Teaching Machine Learning to Undergraduate Electrical Engineering Students

Purpose: This analysis aims to propose guidelines for artificial intelligence (AI) research ethics in scientific publications, intending to inform publishers and academic institutional policies in order to guide them toward a coherent and consistent approach to AI research ethics.Methods: A literature-based thematic analysis was conducted. The study reviewed the publication policies of the top 10 journal publishers addressing the use of AI in scholarly publications as of October 2024. Thematic analysis using Atlas.ti identified themes and subthemes across the documents, which were consolidated into proposed research ethics guidelines for using generative AI and AI-assisted tools in scholarly publications.Results: The analysis revealed inconsistencies among publishers’ policies on AI use in research and publications. AI-assisted tools for grammar and formatting are generally accepted, but positions vary regarding generative AI tools used in pre-writing and research methods. Key themes identified include author accountability, human oversight, recognized and unrecognized uses of AI tools, and the necessity for transparency in disclosing AI usage. All publishers agree that AI tools cannot be listed as authors. Concerns involve biases, quality and reliability issues, compliance with intellectual property rights, and limitations of AI detection tools.Conclusion: The article highlights the significant knowledge gap and inconsistencies in guidelines for AI use in scientific research. There is an urgent need for unified ethical standards, and guidelines are proposed for distinguishing between the accepted use of AI-assisted tools and the cautious use of generative AI tools.

In today’s evolving world of work, Artificial Intelligence (AI) has emerged not merely as a technological tool but as a catalyst for rethinking how jobs are structured and performed (Bankins et al., 2024; Parker & Grote, 2022). Across diverse job families and organizational contexts, tools like generative AI (GAI) go beyond mere instruments for data analysis or structuring and can adopt increasingly far-reaching roles through rapid progress in generating new graphic or textual content or algorithmic decision making (Ferràs- Hernández, 2018; Walsh et al. 2019). Central to this symposium is the recognition that this changing nature of work due to digitalization, propelled by AI and related tools, calls for a fresh look at established theories of job design and strategies for human resource management. Traditionally, research has illuminated how fostering characteristics like autonomy, skill and task variety, or adequate demands affect employee motivation, satisfaction, and well-being and thereby further organizational outcomes (Oldham & Fried, 2016; Parker et al., 2017). Yet the introduction of AI – particularly generative AI – adds complexity: Who performs which tasks? How are decisions made, and by whom? Which skills remain uniquely human, and which may be augmented or even entirely adopted by AI? Such questions demand a more nuanced and strategic approach to designing work in the future that balances efficiency and innovation with psychological and ethical considerations. The four presentations in this symposium aim to contribute to theory building on how AI affects work in terms of work design, the interplay of employees and AI use, and work outcomes. In particular, they highlight potential risks or unique challenges when working with AI (e.g., in terms of less enriched work, lowered autonomy levels, threats to job identity or risks due to disclosure of use) and path the way for interventions to achieve benefits of such technologies at work. In detail, the empirical studies in this symposium illuminate these issues at three interconnected levels: 1. Task-Level Insights: Two contributions focus on the more granular task characteristics (e.g., autonomy, skill variety) in modern, AI-enhanced work contexts. They demonstrate how AI use influences the allocation of specific tasks when designing work roles and how the perception of task characteristics changes when AI is used in a decision-making task. 2. Job- and Identity-Level: One paper zooms out from individual tasks to jobs as a whole and explores how AI-driven change might disrupt employees’ job identity. This lens demonstrates how task changes cascade into bigger questions of role definition and identity. 3. Organizational Context: Finally, one presentation adopts the broadest perspective and investigates managerial and organizational factors that might shape employees’ willingness to disclose and ethically integrate AI tools. Taken together, these presentations point toward a future in which AI’s transformative potential hinges on thoughtful coordination across tasks, roles, and organizational systems. By combining established frameworks with new insights drawn from (quantitative and qualitative) surveys and experiments conducted in Germany, Austria, the United States, the United Kingdom, and Australia, these contributions illustrate how AI can enrich, rather than diminish, work and thereby strengthen human-centered organizations. By focusing on variables at different levels, the studies path the way for applications in practice, emphasizing the importance of individual approaches (e.g., training for job design, supporting self-efficacy), or on the organizational level (e.g., aligning HR practices, develop clear and supportive organizational policies and guidelines for the use of AI at work). Investigating the Influence of Work Design Perspective and AI Integration on Enriched Job Design Author: Tanja Bipp; Heidelberg University Author: Thea Ebert; Aalen University Collaborative Intelligence: Integrating Generative AI in Knowledge Work and its Impact on Job Design Author: Sabina Hodzic; University of Graz Author: Simon Grob; University of Graz Author: Bettina Kubicek; University of Graz Job Identity as Starting Point for SHRM in AI-Driven Organizational Change Author: Sophie Berretta; Ruhr University Bochum Author: Annette Kluge; Ruhr University Bochum Silent Adoption: Understanding the Drivers of Non-Disclosure of Generative AI Tools by Employees Author: Fangfang Zhang; Curtin University - Perth

