators StackExchange. We identified six main challenges related to online teaching: platform recommendation, Q&A management, academic dishonesty, pair programming, and feedback mechanism. Several recommendations are provided by other CS educators in each of the categories, which are discussed in detail in this paper. This study will help organizations come up with better solutions to support their educators so that they can deliver better quality education and reduce the overall stress levels of staff. Full article