Rise Of Artificial Intelligence Research Articles

Are State Comprehensive Institutions Prepared for AI? Not yet.

The rise of artificial intelligence (AI) has profound implications for higher education and teaching and learning. The present study, conducted at a state, comprehensive university (SCU) is the US Midwest, evaluated how effective faculty in a leadership focused program are at differentiating between human or AI-generated content. Participants reviewed 12 “student assignments” representing short answer essays, and completed the following tasks: 1) evaluated the assignment on three dimensions of the grading rubric; 2) assigned a percent grade; 3) indicated whether it was human or AI-generated; and 4) provided a rationale for their selection. Evolving technologies like ChatGPT and other LLMS are forcing educators to reconsider how they will interact with students as well as redefine what will constitute scholarly work in the future. Based on the study, we offer short- and long-term implications regarding AI for both the field of leadership and, more broadly, for faculty in many social science disciplines at SCUs. An exercise at the end of the article allows readers to complete a brief interactive to provide them with a similar revelatory experience as our study participants (see Interactive Reader Activity) before reading the remainder of this article. The Interactive Reader Activity contains three samples from the study—each of which was either human written or AI-generated. Answers are provided.

An Efficient River Water Quality Prediction and Classification Model using Metaheuristics based Kernel Extreme Learning Machine

In the previous years, water quality has been susceptible to different pollutants. Also, the various environmental conditions like vegetation, climate and basin lithology affects the quality of the river water naturally. So, the prediction of water quality (WQ) becomes a major process to control and basin lithology affects the quality of the river water naturally pollution. The rise of artificial intelligence (AI) manners can be utilized for designing predictive methods for water quality index (WQI) and classification. This study focuses on the design of metaheuristics-based kernel extreme learning machine (MBKELM) for river water quality prediction and classification. The proposed MBKELM model aims to predict and classify the quality of river water into different classes. In addition, a prediction and classification model using KELM is derived to appropriately determine the water quality. Moreover, the parameter tuning of the KELM model takes place by pigeon optimization algorithm (POA). A wide range of experimental analyses was performed on benchmark datasets and the experimental outcomes reported the supremacy of the MBKELM technique over the recent techniques. The results stated that the proposed MBKELM model has accomplished minimal MSE and RMSE values. On examining the results in terms of MSE on training set, the MBKELM model has accomplished a lower MSE of 0.00257 whereas the existing model has gained a higher MSE of 0.00336. Also, on examining the results in terms of RMSE on testing set, the MBKELM manner has accomplished a lesser RMSE of 0.05070 whereas the existing model algorithm has gained a higher RMSE of 0.05800.

Using artificial intelligence for exercise prescription in personalised health promotion: A critical evaluation of OpenAI's GPT-4 model.

The rise of artificial intelligence (AI) applications in healthcare provides new possibilities for personalized health management. AI-based fitness applications are becoming more common, facilitating the opportunity for individualised exercise prescription. However, the use of AI carries the risk of inadequate expert supervision, and the efficacy and validity of such applications have not been thoroughly investigated, particularly in the context of diverse health conditions. The aim of the study was to critically assess the efficacy of exercise prescriptions generated by OpenAI's Generative Pre-Trained Transformer 4 (GPT-4) model for five example patient profiles with diverse health conditions and fitness goals. Our focus was to assess the model's ability to generate exercise prescriptions based on a singular, initial interaction, akin to a typical user experience. The evaluation was conducted by leading experts in the field of exercise prescription. Five distinct scenarios were formulated, each representing a hypothetical individual with a specific health condition and fitness objective. Upon receiving details of each individual, the GPT-4 model was tasked with generating a 30-day exercise program. These AI-derived exercise programs were subsequently subjected to a thorough evaluation by experts in exercise prescription. The evaluation encompassed adherence to established principles of frequency, intensity, time, and exercise type; integration of perceived exertion levels; consideration for medication intake and the respective medical condition; and the extent of program individualization tailored to each hypothetical profile. The AI model could create general safety-conscious exercise programs for various scenarios. However, the AI-generated exercise prescriptions lacked precision in addressing individual health conditions and goals, often prioritizing excessive safety over the effectiveness of training. The AI-based approach aimed to ensure patient improvement through gradual increases in training load and intensity, but the model's potential to fine-tune its recommendations through ongoing interaction was not fully satisfying. AI technologies, in their current state, can serve as supplemental tools in exercise prescription, particularly in enhancing accessibility for individuals unable to access, often costly, professional advice. However, AI technologies are not yet recommended as a substitute for personalized, progressive, and health condition-specific prescriptions provided by healthcare and fitness professionals. Further research is needed to explore more interactive use of AI models and integration of real-time physiological feedback.