In the dynamic field of programming education, integrating artificial intelligence (AI) tools has started to play a significant role in enhancing learning experiences. This paper presents a case study conducted during a foundational programming course for first-year students in higher education, where students were encouraged to utilize generative artificial intelligence programming copilot extensions in their programming IDE and browser-based generative AI tools as supportive AI tools. The primary objective was to observe the impact of AI on the learning curve and the overall educational experience.Key findings suggest that the introduction of AI tools significantly altered the learning experience for students. Many who initially struggled with grasping elementary programming concepts found that AI support made understanding basic programming concepts much easier, enhancing their confidence and skills. This was particularly evident in the reduced levels of anxiety typically associated with early programming learning, as the AI copilot provided a non-judgmental, always-available source for clarifying doubts, including queries that students might hesitate to ask in a traditional classroom setting.Notably, some students leveraged the AI to generate similar exercise problems, reinforcing their understanding and skills. The AI's capability to address basic queries also freed up the instructor's time, allowing for more personalized student guidance in more advanced problems. This shift in the instructional dynamic further contributed to a learning environment where students felt more comfortable engaging with complex topics, thereby reducing the psychological barriers often linked with early-stage programming education.The course's structure, enriched by AI, enabled students to delve into more complex programming constructs earlier than traditional curricula would allow. For instance, students were tasked with simulating basic e-commerce operations, such as user registration, product browsing, and cart functionalities. These practical challenges naturally introduced advanced concepts like external data storage, unit testing, and user interface design, which are typically reserved for more advanced courses. With the help of generative AI programming copilot tools, students at any programming skill level were able to develop nearly functional complex structures. Interestingly, even when their projects were not fully functional, students remained motivated. Instead of feeling discouraged by these imperfect outcomes, they showed resilience and a keen interest in understanding and improving their code. This reaction is a significant shift from traditional learning settings, where unfinished or flawed projects often lead to increased anxiety or a drop in motivation.Furthermore, the AI's proactive suggestions inspired students to explore beyond the curriculum. Advanced learners delved into databases, cryptography libraries in Python, and even more advanced user interface design, ensuring that they remained engaged and challenged. This elementary course, enhanced by generative AI tools, also inspired students to learn other programming languages since they now learned that individual learning is more available with the aid of generative AI.In conclusion, the integration of AI in programming education offers a promising avenue for enhancing both the learning experience and outcomes. This case study underscores the potential of AI to revolutionize traditional teaching methodologies, fostering a more dynamic, responsive, and inclusive learning environment.This paper handles the results, possibilities and challenges of AI empowered education in programming. It also gives practical examples as well as future research perspectives.

