This paper is part of a project that aims to conceptualize the knowledge economy (KE) in the Saudi context, focusing on gender in relation to education, employment, human development index, innovation, and ICT. It uses a quantitative methodology. However, the used data is secondary data collected from different government and non-government sources. Different statistical analysis methods were conducted including descriptive statistics, graphs, correlation, and trend analysis. The paper found that despite the importance placed on KE and women empowerment , gender discrepancies were observed in relation to employment, innovation and ICT. Those components, according to our data, are positively correlated with the current Saudi ranking under KEI. The paper shows that although the rate of female graduates from higher education is slightly higher than male, this difference was not translated into participation in the labor market, particularly in jobs related to KE. It was also found that though Saudi Arabia's ranking under innovation and ICT was internationally low, there was a lack of public information regarding the gender dimension. This suggests that gender was not understood as a crucial factor in improving the country's ranking under those components. The paper concludes by highlighting the national innovation system's shortfalls as reflected by education, patents, and innovation. It suggests further investigations into utilizing women under jobs related to KE and calls for including the gender dimension as a variable in any future planning or studies related to the knowledge economy. Knowledge economy; Higher education; Female labor force participation; Human development index, Saudi Arabia, Gender gap. The global interest in the transition to a knowledge economy has become a necessity to the point where knowledge has become an asset and the main component of the modern economic and social system. This transition is based on a new understanding of the role of human capital, especially female human capital, in economic development and societal progress .The role of female labor market participation and its impact on economies have long been established in the literature. In this scenario, a discussion on women empowerment is analogous to a discussion on promoting economic wealth and sustainability as women have started taking the lead role in economic development . Women'sIn the given context, it must be noted that, like many developing countries, in Saudi Arabia, there is gender bias in the composition of most of the available educational opportunities and occupations. The ratio of women in the labor market does not match their enrollment in higher education; this gap is reflective of talent and resource wastage . It explored different data sources to investigate the prevailing knowledge economy and link it with gender participation. This study transcends the festive propaganda around female employment in Saudi Arabia. It is a data-driven study scrutinizing women participation in jobs related to the knowledge economy and connecting it to their education. It also establishes a relationship between the areas lacking a gender perspective and the country's efforts to transition to a knowledge economy.A better understanding of the status of Saudi's knowledge economy in relation to the gender dimension will help relevant decision-makers to make informed decisions, especially regarding education's role to empower women and genderize, de-genderize, and re-genderize the knowledge economy.1.1Saudi Arabia plays a very strategic role regionally and internationally owing to its economic, geographical, political, and religious position in the world. With a population exceeding 34 million, it is the biggest of the six Gulf Cooperation Council (GCC) countries. Saudi Arabia also plays a critical role in the stability of the world's economy as it possesses about one-fifth of the world's proven petroleum reserves .Saudi Arabia is among the first Arab countries to develop an integrated national plan to develop the knowledge economy by using information technologies . The SauSaudi Arabia has also attempted to include the notion of the knowledge-based economy in different phases of its national plans. For example, the Eighth Development Plan (for the period 2005–2009), focused on fundamental developments that laid the basis for transitioning to a knowledge-based economy. These developments included commencement with the implementation of the first five-year plan of the National Science, Technology and Innovation Policy and the adoption of the National ICT Plan, the National Industrial Strategy, and the Strategy and Plan for Giftedness, Creativity and Supporting Innovation. It also included the establishment of the knowledge economic city in Medina and the technology zone of the Saudi Organization for Industrial Estate and Technology Zones in Dammam, the preparation of a new strategy for higher education and for promoting privatization. The Ninth Development Plan, which focused on the 2010–2014 period, made progress toward a knowledge-based economy by focusing on education and paving the way for knowledge transfer, accumulation and generation, and its utilization in various economic and social sectors, particularly those of production and service activities. The Tenth Development Plan, covering the 2015–2019 period, focused on