Artificial Intelligence Technology Research Articles

Fast Detection and Classification of Microplastics below 10 μm Using CNN with Raman Spectroscopy.

In light of the growing awareness regarding the ubiquitous presence of microplastics (MPs) in our environment, recent efforts have been made to integrate Artificial Intelligence (AI) technology into MP detection. Among spectroscopic techniques, Raman spectroscopy is preferred for the detection of MP particles measuring less than 10 μm, as it overcomes the diffraction limitations encountered in Fourier transform infrared (FTIR). However, Raman spectroscopy's inherent limitation is its low scattering cross section, which often results in prolonged data collection times during practical sample measurements. In this study, we implemented a convolutional neural network (CNN) model alongside a tailored data interpolation strategy to expedite data collection for MP particles within the 1-10 μm range. Remarkably, we achieved the classification of plastic types for individual particles with a mere 0.4 s of exposure time, reaching an approximate confidence level of 85.47(±5.00)%. We postulate that the result significantly accelerates the aggregation of microplastic distribution data in diverse scenarios, contributing to the development of a comprehensive global microplastic map.

The role of foreign MNEs in China’s twin transition: a study on the organization of green and digital innovation processes

Purpose The purpose of this study is to shed light on the twin transition in China in the organization of innovation processes in artificial intelligence (AI) and green technology (GT) development and to understand the role of foreign multinationals in Chinese innovation systems. Design/methodology/approach A qualitative research approach is used by interviewing executives from German multinationals with expertise in AI and GT development and organization of innovation processes in China. In total, 11 semi-structured interviews were conducted with companies, and the data were analysed with a thematic qualitative text analysis. Findings The findings show that AI applications for GT are primarily developed in cross-company projects that are led by local and regional authorities through the organization of industrial districts and clusters. German multinationals are either being integrated, remaining autonomous or being excluded from these twin transition innovation processes. Originality/value This paper aims to fill the gap in the literature by providing one of the first qualitative approach towards twin transition innovation processes in China and exploring the integration of multinational enterprises in cluster organizations. To the best of the author’s knowledge, this is one of the first twin transition studies from this perspective in emerging economies.

Bridging knowledge gap: the contribution of employees’ awareness of AI cyber risks comprehensive program to reducing emerging AI digital threats

Purpose In the modern digital realm, while artificial intelligence (AI) technologies pave the way for unprecedented opportunities, they also give rise to intricate cybersecurity issues, including threats like deepfakes and unanticipated AI-induced risks. This study aims to address the insufficient exploration of AI cybersecurity awareness in the current literature. Design/methodology/approach Using in-depth surveys across varied sectors (N = 150), the authors analyzed the correlation between the absence of AI risk content in organizational cybersecurity awareness programs and its impact on employee awareness. Findings A significant AI-risk knowledge void was observed among users: despite frequent interaction with AI tools, a majority remain unaware of specialized AI threats. A pronounced knowledge difference existed between those that are trained in AI risks and those who are not, more apparent among non-technical personnel and sectors managing sensitive information. Research limitations/implications This study paves the way for thorough research, allowing for refinement of awareness initiatives tailored to distinct industries. Practical implications It is imperative for organizations to emphasize AI risk training, especially among non-technical staff. Industries handling sensitive data should be at the forefront. Social implications Ensuring employees are aware of AI-related threats can lead to a safer digital environment for both organizations and society at large, given the pervasive nature of AI in everyday life. Originality/value Unlike most of the papers about AI risks, the authors do not trust subjective data from second hand papers, but use objective authentic data from the authors’ own up-to-date anonymous survey.

Novel milestones for early esophageal carcinoma: From bench to bed

Esophageal cancer (EC) is the seventh most common cancer worldwide, and esophageal squamous cell carcinoma (ESCC) accounts for the majority of cases of EC. To effectively diagnose and treat ESCC and improve patient prognosis, timely diagnosis in the initial phase of the illness is necessary. This article offers a detailed summary of the latest advancements and emerging technologies in the timely identification of ECs. Molecular biology and epigenetics approaches involve the use of molecular mechanisms combined with fluorescence quantitative polymerase chain reaction (qPCR), high-throughput sequencing technology (next-generation sequencing), and digital PCR technology to study endogenous or exogenous biomolecular changes in the human body and provide a decision-making basis for the diagnosis, treatment, and prognosis of diseases. The investigation of the microbiome is a swiftly progressing area in human cancer research, and microorganisms with complex functions are potential components of the tumor microenvironment. The intratumoral microbiota was also found to be connected to tumor progression. The application of endoscopy as a crucial technique for the early identification of ESCC has been essential, and with ongoing advancements in technology, endoscopy has continuously improved. With the advancement of artificial intelligence (AI) technology, the utilization of AI in the detection of gastrointestinal tumors has become increasingly prevalent. The implementation of AI can effectively resolve the discrepancies among observers, improve the detection rate, assist in predicting the depth of invasion and differentiation status, guide the pericancerous margins, and aid in a more accurate diagnosis of ESCC.