Background Histological remission is an important target for Ulcerative Colitis (UC) treatment; however, scoring of histological images is time-consuming and prone to inter and intra-observer variability. Thus, a need exists for an accurate, reproducible, and reliable automated method. Previously, we demonstrated an Artificial Intelligence (AI) Tool using image processing and machine learning algorithms to measure histological disease activity using the Nancy index consistently and accurately.1 Here, we aim to enhance the capabilities of the AI Tool, by adding substantially more population-diversified training data while maintaining accuracy and robustness of results. Methods Eight global sites submitted 600 UC histological images. These were added to the 200 images previously used to train and validate the AI Tool. The 800-image dataset was divided into 2 groups: 90% used for training, 10% for testing. The novel AI algorithms were trained using state-of-the-art image processing and machine learning techniques based on deep learning and feature extraction. Cell and tissue regions of each training image were manually annotated, measured, and assigned a Nancy Index independently by 3 histopathologists, and used to further train the AI using over 43,000 characterisations. The AI Tool fully characterises histological images, identifying tissue types, cell types, cell numbers and locations, and automatically measures the Nancy Index for each image. Intra Class Correlation (ICC) and Confusion Matrix analyses were performed to evaluate the AI Tool and assess accuracy. Results The average ICC was 92.1% among the histopathologists and 91.1% between histopathologists and AI Tool, compared with 88.3% and 87.2% in the previous study.1 Confusion matrix analysis (Table 1) demonstrated the strongest correlation at the extremes of the Nancy Index, with 80% correlation between predicted and true labels for Nancy Scores of 0 or 4. When 2 adjacent scores were combined, correlations were stronger: 96% for a true Nancy score of 0 being predicted as 0 or 1, and 100% for a true Nancy score of 2 being predicted as 2 or 3. Conclusion By adding a larger number of images to the AI Tool training data, the robustness of the AI Tool was substantially improved while maintaining accuracy. The continued high correlation of AI Tool performance with the histopathologists reinforces the potential role for the AI Tool for IBD clinical applications. Fully characterising whole slides could standardise and validate an AI-driven scoring system for histology slides in IBD, eliminating the subjectivity of the human pathologist in assessment of disease activity.

Technologically advanced artificial intelligence (AI) is making a significant contribution to strengthening national security. AI algorithms facilitate the processing of vast amounts of information, increasing the speed and accuracy of decision-making. Artificial intelligence and machine learning (AI/ML) are crucial for state and integrated hybrid attacks and protecting new threats in cyberspace. Existing AI capabilities have significant potential to impact national security by leveraging existing machine learning technology for automation in labor-intensive activities such as satellite imagery analysis and defense against cyber attacks. This article examines selected aspects of the impact of artificial intelligence on enhancing a state’s ability to protect its interests and its citizens., artificial intelligence through the use of neutron networks, predictive analytics and machine learning algorithms enables security agencies to analyse vast amounts of data and identify patterns indicative of potential threats. Integrating artificial intelligence into surveillance, border control and threat assessment systems enhances the ability to respond preemptively to security challenges. In addition, artificial intelligence algorithms facilitate the processing of vast amounts of information, increasing the speed and accuracy of decision-making by police authorities. The rapid development of AI raises a number of questions for its use in securing not only national security but protecting all citizens. In particular, it is worth answering the question How does artificial intelligence affect national security and clarifying the issue of how law enforcement agencies can use artificial intelligence to maximise the benefits of the new technology in terms of security and protecting communities from rising crime. The analysis is based on a descriptive method in describing the phenomenon; by explaining the concepts and applications of artificial intelligence to determine its role in the national security sphere. An analysis of the usefulness of artificial intelligence in particular in police operations is undertaken, with the aim of defending the thesis that, despite some threats to the protection of human rights from AI, it is becoming the best tool in the fight against all types of crime in the country. Technological advances in AI can also have many positive effects for law enforcement, and useful for law enforcement agencies, for example in facilitating the identification of persons or vehicles, predicting trends in criminal activities, tracking illegal criminal activities or illegal money flows, flagging and responding to fake news. Artificial intelligence (AI) has emerged as one of the biggest threats to information security, but efforts are being made to mitigate this new threat, but also to find solutions on how AI can become an ally in the fight against cyber-security, crime and terrorist threats. Artificial intelligence algorithms search huge datasets of communication traffic, satellite images and social media posts to identify potential cyber security threats, terrorist activities and organized crime. It is advisable, when analyzing the opportunities and threats that AI poses to national and public security, to gain a strategic advantage in the context of rapid technological change and also to manage the many risks associated with AI. The conclusion highlights the impact of AI on national security, creating a range of new opportunities coupled with challenges that government agencies should be prepared for in addressing ethical and security dilemmas. Furthermore, AI improves predictive analytics, thereby enabling security agencies to more accurately anticipate potential threats and enhance their preparedness by identifying vulnerabilities in the national security infrastructure