the diversification of the country's economy and increasing its non-oil revenue by 8.5% to reach 66% of the GDP in 2019 and, in the same year, increasing the private sector participation in the country's GDP to 50.6%. The Plan also emphasized investments in the manufacturing industries and mining sectors, assigned 372 billion riyals for infrastructural investments, and spelled out details on government spending and accountability. These initiatives resulted in investments in the knowledge economy infrastructure, such as advanced information and technology, which has become one of the mainstays of production in the country's current economy . In thisThrough these endeavors, the Saudi government seeks to enhance the comparative advantages of the economy, diversify it, and increase local productivity and competitiveness while creating commensurate employment opportunities for its citizens. Saudi Vision 2030 reflects a clear trend in pursuit of the shift toward a knowledge-based economy, incorporating efforts to diversify the country's economy through several plans, initiatives, and programs. The latest plans of Saudi Vision (2030) include the announcement of the NEOM project, which aims to build globally smart cities that depend on clean energy and provide investment opportunities in connecting continents. Vision 2030 also represents a qualitative shift in the Kingdom's policy toward dealing with economic and societal fundamentals. By setting goals for each agency in the government, the Vision also directs the components of the knowledge society toward sustainable economic goals that take the country's competitive advantages in various fields into account.Vision 2030 encompasses the potential to promote competitiveness and sustainable social and economic growth thanks to the readiness of the science and technology systems' components, the pursuit of innovation, the completeness of the communication and information technology system, and individuals' high education and cultural levels. Utilizing and increasing the participation of female labor in the transformation process is a central focus of discourses on these development plans. Women comprise more than 50% of the country's working population, a proportion that cannot be ignored by the government .1.2i.To identify the status of Saudi women's labor force participation in relation to the components of the knowledge economy—education, employment, human capital, innovation, and information and communication technology.ii.To identify the relationship between education, employment, human capital, innovation, and information and communication technology and Saudi's ranking in the knowledge economy index.iii.To explore the relationship between Saudi women's current labor force participation in relation to the knowledge economy's components and Saudi's ranking in the knowledge economy index.This study's main aims are to conceptualize Saudi women's participation in the knowledge economy and to link it with the role of education. The specific objectives of the study are as follows:2Knowledge represents one of the fundamental and most distinctive characteristics of human society. It has facilitated significant transformations covering the economic, social, and cultural aspects of life throughout history. Although the literature provides different knowledge epistemologies, it often links knowledge to various explanations and applications of the knowledge itself . It mustIt has become common knowledge that most of the world's advanced economies are heavily dependent on the efficient production, distribution, and use of knowledge. The increased use and utilization of knowledge, information, and technology in the production process has confirmed knowledge as a factor of production and induced investment in knowledge capital. Consequently, knowledge contributes toward and develops production processes that result in job creation. The countries with the highest economic growth rates are those that have comparatively more knowledge capabilities. Other factors that justify the transition to a knowledge-based economy are the rapid growth of knowledge, increased sources of knowledge, and the emergence of new branches in science, technologies and products that enhance the dissemination of knowledge ."an economy that is capable of knowledge production, dissemination, and use: where knowledge is a key factor in growth, wealth creation, and employment, and where human capital is the driver of creativity, innovation, and generation of new ideas, with reliance on information and communication technology (ICT) as an enabler" , 9–11.The broad recognition of knowledge as a dominant production factor influencing economic and societal transformation and development has led to the emergence of different terms and definitions for the knowledge economy. The emergence of the numerous terms used to denote the concept of a knowledge-based economy indicates that there is no universally accepted definition . This stIt is noteworthy that the Saudi government embraced the OECD definition of the knowledge economy (KE) in its latest development plans. This definition reflects the multiple facets of knowledge and assigns central importance to economic growth. It also shows the relationship between knowledge, job creation, innovation, and ICT use. Owing to their prevalent economic impact, the paper