Will AI Slay the Poverty Dragon?

The rise of artificial intelligence and other new technologies has raised hopes that they might contribute to reducing poverty. History suggests that they need to be embedded in broader frameworks of institutions, regulations, and training.

The Analysis of Neural Network Models to Distinguish AI generated faces from Real faces

The rise of Artificial Intelligence (AI)-generated faces that are identical to actual ones is both a technological innovation and a major concern. While considering the implications, some of the existing security systems are unable to differentiate between a high-quality deep-fake and an actual intruder's face. For instance, the risks are quite high at an airport security checkpoint, where facial recognition is the first line of security against unauthorized entry. The primary concern here is how trustworthy the computer programs and algorithms will be in recognizing counterfeits among a plethora of actual and artificially generated faces. This necessitates the need to introduce machine learning approaches to differentiate between actual and fraudulent faces, particularly when AI-generated faces are involved. Effective artificial intelligence systems must be adaptive and change quickly in the face of more complex threats rather than simply recognizing them. AI-generated faces are becoming more convincing by the day, increasing the risk of their exploitation. This poses the need to ensure that the technology on which people rely should be robust and trustworthy in critical situations, whether it is a security checkpoint or an e-commerce site. In this perspective, this study has attempted to develop and implement Artificial Intelligence-powered solutions to detect the artificial faces while ensuring reliability for critical functions in an age where reality constantly blends with fiction.

Artificial Consciousness: Misconception(s) of a Self-Fulfilling Prophecy Nobody Wants

The rise of Artificial Intelligence (AI) has produced prophets and prophecies announcing that the age of artificial consciousness is near. Not only does the mere idea that any machine could ever possess the full potential of human consciousness suggest that AI could replace the role of God in the future, it also puts into question the fundamental human right to freedom and dignity. This position paper takes the stand that, in the light of all we currently know about brain evolution and the never-stopping formation of adaptive neural circuitry for learning, memory, decision making and, ultimately, fully conscious reasoning and creativity in the human species, the idea of an artificial consciousness appears misconceived. The paper highlights some of the major reasons why. While awareness to external stimuli for processes such as perception, recognition, and operational problem solving is under the direct control of functionally specific brain networks associated with sensory and cognitive functions across animal species, consciousness is a unique property of the human mind. Potentiated by brain evolution, consciousness has come to be when humans became able to represent, and reflect on, the Self in relation to past, present and future, and to project these representations into possible worlds by drawing and other forms of conceptual and creative expression. Epigenetically determined, shaped by experience, capable of representing real and non-real world states, consciousness is enabled by context-dependent adaptive brain circuits that have evolved on the grounds of self-organizing functional interactions at different levels of integration in a from-local-to global functional brain design. The evolution of the latter being continuous, the limits of consciousness are unpredictable. If cracking the computational code to human consciousness were possible, the resulting algorithms would have to be able to generate temporal activity patterns simulating long-distance signal reverberation across the brain and the de-correlation of spatial signal contents from their temporal signatures. In the light of scientific evidence for complex interactions between implicit (non-conscious) and explicit (conscious) representations in learning, memory, and the construction of conscious representation, the code would have to be capable of making all implicit processing explicit. Algorithms would have to be capable of a progressive, less and less arbitrary selection of temporal activity patterns in a continuously developing neural network structure akin to that of the human brain, from synapses to higher cognitive functional integration. The code would have to possess the self-organizing capacities that generate the brain signatures of phenomenal consciousness. In the biological brain, consolidation or extinction of these temporal brain signatures is driven by external event probabilities according to the principles of Hebbian learning. Consciousness is constantly fed by such learning, capable of generating stable representations despite an incommensurable amount of variability in input data, across time and across individuals, for life-long integration of experience data. Artificial consciousness would require probabilistic adaptive computations capable of emulating all the dynamics of human learning and memory that enable human intelligence and creativity. No AI is likely to ever have such potential.