Early detection of disease and accurate assessment of its extent are of paramount importance for the course of treatment and prognosis of larynx cancer. Machine learning and artificial intelligence tools have the potential to accelerate and improve diagnostic procedures in medicine, as well as to predict disease outcomes and response to specific therapies. Computer algorithms can analyze two-dimensional images obtained during procedures such as laryngeal spectroscopy and endoscopy. Radiological images can be evaluated using appropriate algorithms to determine whether the laryngeal tissue is benign or malignantly altered. In recent years, machine learning tools have been developed to determine the precise radiation doses, predict tumor radiosensitivity, as well as the possibility and severity of complications based on radiological image analysis. In the field of pathology, significant progress has been made by creating digital records of histopathological preparations, which can be further analyzed. This allows changes in intercellular interaction and tissue architecture that cannot be detected by conventional microscopic methods to be identified. With innovative computer techniques, it is possible to quantify tissue and cell structure parameters, which are calculated based on mathematical formulas and used to measure structural homogeneity and uniformity in both normal and pathologically altered tissue. Future multidisciplinary research aimed at developing new and innovative biosensors for the detection of discrete morphological changes characteristic of squamous cell carcinoma of the larynx will make a significant contribution to the advancement of diagnosis and treatment in the field of otolaryngology. In the future, the use of artificial intelligence and machine learning could enable the fusion of algorithms that combine data obtained from radiological, endoscopic, and histopathological findings, which could significantly increase the accuracy and precision of diagnosis, facilitate the process of deciding on therapeutic options, and improve the success rate of larynx cancer treatment.

This study presents results of a systematic literature review (SLR) of the responsible use of artificial intelligence (AI) tools in higher education, identify patterns of ethical and irresponsible use, and propose a conceptual framework for predicting ethical AI adoption. Following PRISMA guidelines, was conducted on 60 peer-reviewed studies published between 2022 and 2026, sourced from Google Scholar. Studies were mapped against four research questions addressing AI tools used, their applications, reported unethical practices, and predictive modelling approaches. Results reveal that general AI, generative AI tools, and large language models dominate higher education contexts, primarily deployed for personalized learning, academic work, and teaching. Irresponsible practices were documented in one-third of studies, including academic integrity breaches (13.33%), algorithmic bias, and privacy violations. Critically, no existing study developed a real-time predictive model capable of monitoring ethical AI use, despite four studies demonstrating predictive modelling capabilities for other purposes. This study addresses a significant gap by proposing a novel conceptual framework that integrates AI tool deployment, user behaviour, governance measures, and predictive analytics to forecast ethical outcomes. The framework provides higher education institutions with a pathway toward data-informed, proactive governance of AI technologies.

Instructional writing has long been at the forefront of research, with the advent of artificial intelligence (AI) tools such as ChatGPT. Understanding teachers’ perceptions of ChatGPT's use for teaching English writing skills has become increasingly significant. The present study explores Pakistani English teachers’ perceptions of using ChatGPT to teach writing skills to college-level students. Using a qualitative research design, the study involved 15 faculty members from a government college in Pakistan who participated in semi-structured interviews. The study found that teachers perceived ChatGPT as a valuable tool for developing vocabulary, syntactic structure, paragraph writing, and emotional intelligence. The results also showed a potential increase in the students’ involvement while reducing writing anxiety. However, concerns were rising about the overreliance on AI tools, less academic creativity, diminished critical thinking, and a lack of AI-assisted writing classrooms. Further, the findings suggest a balanced approach towards implementing ChatGPT favors creative writing skills, organization, and accuracy. This study recommends practical strategies and provides clear instructions for using AI tools. Moreover, the Technology Acceptance Model (TAM) highlights perceived usefulness, influence, and challenges as key determinants of TAM acceptance. The participants emphasized the need to prompt teachers’ training programs, provide scaffolding, and integrate ChatGPT and other AI tools for effective writing instruction, focusing on structured guidelines to minimize risks and maximize outcomes. This research contributes to contemporary debates on the use of AI tools to support digital transformation in education and provides an actionable plan for integrating AI tools into the English writing classroom.