covers these main domains, and it does so from a gender perspective.2.1Both genders must participate equally in improving the nation's economic capability using knowledge. In other words, working-age men and women are expected to participate equally in the KE; both genders can contribute equally, given that the advancement of the KE depends on intellectual capabilities rather than physical inputs or natural resources . NeverthThe importance of women's participation in the economy has been long debated in academic research and continues to be contested to this day, mostly regarding women's effective participation in knowledge-based jobs. Several scholarly works, such as those by The Saudi government resolved to address this issue and expressed its commitment to increase women's inclusion in economic life across all the sectors. However, the fulfillment of this vision would require the Saudi government to amend workplace legislation and develop new ways of defining women's values as contributors to economic development. The government should also address the social norms governing women's employment and willingness to participate and focus on the nature of employment and its relevance to the labor market's demands. Concerning gender parity, 2.2Since the KE deploys knowledge as an essential tool for accumulating wealth, assuring efficiencies, boosting economic growth, and creating jobs, it is imperative to explore the relationship between education and the process of knowledge generation. There is a strong relationship between the KE and education. Educational institutions facilitate knowledge exploration, discussion, contestation, and the creation and development of knowledge applications. It is safe to assume that education produces different forms of knowledge that can be converted into capital or commodities that can advance economic growth, as argued by Barkhordari et al. (2018). Thus, the The relationship between the two parties is overly complex and fluid and involves various enabling actors . Higher 33.1The researchers used secondary data from various sources, including Saudi Arabia's General Authority for Statistics, the Ministry of Education, Labor, the Ministry of Economy and Planning, the World Bank, and Saudi Vision 2030. The used data were public and open-access except those obtained from the global economy website, which are paid-for. The researchers utilized time series data spanning from 2010 to 2018 for the knowledge economy indicators.3.1.1The variables used in the statistical analysis represent the indicators of KE's pillars. Most of the indicators were analyzed using variables broken down by gender whenever possible.3.2The data were entered and manipulated using Microsoft Excel software and the Statistical Package for Social Sciences (SPSS) software. Excel and SPSS software were used to run the statistical analysis of data obtained from different secondary sources. The data were organized in quantitative variables and defined in SPSS prior to the analysis process.Since SPSS is software that can be used for both primary and secondary data, the analyses were conducted using different methods and statistical tools. That included a descriptive analysis method for mean values and graphs (line and bar charts) to present the trend of variables during the study period. Additionally, correlation analysis was used to examine the relationship between the selected variables.4This section discusses the results in the Saudi context, based on the World Bank's knowledge economy framework. The first part addresses education and employment as essential indicators of the KE. The subsequent section discusses the other KE indicators—human development index (HDI), innovation, and ICT.4.1Education is one of the leading indicators of the KE, given that it promotes women's labor market participation. There is a strong correlation between education and employment ; howeverThe rate of enrollment in tertiary education in Saudi Arabia is higher than in many other Arab countries . AlthougAs of 2020, the percentage of unemployed Saudi women was 30.2%, and their labor market participation stood at 24.2% . NeverthDespite the increase, the rate of unemployment among women graduating from higher education institutions seems to be insignificant during the same period, as shown in Data illustrating the trend of male and female labor force participation during the sampled period confirm the statement made about the inability of females with bachelor's degrees to secure employment Figures  and 3. FThe data presented in Figures It is noteworthy to mention here that, according to many studies, sales-related jobs contribute mainly toward the knowledge economy, and salespersons are considered knowledge workers . HoweverThe discrepancy between the attained education at the level of a bachelor's degree and the labor market participation is amplified at the postgraduate level. This discrepancy can be observed in This section has exposed a clear gender gap and discrepancy between women's education and employment. As has been proved in previous studies in different contexts, KE can provide the best opportunity to the Saudi government to increase women's labor market participation and, consequently, to improve their GDP and accelerate the process of transition to a knowledge-based economy . Neverth4.2Human Development Index (HDI) is one of