Mechanically toughed and self-adhesive organohydrogel with anti-freezing and non-drying properties for human motion monitoring and information transmission

With the rise of artificial intelligence, hydrogel-based wearable flexible strain sensors have acquired increasing attention in recent years. Herein, a mechanically toughed versatile organohydrogel with anti-freezing and non-drying properties is constructed by simultaneously introducing methacrylic acid (MAA), acrylamide (AAm) and ZrCl4 into the glycerol/water binary solvent system. Based on the carboxyl-Zr4+ strong metal-coordination interaction, the fracture strain and tensile strength of as-prepared organohydrogel can reach 1015% and 180 kPa, respectively. At the same time, the glycerol/water binary solvent endows the organohydrogel with excellent low-temperature tolerance (−35 °C) and long-term moisture-retention capability (80% at 30 °C for 10 days). Furthermore, the organohydrogel-based strain sensor exhibits wide detection range (5–400%) and remarkable cycling stability (500 cycles at 200% strain), which can not only real-timely monitor various human movements but also precisely distinguish the direction of human motion. Impressively, the as-fabricated organohydrogel sensor also demonstrates promising potential in the application of rehabilitation training of a patient and information transmission. Benefiting from the remarkable environmental stability, the organohydrogel sensor after being stored at − 35 °C and 30 °C for 10 days, respectively, the resistance signals corresponding to different stretching deformations and complex human movements have no obvious decay. It is anticipated that the obtained organohydrogel can as a promising candidate of the next-generation flexible electronic devices for their substantial applications in motion sensing, human-machine interaction, artificial intelligence and biomedicine.

The rise of artificial intelligence

As artificial intelligence continues to develop, Penny Harrison discusses the impact it is already having on gastrointestinal nursing

Generative Artificial Intelligence (AI) Educational Pedagogy Development: Conversational AI with User-Centric ChatGPT4

In terms of language models, generative artificial intelligence (GenAI), and more specifically ChatGPT, offer a significant technological achievement as a revolutionary tool for natural language processing (NLP) and a transformative educational business tool. ChatGPT users' suggestions have the ability to optimize teaching and learning, thereby having a substantial impact on the educational environment of the twenty-first century. Educational robots are getting easier to access for a number of reasons. The human-robot cooperation that has advanced scientifically in industry 5.0 extreme digital automation, will also probably become a regular aspect of life in the days to come. This study examines the prospective uses of GenAI for NLP synthesis as well as its potential role as a conversational agent in the classroom business. GenAI's capacity to understand and produce language that is human-like by employing NLP to generate semantics was essential to its ability to replicate the most advanced human technology through comprehensive assumptions of patterns and structures it learns from its training data. With the rise of artificial intelligence (AI) driven conversational agents, prompt engineering has become an important aspect of digital learning. It is essential to get ready for an AI-dominated future when general and educational technologies combine. The study demonstrated how society may impact and contribute to the development of AI pedagogic learning using an instructional robotics application driven by AI, emphasizing the responsibility of humans as producers to reduce any potential misfortunes. The study highlights that since generative AI technologies have the potential to drastically change teaching and learning approaches and necessitate new ways of thinking, more research on organizational robotics, with a focus on human collaboration and education, will emerge from the technological concerns raised in this study.

Application of Artificial Intelligence in E-commerce

The rapid development of artificial intelligence in recent years is directly reflected in commercial activity, where a large number of transactions are carried out, a significant amount of data is formed and processed about deliveries made, sales made, and customers served. This is also true of e-commerce, which is establishing itself as the dominant non-store form of commercial exchange. The rise of artificial intelligence is changing the way businesses approach e-commerce. The algorithms and systems powered by it enable e-retailers to make data-driven decisions, automate processes and offer personalized experiences that match the unique preferences of individual customers. The purpose of this paper is to present the essence, advantages and guidelines for the application of artificial intelligence in e-commerce, and on this basis, the challenges for online merchants in its implementation are presented. The paper provides a brief analysis of data from Statista on the areas where artificial intelligence is helping e-commerce.