The rapid development of generative artificial intelligence (AI) tools, particularly large language models such as ChatGPT, has generated significant debate within the academic publishing community. This study examines emerging research trends and discussions surrounding the use of generative AI in scientific writing and publishing. Using a bibliometric approach, the study analyses 311 documents indexed in the Scopus database and published between 2023 and 2025. The dataset was processed using the Bibliometrix R package to identify publication trends, frequently used keywords, and thematic relationships. Visualisation techniques, including a word cloud and a keyword co-occurrence network, were employed to map the conceptual structure of the field. The analysis reveals three primary thematic clusters: a technological cluster focused on AI tools and scientific writing, a biomedical-ethical cluster addressing clinical and educational implications, and a publishing-integrity cluster concerned with research ethics, authorship, plagiarism, and peer review. The findings indicate that debates about generative AI in academic publishing are strongly connected to issues of transparency, accountability, and the reliability of scholarly communication. The study highlights the importance of clear editorial policies, responsible AI use, and disclosure practices to maintain research integrity while supporting accessibility and linguistic equity in academic publishing.

RETRACTED: Artificial intelligence enterprise human resource management system based on FPGA high performance computer hardware

The advent of artificial intelligence (AI) presents an opportunity to enhance the management of multiple medications for patients. Despite the growing body of research on the clinical impact and accuracy of AI in healthcare, current literature is limited in addressing AI’s role in medication management for older adults. The goal of this scoping review was to assess the extent and applicability of AI applications in managing multiple pharmacological therapies among adults aged 50 and older. Relevant articles were searched in EMBASE, Ovid MEDLINE, and Web of Science databases. Search phrases included “polypharmacy/polytherapy”, “artificial intelligence in healthcare”, “machine learning/language learning”, and “elderly populations”. The search initially identified 58 citations. After a systemized and rigorous screening process, 12 articles were further screened for eligibility and critically appraised for bias and appropriateness. Of those, a total of five articles were retained for the final analysis. They focused on the application of AI and web-based applications to reduce inappropriate medication use and drug interactions, how machine learning and audio-based activity recognition systems could improve medication adherence among older adults, and recognizable patterns between multimorbidity and polypharmacy in elderly populations with chronic illnesses. The main findings of this review suggested that AI tools have demonstrated efficiency and accuracy in eliminating drug-drug interactions, assisting in the detection of potentially inappropriate medications (PIMs), and identifying patterns of multimorbidity due to polypharmacy in older adults. Moreover, AI tools were considered easy to use and helped enhance medication adherence. Results suggest that AI has potential in managing polypharmacy, particularly in enhancing medication safety, improving adherence, and predicting risk factors for medication-related errors in older adults. Future investigations are needed that focus on AI’s long-term effect on patient outcomes, its role in personalized pharmacotherapy, and ongoing challenges related to algorithmic transparency, bias mitigation, and regulatory oversight. As AI continues to evolve, its integration into healthcare would benefit from methodical evaluation, stringent oversight, and interdisciplinary collaboration to ensure its safe and effective deployment in patient care.