the most important indicators of KE, and it is closely related to education. Human development can be defined as "the knowledge, skills, competencies and other attributes embodied in individuals or groups of individuals acquired during their life and used to produce goods, services or ideas in market circumstances" . HDI is Figures Saudi Arabia's classification is considered satisfactory when it comes to HDI; however, this ranking may not capture Saudi government's goals in this area owing to different reasons. For instance, as one of the most influential members of the G-20 group and given its strong macroeconomic performance, Saudi Arabia is expected to exhibit a better and higher ranking. The influence of country’s performance, however, will be witnessed more profoundly in the section that discusses education and innovation.th position, next to Italy and before Argentina. This ranking indicates the good performance of Saudi Arabia in the HDI components. The HDI indicator stands out among the other indicators discussed in this study. The HDI trend between 2010 and 2018 shows an increasing pattern, justifying the relatively high ranking of the country under this KE indicator. It is important to mention here that government spending and oil exports rather than KE are the main drivers of human development index (HDI) in Saudi Arabia .The HDI trend based on gender shows a steady performance; however, there is a gender gap in favor of male, which is not statistically significant (0.89–0.78).While Saudi has a high ranking under HDI, among the G-20 countries, this ranking is not reflected for the innovation index. HD is correlated with education and, consequently, innovation and ICT . The rel4.3This section presents details regarding Saudi Arabia's status in innovation, research and development and ICT. It also compares this status to the rest of the G20 countries to shed light on the areas where the country needs further improvement. Although Saudi Arabia has accomplished a remarkable advancement in its HDI ranking, its Innovation Index remains an area of concern. 4.3.1The 2010–2014 data regarding innovation in Saudi Arabia show a gradual increase in the country's ranking on the international innovation index. The increase, however, was followed by a constant decline in the curve between 2015 and 2019, as depicted in It is agreed upon by scientists and economists that innovation is vital for countries striving to evolve into a knowledge-based economy. Saudi Arabia's low ranking in innovation has negatively impacted the country's international ranking owing to the strong correlation between innovation and KE indexes, as can be seen in The analysis of the relationship between the KE and innovation indexes revealed a positive and significant correlation between the two indicators, as reflected in The percentage of high-technology exports is one of the indicators of a country's innovation status. This indicator significantly impacts the country's positioning among the knowledge-producing countries and its economic performance. Patent application is another element of innovation that significantly contributes toward the innovation index ranking. The following chart shows the trend of patent applications by Saudi residents.Patents are important indicators of the direction of innovation in a country. However, when patents and inventions are converted into innovation, they create "patent-sensitive exports" that add significant value to the country's economy . The priResearch development and expenditure are also strongly related to patents and, consequently, innovation. However, the relationship is quite complex as it involves other elements such as higher educational institutions, technology use, and the industry itself . Figure 4.3.2ICT occupies a central place in the KE—it is considered as the driving force of the KE. However, there recently have been increasing calls for patent-based analyses to better understand the ICT as indicator and to foster development of the KE . ConcernTo investigate the reasons behind the current status of Saudi Arabia in relation to its ICT determinants and its relationship with patents' application and innovation, the researchers built the regression model described below.4.3.2.1•Innovation Index•Patents applications by residents•Research and development expenditure, percentage of GDP•Internet use, percentage of the population•High-tech exports, percentage of manufacturing exports•Mobile phone subscribers, in millions•Capital investment, percentage of GDP.Selected variables:The correlation matrix above see  summarizInnovation, patents, and research expenditure are usually associated with higher education outputs. The education provided by universities is part of the "innovation system," which is discussed by 4.3.3Owing to the difficulty in obtaining information about gender in the context of innovation, patents and ICT, the research team examined the relationship between education and these indicators. Innovation and patents are usually closely correlated with education's outcome. They also play a crucial role in the country's ranking in the KE. Therefore, when moving to a knowledge-based economy, the government emphasizes specialties such as natural sciences, ICT, and mathematics . However, such specialties are not strategically planned in the Saudi context as graduates of those