Enhancing risk management in hospitals: leveraging artificial intelligence for improved outcomes

In hospital settings, effective risk management is critical to ensuring patient safety, regulatory compliance, and operational effectiveness. Conventional approaches to risk assessment and mitigation frequently rely on manual procedures and retroactive analysis, which might not be sufficient to recognize and respond to new risks as they arise. This study examines how artificial intelligence (AI) technologies can improve risk management procedures in healthcare facilities, fortifying patient safety precautions and guidelines while improving the standard of care overall. Hospitals can proactively identify and mitigate risks, optimize resource allocation, and improve clinical outcomes by utilizing AI-driven predictive analytics, natural language processing, and machine learning algorithms. The different applications of AI in risk management are discussed in this paper, along with opportunities, problems, and suggestions for their effective use in hospital settings.

Medical, dental, and nursing students' attitudes and knowledge towards artificial intelligence: a systematic review and meta-analysis.

Nowadays, Artificial intelligence (AI) is one of the most popular topics that can be integrated into healthcare activities. Currently, AI is used in specialized fields such as radiology, pathology, and ophthalmology. Despite the advantages of AI, the fear of human labor being replaced by this technology makes some students reluctant to choose specific fields. This meta-analysis aims to investigate the knowledge and attitude of medical, dental, and nursing students and experts in this field about AI and its application. This study was designed based on PRISMA guidelines. PubMed, Scopus, and Google Scholar databases were searched with relevant keywords. After study selection according to inclusion criteria, data of knowledge and attitude were extracted for meta-analysis. Twenty-two studies included 8491 participants were included in this meta-analysis. The pooled analysis revealed a proportion of 0.44 (95%CI = [0.34, 0.54], P < 0.01, I2 = 98.95%) for knowledge. Moreover, the proportion of attitude was 0.65 (95%CI = [0.55, 0.75], P < 0.01, I2 = 99.47%). The studies did not show any publication bias with a symmetrical funnel plot. Average levels of knowledge indicate the necessity of including relevant educational programs in the student's academic curriculum. The positive attitude of students promises the acceptance of AI technology. However, dealing with ethics education in AI and the aspects of human-AI cooperation are discussed. Future longitudinal studies could follow students to provide more data to guide how AI can be incorporated into education.

IMPACT OF GENERATIVE AI ON EDUCATIONAL SECTOR

This study looks into how generative artificial intelligence (AI) is affecting the field of education. The study examines the possible advantages of using generative AI into instruction, such as more individualized learning opportunities, enhanced accessibility, and creative teaching strategies. The study also recognizes and examines the moral issues raised by the application of AI in education, highlighting the significance of upholding academic honesty and openness. It draws attention to the necessity of cautious application in order to guarantee that AI fosters fruitful learning results. The suggested research technique describes a mixed methods strategy that combines quantitative data analysis with qualitative information from surveys and interviews. The goal of the project is to provide best practices and standards for the appropriate integration of generative AI in education, while also identifying the potential and problems that this technology presents. The anticipated results include a thorough comprehension of how generative AI will affect education and how it might improve learning opportunities, ethical issues for responsible usage, and the identification of possible problems and solutions. In the end, the research hopes to promote a more inclusive and productive learning environment by aiding in the creation of useful frameworks for incorporating AI into educational settings. The education industry has drastically evolved in recent years, thanks to the remarkable advancements made by artificial intelligence. With the integration of generative AI technology, educators and students are now presented with a wealth of possibilities and opportunities. As AI continues to advance further, it holds great potential for revolutionizing education as we know it through improved efficiency measures that personalize learning experiences while enhancing overall student outcomes. Harnessing this power-packed tool in teaching environments creates an opportunity for educational institutions across various sectors allowing them efficient intelligent assessment tools coupled with personalized tutoring provisions towards addressing classroom weaknesses resulting in custom-made lesson plans aimed at betterment of individual academic progress developed using predictive models creating ample time such that teachers can focus solely on providing tailored instruction &amp; support based exclusively around their Student's requirements&amp; specifications. This groundbreaking technological development provided meaningfully leveraging Generative Intelligence efforts promises nothing short but will be instrumental when shaping how Education is structured from henceforth enabling automation capabilities within the Administrative tasks management system,&amp; content-building-The advantages come twofold-Enhancement not only resides generally concerning Upgrade Learning Experience situations; there exist tailor solutions projecting Speaker understanding regarding distinctive needs pupils have always had throughout all levels ultimately leading to Developmental Progression heights hitherto attainable anticipated sooner than later.