The COVID-19 pandemic came with disruptions in every aspect of human existence, with all the sectors of the economies of the world affected greatly. In the health sector, the pandemic halted and reversed progress in health and subsequently shortened life expectancy, especially in developing and underdeveloped nations. On the other hand, machine learning and artificial intelligence contributed a great deal to the handling of the pandemic globally. Therefore, the current study aimed to assess the role played by artificial intelligence and machine learning in addressing the dangers posed by the COVID-19 pandemic, as well as extrapolate the lessons on the fourth industrial revolution and sustainable development goals. Using qualitative content analysis, the results indicated that artificial intelligence and machine learning played an important role in the response to the challenges posed by the COVID-19 pandemic. Artificial intelligence, machine learning, and various digital communication tools through telehealth performed meaningful roles in scaling customer communications, provided a platform for understanding how COVID-19 spreads, and sped up research and treatment of COVID-19, among other notable achievements. The lessons we draw from this is that, despite the disruptions and the rise in the number of unintended consequences of technology in the fourth industrial revolution, the role played by artificial intelligence and machine learning motivates us to conclude that governments must build trust in these technologies, to address health problems going forward, to ensure that the sustainable development goals related to good health and wellbeing are achieved.

This study investigates the initial response of Chinese higher education institutions to generative artificial intelligence (AI) tools by analyzing social media posts from top universities. As generative AI, such as ChatGPT, transforms education and knowledge production, universities face the challenge of integrating these technologies while addressing concerns about academic integrity and pedagogical impact. Using a qualitative content analysis of social media posts from 30 leading Chinese universities, this paper identifies the primary themes, opportunities, and challenges discussed. The findings highlight three major themes: awareness and guidance, ethical considerations, and academic innovation. Universities emphasize the need to educate stakeholders about AI tools, ensure ethical use, and leverage AI to innovate teaching and research. This study provides valuable insights into how higher education in China is adapting to generative AI, offering practical implications for policymakers, educators, and researchers navigating this evolving landscape.

- The rapid technological developments in recent decades provide most effective expertise business environment for the Entrepreneurs. In this digital era, the Entrepreneurs need to respond quickly to the increasing demands from the consumers. Artificial intelligence (AI) is a dominant technology with machine learning capabilities, which are currently used in all the business sectors. A recent trend in Artificial Intelligence highly concentrates on the development of machine learning based applications which change the landscape of business industry. The emerging AI tools handles massive amount of business data efficiently and facilitate almost every business operation which in turn increases productivity. Eventually, the advance in technology also brings several business risks and challenges. This paper briefly describes the impact of Artificial Intelligence in Business and explores latest Artificial Intelligence applications and tools available in business environment which lowers the operational expenses and become boon for Entrepreneurs. This paper also discusses some of the major challenges in business and supportive AI tools which offers operative solution to overcome these challenges particularly in the three major business fields namely Finance, Marketing and Human Resources, as well as to improve the business efficiency. Keywords: Artificial Intelligence, Business Challenges, AI Tools, Finance, Marketing, Human Resources.

As generative artificial intelligence (AI) tools and large language models (LLMs)-powered applications develop rapidly in the era of algorithms, it should be integrated thoughtfully to enhance English as a Foreign Language (EFL) teaching and learning without replacing learners’ critical thinking (CT). This study systematically analyzes the impact of generative AI tools and LLMs on language learners’ CT in EFL education using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework to identify, evaluate, and synthesize relevant studies from 2022 to 2025. A thorough review of 15 selected studies focuses on generative AI tools and LLMs’ dual nature, research methods, main focuses, theory and models, limitations and challenges, and future directions in the field based on Web of Science (WoS), SCOPUS, ERIC, ProQuest, and Google Scholar. The findings identified generative AI tools and LLMs possessed both the potential to nurture and the risk of hindering CT in EFL education. 66.67% of studies reported generative AI tools and LLMs’ positive role in CT, while 33.33% of studies reported its negative role in CT. Furthermore, 3 types of research methods, 3 key themes of research focus, and 4 groups of theoretical perspectives were examined. However, 4 kinds of limitations in this field remain, including research scope, user dependency, generative AI reliability, and pedagogical integration. Future research can focus on assessing long-term effects, broadening research scope, promoting responsible AI use, and refining pedagogical strategies. Finally, Limitations, implications and future direction of this study were discussed.