specialties, according to our data, have been absorbed by sales-related jobs see . This fiThe results show that patent applications by residents were significantly and positively correlated with local postgraduates for all genders (P < 0.05). The same finding was not arrived at regarding doctorates in relation to patents. There is no significant correlation between the holders of master's and doctorate degrees and innovation. This interesting result highlights the importance of examining the nature of the education of those postgraduates.To better understand the relationship between the nature of education and innovation and patents, the researchers considered the local graduates in science and information technology (IT) Tables  and 5.Ta5The increasing international emphasis on knowledge as a driving force for economy, wealth, and job creation has directed Saudi Arabia's pursuits toward developing a high-skilled and high-value economy. The Saudi government's realization of the growing value of knowledge as an economic asset has also led to a strong emphasis on transitioning from an oil-based to a knowledge-based economy . This reThis study is a step toward having a better understanding of Saudi women's status in relation to the five most critical KE pillars—education, employment, HDI, innovation, and ICT, the first research objective. The findings confirm that the Saudi government is on the right path with its initiative to empower women through education. Education has witnessed a high percentage of women enrollment, especially at post-secondary level, which is considered as the bridge to the labor market. Nevertheless, a discrepancy was noted between women's education and employment. With a female unemployment level of more than 30%, the Saudi government is faced with great challenges. Surprisingly, more than 40% of the unemployed Saudi female graduates are bachelor's degree holders, who can be enlisted under the umbrella of the KE . Hence, to improve its current low ranking on KE indexes, improve the GDP and transition to a knowledge-based economy, the Saudi government should revisit unemployment in general and among women in particular and introduce appropriate actions to change the status quo. These efforts should be directed toward rehabilitating unemployed women, reskilling/upskilling them, especially with STEM-related degrees, and including them in KE-related jobs.In this context, it must be noted that job creation under the KE is not a simple, straightforward process. It is a complicated interaction between different actors including universities, government, and industry. The researchers have noted an inconsistency in the Saudi ranking under the pillars of the KE; while Saudi Arabia has a good ranking in education, HDI, and most of the ICT enablers, its ranking in innovation and patents is poorer than most of the G-20 countries . This fiImportantly, for a country struggling to increase women's labor market participation, not attending to the gender dimension under ICT and innovation is quite alarming. The lack of correlation between the education output and the vital elements of the KE, such as patents and innovation, indicates a missing link in and deviation from the long-established relationship between education and innovation and between innovation and GDP. This study highlighted the lack of higher education's contribution toward innovation, the answer to the third research objective.61.The Saudi government should emphasize the role of higher education and increase the competitiveness within its establishments. This can be done by introducing a system of incentives that accelerates the process of knowledge production and dissemination.2.The Saudi government should also utilize its robust ICT infrastructure and ensure ICT adoption.3.It should view innovation as an end-to-end process that starts at educational establishments and ends in the local and international markets.4.Given this, the government should create a sophisticated knowledge market that can absorb the higher education outcome and utilize it.5.Saudi Arabia must invest in HD, particularly in higher education, which leads to innovation and accelerates the country's transition toward a knowledge-based economy.6.The lack of public gender-related information under specific KE indicators  will impact the country's transformation process, given the significance of big data in charting future economic plans—evidence-based policymaking.7.There is an urgent need to carry out a nationwide survey to analyse the nature of the available jobs in the labour market and their relation to knowledge. Understanding the nature of the currently available and the needed jobs is a critical step towards matching them with the output of higher education. It also presents an essential step in providing relevant training to reskill or upskill the HE graduates.The study concludes with several recommendations that can be summarized as follow:Sabria Jawhar; Sajjadllah Alhawsawi; Asaad Jawhar; Mohmmad E. Ahmed: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.Kholoud Almehdar: Performed the experiments; Contributed reagents, materials, analysis tools or data.Ministry of Education [RJ20/009/J].Dr. Sabria Salama Jawhar was supported by Data included in article/supp. material/referenced in article.The authors declare no conflict of interest.No additional information is available for this paper.