Form Perfector

Home-based workouts are known to have significant health and fitness benefits, but it could be dangerous if not performed with proper posture. This could be due to a lack of sufficient training and bad habits, or the inability to afford a personal trainer to fix the individual's posture. With the current Artificial Intelligence technology, we are now able to achieve workout analysis by using computer vision approach without any involvement of personal trainer. This report discussed the approach of keypoint detection, human pose estimation and pose analysis that will be utilized to produce the proposed project through Rapid Application Development (RAD) methodology, which will monitor these procedures phase by phase. Pretrained model from MediaPipe was chosen as the main human key point detection, and various machine learning algorithms such as Linear Regression and Random Forest were studied to find the suitable algorithm to be implemented in detecting and analyze home workout postures. Furthermore, this research also studies on geometrical approach to determining proper and improper posture for analysis. These approaches were then compared to see which will produce the best results and user experience. Since machine learning approach does not produce the best result due to lack of training data and variety of other factors, the geometrical approach was chosen as the final approach before implementing it into desktop application. Keyword: Personal Trainer, Keypoint Detection, MediaPipe, Rapid Application Development.

The Impact of AI on Agriculture and Farmers

Artificial Intelligence (AI) has emerged as a transformative force in the field of agriculture, offering innovative solutions to enhance productivity, sustainability, and efficiency. This research paper explores the multifaceted impact of AI on agriculture and farmers, encompassing various aspects such as precision farming, crop monitoring, livestock management, supply chain optimization, crop hybridization, and the development of AI tools and technology. Additionally, it delves into the opportunities AI presents for farmers and its potential role in advancing organic farming practices. Through a comprehensive analysis of existing literature and case studies, this paper aims to provide insights into the evolving landscape of agriculture facilitated by AI technologies.

Impressing Artificial Intelligence: Automated Job Interview Training in Professional English Subjects

More organizations are using automated job interview platforms to screen candidates at the early stages of the recruitment process. These platforms enable job candidates to take automated video interviews remotely using their personal devices. The interviews are analysed by artificial intelligence (AI) powered algorithms to produce analytics which inform hiring decisions. In this article, we explore the use of automated job interviewing within professional English subjects delivered to undergraduate students in Hong Kong. The innovation was introduced to help students to keep up with recruitment practices, provide speaking practice and explore AI-powered evaluation as a form of feedback. As well as outlining how automated interviewing was integrated into our teaching practice, we discuss student feedback on the experience and offer suggestions for future teaching practice with this technology. We also highlight considerations for practitioners such as the acceptance and adoption of AI technologies into language learning provision and the development of AI literacy skills. AI technologies have much potential for language teaching and this article offers a practical exploration into one such technology: automated job interviewing.

Security Situation Awareness System Based on Artificial Intelligence

There is rapid growth of the security threats faced by enterprises and the security attacks technology is also established at a higher level. Enterprises are facing an escalating threat landscape marked by sophisticated security attacks. To address the structural and technical challenges of information security situational awareness, a method for designing an artificial intelligence-driven system is proposed. In order to solve the system structure and key technical problems of information security situational awareness technology of artificial intelligence, a method of designing information security situational awareness system is proposed, and experiments are carried out through the method. By analyzing the data sources that the system needs to collect, including network traffic mirror data, log data, security intelligence and support data, this paper verifies the feasibility of the system method of information security situational awareness technology of artificial intelligence technology. The proposed core competence platform has the characteristics of low delay and robust real-time capabilities. By presetting the event processing topology, we can quickly build the event processing process, and build the corresponding event processing topology model according to different processing requirements to meet the business requirements. The AI-powered information security situational awareness system substantially enhances security awareness and prediction accuracy.