Utilization of Writing Assistance Tools in Research in Selected Higher Learning Institutions in the Philippines: A Text Mining Analysis

The rise of artificial intelligence (AI) in higher education has sparked significant interest. Artificial intelligence offers opportunities for global learning, personalized instruction, and efficient resource management. In research writing, AI tools streamline processes, from a literature review to data analysis, enhancing efficiency and freeing up time for critical thinking. The researchers of this study used text mining techniques to extract patterns and trends of using writing assistance tools in research from the responses of 327 faculty researchers in various higher learning institutions in the Philippines. Unigram tokenization was used to present the 10 most frequently used words in the corpus, while the k-means elbow method was utilized to show the optimal number of clusters. Sentiment analysis was use to show the positive and negative implications of using writing assistance tools in research. The finding is that faculty respondents value writing assistance tools for enhancing research writing by expediting processes and improving clarity. The implementation of these tools in selected higher learning institutions may present difficulties, such as issues related to overreliance, and hinder students’ and researchers’ development of critical thinking and writing skills. Along with technological and resource challenges, these tools could inadvertently encourage plagiarism if not used responsibly. Cultural and language considerations are also relevant because these tools may not always be attuned to the specific linguistic and cultural nuances of Filipino academic writing. The results of this research may serve as a guide for researchers, educators, and software developers, who may use AI to streamline research writing processes in the educational sector.

Who, or Rather, What Painted This? Generation Z's Attitudes Towards Artificial Intelligence Artworks

Throughout art history, the exact boundaries of what can and cannot be considered art has long been debated, and the rise of artificial intelligence (AI) algorithms that produce artworks has rekindled this debate in the context of technology. History indicates that the answer to what is considered “art” largely depends on the evaluator’s personal definition of creativity and their perception of the artist, which gives reason to investigate people’s attitudes towards AI art. Pre-existing research has discovered a negative perception bias towards AI artists in adult generations. However, no studies have been conducted on Generation Z specifically. Gen Z possesses a close association to technology which distinguishes them from previous generations, and could therefore produce differing attitudes towards AI art. Therefore, this study seeks to investigate: what are Generation Z’s attitudes towards AI art? This study defined “attitudes” to combine attitudes towards the artist, and attitudes towards the artworks. Therefore, a mixed method study consisting of 1) a ratings test to examine any perception biases towards AI artists, and 2) a questionnaire to investigate the criteria Gen Z used to determine value in AI artworks were combined to answer this question. It was concluded that Generation Z had a neutral attitude towards AI artists and responded rather well to AI art styles of landscape paintings.

A Supervised Deep Learning Model for the Detection of Cardiovascular Disease

In our world today, cardiovascular disease (CVD) stands as the foremost cause of death worldwide, claiming the lives of nearly 20 million individuals annually. As CVD continues to burden the healthcare industry, there is a critical need for early detection and prevention. The rise of Artificial Intelligence in the medical field offers a range of capable solutions. In order to address the problem, this paper presents the development of a simple, supervised deep learning model for detecting cardiovascular disease in patients. The research focused on creating a model enriched with multiple layers, activation functions, optimizers, and loss functions. The chosen approach leveraged the power of AI to analyze labeled patient data and map input features to corresponding class labels, enabling accurate detection of CVD. The dataset used contained 70,000 patient records with 12 different clinical attributes. In addition, it provides an overview of the most common types of cardiovascular disease, such as coronary artery disease, aortic valve disease, stroke and peripheral artery disease. The accuracy of the obtained results from the deep learning model was up to 73%. The utilization of AI systems can present a novel approach to addressing daily challenges within the rapidly-evolving world of medicine. Health personnel can take advantage of rapidly changing artificial intelligence and user friendly deep learning models to detect similar future medical concerns.

The rise of artificial intelligence as the future of Korean culture diplomacy

The COVID-19 pandemic ushered in a new era of technology, pushing everything online and necessitating human adaptation to computers. Industry 4.0 proved invaluable during this pandemic, with Artificial Intelligence (AI) emerging as a key player, particularly in the beloved South Korean music industry, which enjoys international acclaim. The pandemic played a significant role in the increased use of AI within the K-POP industry, with the emergence of the Aespa Girl Group being a pivotal moment in shaping a futuristic concept. This research aims to investigate the recent surge in AI usage as a futuristic K-Pop concept through a qualitative literature study method. This paper contends that AI has the potential to profoundly impact Korean cultural diplomacy, serving as a game-changing element in the realm of digital diplomacy. While AI has traditionally been applied in high-context scenarios, such as search engines or high-tech automotive control systems, its application in cultural contexts could propel South Korea's cultural diplomacy to unprecedented heights, rendering the concept difficult for other nations to replicate.