The development of AI technology has a significant impact on every sector of business. Artificial intelligence uses this technology to reduce the amount of work required, duplicate work, and increase the accuracy of work by modelling human behaviour and thought. On the basis of a thorough analysis of artificial intelligence technology, the technology is applied to teaching, specialised training, and specialised testing in response to the current issues in the field of physical education and the business that can be improved through auxiliary tools. The paper discusses how artificial intelligence technology can be used in physical education. After that, it examines how physical education is currently taught in classrooms and discusses how artificial intelligence is affecting this field. Following a thorough demonstration of the aforementioned material, the commonly used artificial intelligence technologies are introduced in turn, along with the methodology used in physical education. The use of computer models in physical education is then explained, and future analysis is conducted on the basis of this information. Artificial intelligence, which can simulate, extend, and expand human intelligence, is a field of study built on the foundations of many other fields, including computer science, cybernetics, information theory, systems science, and philosophy. In essence, computer science has a subfield called artificial intelligence. The public generally accepts the quick development of computer application technology because it exhibits greater intelligence and applicability. Due to the country's top-notch design and effective artificial intelligence policies, the characteristics of artificial intelligence application and physical training education are more closely related and support one another. The rapid development of artificial intelligence is an indispensable backbone in the process of accelerating the modernization of education benefit. The data support of artificial intelligence in education and teaching has grown steadily stronger with the advancement of Internet of Things technology. More thorough scenarios are provided, along with novel application scenarios. Machine learning is now the primary artificial intelligence research area and application paradigm. Intelligent processing is based on precise data processing and is taught through data training sets. An unavoidable trend in modern development is the advancement of artificial intelligence in subject education, and this trend will significantly advance school sports reform and development , 2.There are numerous ways to categorise sports. This paper divides sports into three categories based on their use as application objects for artificial intelligence: competitive sports, school sports, and mass sports. The application status of AI in these three categories of sports is briefly described as follows.Competitive sports require high accuracy, and in the process of competition, both parties need to follow certain sports rules to complete. It is characterized by sports competitions and is a social sports activity with the main goal of giving full play to the spirit of sports competition based on sports rules. At present, artificial intelligence , 4 is wiWith the continuous integration of artificial intelligence and the sports and fitness market, a large number of intelligent fitness products have gradually entered the consumer market. Wearable smart devices such as smart bracelets and smart sports shoes are gradually appearing in people's daily fitness. With the rapid update and iteration of smart products entering the consumer market, the integration of AI and mass fitness has been accelerated.At present, the application , 6 of arIt is an indisputable fact that the quality of school physical education is not high and the teaching efficiency is low. Teachers can only rely on personal experience and cognition to carry out teaching. The teaching design, content setting, and adjustment lack an effective scientific basis, and there is a trend of random teaching. The effect of physical education is limited by many shortcomings. For example, teachers' explanation and demonstration are limited by the influence of skill level, physical quality, and age, and the demonstration effect is difficult to guarantee. Teachers do not understand the specific needs of students and the basics of physical education and carry out unified progress teaching. Students learn in confusion and passive indoctrination, resulting in students' physical problems such as poor physical learning atmosphere and strong resistance.The combination of artificial intelligence and physical education is a new trend in contemporary school physical education . AccordiSmart wearables may be advertised as jewellery, watches, clothing, or other accessories. It makes use of contemporary communications and sensors to record a range of bodily life data and track the health of the human body. Basic characteristics of wearable artificial intelligence products include portability, pertinence, real-time, and safety . Smart cThe application of sports vision technology in physical education can accurately and intelligently identify the actions of students  and solvVR is a computer simulation system that can create and experience virtual worlds, and users can get the most