Knowledge Graph Analysis for Chronic Diseases Nursing based on Visualization Technology and Literature Big Data

The use of knowledge graph analysis for chronic disease nursing based on visualization technology and literature big data is an unexplored area of research in this field of study. To uncover research hotspots and developmental trends in the field of chronic disease nursing, and to provide a scholarly reference, we employed mathematical and statistical methods along with CiteSpace literature visualization analysis software for quantitative analysis of extensive literature data from the Web of Science Core Collection. We examined aspects such as publication trends, journals, author collaborations, research institutions, national and regional distributions, keyword co-occurrence, clustering, time zones, emergence, literature co-citations, and more. These analyses identified the current hotspots and future directions for research. Notably, scholars' interest in chronic disease nursing exhibited a consistent upward trajectory. In particular, the field of artificial intelligence technology application in nursing yielded $3,610$ published papers in $141$ journals with more than or equal to $10$ published papers on the topic, accounting for $58.41 \%$ of the total number of published papers in this field of study. Furthermore, the top three publishers were the “Journal of Clinical Nursing,” “Journal of Advanced Nursing,” and “BMC Health Services Research.” Among authors, Hu, Frank B., Willett, Walter C., and Rimm, Eric B., ranked as the top three, and 12 authors had more than 10 publications. The most active research institutions included Harvard University, Harvard Medical School, Brigham &amp; Women’s Hospital, University of California System, University of London, US Department of Veterans Affairs, Veterans Health Administration (VHA), Harvard T. H. Chan School of Public Health, University of Sydney, and the University of Toronto. The United States, Australia, England, China, Canada, Netherlands, Spain, Italy, Sweden, and Germany emerged as the leading countries in terms of research output, while emerging hotspots encompassed topics such as incidence, rheumatoid arthritis, qualitative research, burnout, kidney transplantation, critical illness, COVID-19, Sars-COV-2, public health, and the well-being of medical staff. These findings present valuable insights for prospective research endeavors.

The Impact of Artificial Intelligence on Unemployment and Future Work

The rapid development of artificial intelligence should also affect the world market. Unemployment is ex- pected to increase as artificial intelligence can perform tasks performed by humans in many different sectors. The eight- year study (2010-2017) investigates the impact of artificial intelligence (AI) on unemployment in 26 developed and devel- oping countries. More precisely, this study uses data analysis techniques to investigate how the use of artificial intelligence technology affects unemployment in 26 different sectors. The results of a data study compiling World Bank and OECD data show a positive relationship between advances in artificial intelligence and unemployment. The advancement of artificial intelligence is determined by patent information. Due to the differences in artificial intelligence development between the countries studied, artificial intelligence development has been shown to have a positive impact on the unemployment rate in the studied countries. Additionally, compared to younger workers, older workers with higher education may have less impact on employment due to improved skills. Finally, re- search and development (R&amp;D) spending varies widely across countries, making it difficult to aggregate. Therefore, this study does not show the relationship between the share of R&amp;D expenditures in GDP and how artificial intelligence affects unemployment. One of the most talked about topics in the public today is intelligence. Many people associate this with changes in the economy, especially changes in the economy that are generally expected to cause high unemployment. As artificial intelligence becomes more widespread, the public tends to predict which professions will become obsolete and eventually disappear. The healthcare sector is one of the most challenged sectors outside of production due to various medi- cal treatment studies. There are other scientists who disagree with this bad proposition. They argue that the emergence of artificial intelligence will create new jobs, just like all previous economic revolutions. Keywords-- Artificial Intelligence, Unemployment, Future Work, Impact, Automation, Job Displacement, Skills Gap, Job Creation, Manufacturing, Retail, Healthcare, Reskilling, Upskilling, Policy, Regulation, Mitigation Strategies, Labor Market, Education, Training, and Economic Impact.