Artificial Consciousness: Misconception(s) of a Self-Fulfilling Prophecy Nobody Wants

The rise of Artificial Intelligence (AI) has produced prophets and prophecies announcing that the age of artificial consciousness is near. Not only does the mere idea that any machine could ever possess the full potential of human consciousness suggest that AI could replace the role of God in the future, it also puts into question the fundamental human right to freedom and dignity. Yet, in the light of all we currently know about brain evolution and the adaptive neural dynamics underlying human consciousness, the idea of an artificial consciousness appears misconceived. This article highlights some of the major reasons why the prophecy of a successful emulation of human consciousness by AI ignores most of the data about adaptive processes of learning and memory as the developmental origins of consciousness. The analysis provided leads to conclude that human consciousness is epigenetically determined as a unique property of the mind, shaped by experience, capable of representing real and non-real world states and creatively projecting these representations into the future. The development of the adaptive brain circuitry that enables this expression of consciousness is highly context-dependent, shaped by multiple self-organizing functional interactions at different levels of integration displaying a from-local-to global functional organization. Human consciousness is subject to changes in time that are essentially unpredictable. If cracking the computational code to human consciousness were possible, the resulting algorithms would have to be able to generate temporal activity patterns simulating long-distance signal reverberation in the brain, and the de-correlation of spatial signal contents from their temporal signatures in the brain. In the light of scientific evidence for complex interactions between implicit (non-conscious) and explicit (conscious) representations in learning, memory, and the construction of conscious representations such a code would have to be capable of making all implicit processing explicit. Algorithms would have to be capable of a progressive and less and less arbitrary selection of temporal activity patterns in a continuously developing neural network structure that is functionally identical to that of the human brain, from synapses to higher cognitive functional integration. The code would have to possess the self-organizing capacities of the brain that generate the temporal signatures of a conscious experience. The consolidation or extinction of these temporal brain signatures are driven by external event probabilities according to the principles of Hebbian learning. Human consciousness is constantly fed by such learning, capable of generating stable representations despite an incommensurable amount of variability in input data, across time and across individuals, for a life-long integration of experience data. Artificial consciousness would require probabilistic adaptive computations capable of emulating all the dynamics of individual human learning, memory and experience. No AI is likely to ever have such potential.

Media Presentation and Rise of Artificial Intelligence: Possibilities, Limits and Dangers

Bu çalışma yakın bir geçmişte hayatımıza giren, pek çok alanda kullanılmaya başlanan, bazı bilinmezlik ve belirsizlikler içermekle birlikte her geçen gün artan bir ilgi ve merakla takip edilen “yapay zekâ” kavramı üzerine yapılmıştır. Yapay zekâ kavramının tarihi ve ilerlemesi literatür taraması yapılarak betimleyici bir analizle anlatılmış, ardından yapay zekanın yükselişi bilim ve teknoloji haberleri ölçeğinde analiz edilmeye çalışılmıştır. Yapay zekânın ne olduğu ya da olmadığı, yapay zekâ uygulamaları ile insanlığı bekleyen olanak ya da tehlikelerin neler olabileceği haber metinleri üzerinde aranmaya çalışılmıştır. Bu çözümleme için ülkemizde bilim ve teknoloji haberlerinde konunun sıklıkla görünür olmaya başladığı 2023 Ocak ayı başlangıç olarak alınmış, o tarihten itibaren altı aylık dönem içinde çıkmış yapay zekâ haberleri taranmıştır. Temsili örneklem yöntemine göre seçilmiş haber metinleri, oluşturulan kategorilere göre içerik analizi yönt emi ile çözümlenmiş, göstergeler ise bu çözümlemeye katkı getirmesi bağlamında çalışmaya dâhil edilmiştir. Haber metinlerinin çözümlenmesi sonrasında yapay zekânın yükselişine ve sınırlarına ilişkin olarak; sunduğu olanaklar ve içerdiği tehlikeler belirlenmiş ve bunlara ilişkin değerlendirmeler yapılmıştır. Sonuç olarak, haberlerde yapay zekâya ilişkin bilgi ve uygulamalardan söz edilirken insanlığı bekleyen olanak ve tehlikelerin boyutlarına ilişkin toplumsal bir farkındalık ve bilinç geliştirerek bu yenidünya algoritmasına insanlığı hazırlıklı kılmak yönünde bir inşanın söz konusu olduğu görülmüştür. Haberin yarattığı anlamın ise daha çok tehlike ve tehdit alanları üzerinde odaklandığı söylenebilir.