Application of Intelligent Algorithms and Big Data Analysis in Film and Television Creation

With the rapid development of social media, people can access a large amount of data in a short period of time, and big data technology has emerged. With the vigorous development of cloud computing and big data, the method of mining audience interests through a large amount of data to guide film and television creation has attracted more and more attention from experts and scholars. In order to understand the current situation of film and television drama creation in China and provide suggestions for the shortcomings in the industry, this article mainly analyzes the application of intelligent algorithms for traffic prediction models and big data analysis in film and television creation. This intelligent algorithm can predict the potential audience of movies or TV dramas, helping producers and investors make decisions. This system utilizes artificial intelligence technology to select suitable actors for characters based on their matching degree and past work performance. This article applies intelligent algorithms to big data processing to improve the accuracy of data processing. This article explores the application of intelligent algorithms and big data analysis in film and television creation. Using machine learning algorithms to predict the potential audience of a movie or TV series based on historical data, providing decision-making basis for investors and producers. This system utilizes AI technology to select suitable actors for characters based on their matching degree with characters and past work performance, improving the scientific and accurate selection of roles. The application of these technologies helps to improve production efficiency and quality, reduce costs and risks, and inject new impetus into the sustainable development of the film and television industry.

A Novel Hybrid Approach to the Diagnosis of Simultaneous Imbalance and Shaft Bowing Faults in a Jeffcott Rotor-Bearing System

Ensuring optimal performance and reliability in rotor-bearing systems is crucial for industrial applications. Imbalances and shaft bowing in these systems can lead to decreased efficiency and increased vibrations. The early detection and mitigation of a rotor’s faults are essential, and model-based fault identification has gained much attention in the manufacturing industry over the years. Over the past two decades, however, the development of fault diagnosis rules with data-driven and artificial intelligence (AI) methods has become a trend, and in the foreseeable future the combination of AI with big data will become mainstream. Nevertheless, the critical role of rotating machinery in manufacturing introduces a challenge, as often insufficient fault data are available. This limitation renders the establishment of diagnostic rules using data-driven methods and AI technologies impractical. In light of these challenges, this study proposes a novel hybrid approach that combines a physical model with machine learning (ML) techniques for the diagnosis of multi-faults (imbalances and shaft bowing are demonstrated) in a Jeffcott rotor. To overcome the lack of real-world labeled fault datasets, a physics-based Jeffcott rotor model is first derived and then used to generate abundant fault datasets for ML. Subsequently, simulated data are employed for the training of an artificial neural network (ANN), enabling the network to learn from and analyze the vast array of generated data. The results prove that a well-trained feed-forward neural network (FNN) can accurately isolate and diagnose imbalance and shaft bowing faults using the simulated and real data from the Jeffcott rotor experiment. These physics-based and ML approaches prove effective particularly for multi-faults, offering new possibilities for advanced rotor system monitoring and maintenance strategies in industrial applications.

Application of Closed-loop Theory in Deep Learning Training Guided by High-strength Intelligent Machinery

Artificial intelligence (AI) algorithms and continuous monitoring technologies have the potential to transform the way chronic illnesses are managed. We will also talk about the problems and potential that AI technology presents for CGM in individualised and preventive medicine. Furthermore, we assessed the AHCL system's usefulness in patients with impaired awareness of hypoglycemia (IAH) and those who correctly recognised hypoglycemia symptoms. The participants' ages varied from 37 to 15, and they had received diabetes medication for an average of 20 to 10 years. IAH was seen in 12 individuals (27%) with a Clarke's score of less than 3. Patients with IAH were older than those who did not have IAH. The baseline CGM readings and A1c were the same, but the estimated glomerular filtration rate (eGFR) was lower. Despite prior insulin treatment, the AHCL system resulted in an overall drop in A1c (from 6.9 0.5% to 6.7 0.6%, P 0.001). Only three patients (7%) received Clarke's three scores after six months on the AHCL system, resulting in a 20% absolute risk decrease for IAH (95% confidence interval: 7-32).  

Towards an international regulatory framework for AI safety: lessons from the IAEA’s nuclear safety regulations

This study explores the necessity and direction of safety regulations for Artificial Intelligence (AI), drawing parallels from the regulatory practices of the International Atomic Energy Agency (IAEA) for nuclear safety. The rapid advancement and global proliferation of AI technologies necessitate the establishment of standardized safety norms to minimize discrepancies between national regulations and enhance the consistency and effectiveness of these rules. The study emphasizes the importance of international collaboration and the engagement of various stakeholders to strengthen the appropriateness of regulations and ensure their continuous updating in response to the evolving risks associated with technological advancements. The paper highlights the critical role of subgoal setting mechanisms in AI’s decision-making processes, underscoring their significance in ensuring the technology’s stability and social acceptability. Improperly tuned subgoal setting mechanisms may lead to outcomes that conflict with human intentions, posing risks to users and society at large. The study draws attention to the hidden risks often embedded within AI’s core decision-making mechanisms and advocates for regulatory approaches to guarantee safe and predictable AI operations. Furthermore, the study acknowledges the limitations of directly applying IAEA’s nuclear safety cases to AI due to the distinct characteristics and risks of the two fields. The paper calls for future research to delve deeper into the need for an independent regulatory framework tailored to AI’s unique features. Additionally, the study emphasizes the importance of accelerating international consensus, developing flexible regulatory models that reflect the situation in each country, exploring harmonization with existing regulations, and researching timely regulatory responses to the fast-paced development of AI technology.

Artificial intelligence in accounting

Artificial Intelligence (AI) technologies open up broad horizons for enhancing business efficiency and advancing various professional domains, boosting their productivity and compe­titiveness. There is an active exploration of approaches to incorporating AI technologies in the accounting sphere, promising a seamless transition from human to machine involvement. The aim of this article is to summarize the acquired experience, identify perspectives, constraints, and risks associated with the use of AI technologies in the professional activities of accountants. The research is based on the hypothesis that widespread use of AI in the professional activity of an accountant with an insufficient level of professional skepticism and caution carries significant threats and risks for both the accountant and the business as a whole. Scientific search methods, comparative and critical analysis, theoretical generalization, and synthesis were used. A prerequisite for imple­menting AI technologies in accounting is expert information systems and ERP systems. The analysis of AI technology implementation experience in various industries demonstrates their relevance in the accounting field for performing routine tasks (automated recognition of primary documents, processing incoming signals, and other standard operations with a simultaneous reduction in the probability of errors), analyzing large datasets, and providing information support for decision-making (pro­ces­sing business data and regulatory docu­ments), training professionals, and organi­zing internal and external communication (parti­cularly between humans and machines). Identi­fied potential risks include breaches of privacy and data security, misinterpretation of output data, and the disregard of activity context, external and internal environments, especially due to the absence of emotional intelligence, which influences the trust level in integrated information systems. The requirement for the application of professional assessments and judgments, mandated by regulatory documents, limits the scope of AI technology utilization in accounting. Future research should focus on exploring the possibilities of widespread integ­ration of AI technologies in information systems for accounting and improving legislation based on the principle of risk assessment.

Artificial Intelligence for Web 3.0: A Comprehensive Survey

Web 3.0 is the next generation of the Internet built on decentralized technologies such as blockchain and cryptography. It is born to solve the problems faced by the previous generation of the Internet such as imbalanced distribution of interests, monopoly of platform resources, and leakage of personal privacy. In this survey, We discuss the latest development status of Web 3.0 and the application of emerging AI technologies in it. First, we investigate the current successful practices of Web 3.0 and various components in the current Web 3.0 ecosystem and thus propose the hierarchical architecture of the Web 3.0 ecosystem from the perspective of application scenarios. The architecture we proposed contains four layers: data management, value circulation, ecological governance, and application scenarios. We dive into the current state of development and the main challenges and issues present in each layer. In this context, we find that AI technology will have great potential. We first briefly introduce the role that artificial intelligence technology may play in the development of Web 3.0. Then, we conduct an in-depth analysis of the current application status of artificial intelligence technology in the four layers of Web 3.0 and provide some insights into its potential future development directions.

Transforming Educational Assessment: Insights Into the Use of ChatGPT and Large Language Models in Grading

The integration of Artificial Intelligence (AI) technologies in the field of education has prompted significant advancements, particularly in the domain of assessment and grading. This research delves into the potential of large language models, specifically OpenAI's ChatGPT, in simulating human-like interactions and accurately grading student assessments. To accomplish its objectives, the study compares the grading performance of ChatGPT with that of human correctors in a sample of second-year university students. The research findings indicate only a moderate correlation between the grades assigned by ChatGPT and those of human correctors, suggesting nuanced capabilities in providing comprehensive feedback and streamlining the grading process. While the study highlights the benefits of AI integration in education, it also addresses potential risks, including the exacerbation of educational inequalities and the limitations associated with AI's automated nature. This research contributes to the ongoing discourse surrounding AI's role in education, emphasizing the importance of striking a balance between AI and human instruction for optimal educational outcomes.