Modified YOLO network model for metaphase cell detection in antinuclear antibody images

The antinuclear antibody (ANA) test plays the role of screening, diagnosis, treatment and prognosis for autoimmune diseases. However, the ANA test is time-consuming, and strongly depends on the interpretation of experienced medical examiners. With the rise of artificial intelligence, a lot of convolutional neural networks (CNN) have been used to recognize the features of ANA images, such as GoogLeNet, ResNet, and MobileNet. By extracting the disease-related features from the entire ANA image, these models achieve acceptable accuracy, but none of them can replace the manual ANA test completely. This is because they ignore the key features of ANA images. Accordingly, this paper aims to design an object detection model that can directly identify the key features of ANA images, i.e., the Metaphase cells. This process is critical in the manual ANA test performed by the medical examiners. Based on the YOLO model, our design first applies various image preprocessing and data augmentation techniques to enhance the critical features in the training data. We modify the original YOLO model, in which the residual block is replaced with SENet module to improve the classification performance by emphasizing critical features and reducing the influence of low-level features. Moreover, we use the different activation function to prevent the modified model from partial judgment. The experimental results show that our design can effectively improve the performance of ANA metaphase identification. Compared to the related CNN models, our design can improve F1-score and mAP by range 5%–14% and 11%–13%, respectively. In particular, the Kappa value of our design is 0.82 that is almost perfect agreement with the ANA experts.

Artificial Intelligence (AI) and Automation in the World of Work: A Threat to Employees?

The introduction of machines driven by artificial intelligence (AI) and automation technologies has already had a significant impact on the manufacturing, automotive, logistics, retail, and wholesale industries, and the repercussions of their replacement on the human labour has been a hotly disputed subject. AI and automation technical advancements are having a big influence on workforce turnover. The aim of this study was to look at employees' attitudes on Artificial Intelligence and Automation at work, specifically whether they see AI as a threat or not. Secondary data sources from several authors were utilised in this article. This data was gathered from published and peer-reviewed publications, internet sources, and textbooks pertinent to the issue under consideration. Some researchers contend that automation is likely to add to South Africa's high unemployment rate. There is a scarcity of empirical data in the form of published empirical research concentrating on the stress that AI and automation place on employees, hence the study is being conducted. Based on this study, it appeared that employees feel threatened by this rise of Artificial Intelligence and Automation.

An extension of iStar for Machine Learning requirements by following the PRISE methodology

The rise of Artificial Intelligence (AI) and Deep Learning has led to Machine Learning (ML) becoming a common practice in academia and enterprise. However, a successful ML project requires deep domain knowledge as well as expertise in a plethora of algorithms and data processing techniques. This leads to a stronger dependency and need for communication between developers and stakeholders where numerous requirements come into play. More specifically, in addition to functional requirements such as the output of the model (e.g. classification, clustering or regression), ML projects need to pay special attention to a number of non-functional and quality aspects particular to ML. These include explainability, noise robustness or equity among others. Failure to identify and consider these aspects will lead to inadequate algorithm selection and the failure of the project. In this sense, capturing ML requirements becomes critical. Unfortunately, there is currently an absence of ML requirements modeling approaches. Therefore, in this paper we present the first i* extension for capturing ML requirements and apply it to two real-world projects. Our study covers two main objectives for ML requirements: (i) allows domain experts to specify objectives and quality aspects to be met by the ML solution, and (ii) facilitates the selection and justification of the most adequate ML approaches. Our case studies show that our work enables better ML algorithm selection, preprocessing implementation tailored to each algorithm, and aids in identifying missing data. In addition, they also demonstrate the flexibility of our study to adapt to different domains.

The Impact of Early Family Environment on Childrens Creativity

With the rise of artificial intelligence and the increase in social demand, creativity has increasingly become a competitive factor for individuals. Therefore, the impact of early family environments on childrens creativity development, especially the psychological one, is further emphasized. However, there is still a research gap in the impact of different parenting styles and different language environments on childrens creativity. This article uses literature analysis to study the impact of bilingual family environment and ancestral dominated family environment on childrens creativity. Among them, intergenerational education usually has a negative influence on childrens creativity due to the educational limitations of the grandparent, while bilingual education can cultivate childrens unique ways of thinking, thus playing a positive role in developing childrens creativity. Parents and grandparents should pay attention to choosing positive strategies when creating an early family environment for their children, in order to strengthen the generation of positive effects and reduce the impact of negative effects.