realistic feeling in the illusory world. The huge potential is shown by VR technology. With the maturity of technology, it has been successfully applied in the field of competitive sports and achieved good results. With the advent of the VR era, it makes the traditional school physical education ideas, plans, and methods have a greater impact and also provides better opportunities for physical education improvement. The application of VR technology in physical education can change the traditional teaching and learning methods, solve the problems of poor teacher's explanation and demonstration effect, lack of situational guidance and students' difficulty in imitating and understanding under the traditional teaching mode, so that students can obtain the experience close to the real situation , 11. TheArtificial intelligence empowers school physical education, which can bring beneficial changes and positive effects to teachers' teaching mode, student learning mode, teacher's teaching evaluation, student learning evaluation, and student learning effect.Clarifying teaching objectives, explaining and demonstrating concepts, and guiding and summarising learning activities are all part of the traditional physical education teaching process. The core of the physical education teaching method is comprised of the teacher's explanation, demonstration, and practise guidance. The influence of skill level, physical quality, and age limits the impact of a teacher's action demonstration. It can be challenging to ensure that students are learning something new because teachers frequently find it difficult to teach complex movements through demonstrations and must instead rely solely on their own explanations . In addiTraditional physical education classes do not engage students because they do not feel like themselves. The fact that teachers are unable to provide students with individualised instruction based on their unique foundations and receptive abilities is one of the key contributing factors. Students learn in confusion and lack motivation because teachers lack a clear understanding of the fundamentals of physical education and the needs of individual students. A new avenue for students' physical education learning is opened up by the integration of artificial intelligence technologies like sports vision, smart wearables, and VR. Utilizing wearable technology, virtual reality (VR) technology can gather knowledge from students and acquire real-time, multiperspective image information. It is possible to provide detailed evaluation and information on the three-dimensional motion. To help teachers carry out targeted teaching design and guidance so that students can transition from a unified learning mode to a personalised learning mode, artificial intelligence technology enables big data of students' movement to be obtained in real time and the quality of movement to be fed back in real time. The interaction between teachers and students can be carried out effectively with the help of artificial intelligence, and students can now learn in a personalised way, which has a significant positive impact on their motivation and interest .An efficient way to raise the standard of physical education instruction is to evaluate the teaching abilities of teachers. The two main aspects at the moment are student evaluation and supervision evaluation. There are some issues with the supervision evaluation, such as the evaluation's single subject, subjective results, flashy content, and failure to demonstrate the improvement's goal , which hAt present, teachers' evaluation of students' learning mainly includes process evaluation and summative evaluation. Summative evaluation is the examination evaluation, which has specific and quantifiable evaluation indicators , 16,. ThArtificial intelligence models and algorithms are used in physical education work. The commonly used machine learning models  are descThe traditional neural network achieves feature extraction through multiple activation function compounding, splicing, layering, and other steps. The automatic learning of the network reduces the workload of manual feature extraction, but the feature model extracted by the traditional neural network is one-dimensional, which is not suitable for some. Features that are spatial in nature are very disruptive. For this reason, scholars have proposed Convolutional Neural Networks (CNNs). In recent years, CNN has become a hot spot for scholars to study artificial intelligence, and it is mostly used in the standardized monitoring of sports training movements.In the course of physical education, mistakes are frequently made during the movement process after students learn the movements for the first time because the connection between the nerves and muscles of the main body of the movement is not yet precise. Physical education teachers must therefore learn how to correct and prevent incorrect movements in order to enhance the quality of their instruction in sports technology. According to pertinent research and investigation, in the past, it was difficult to accurately identify incorrect movements during physical education training, which made it difficult to correct them in a timely manner. Therefore, the convolutional neural network can be used as the basis for improving the wrong action detection process in physical education training through deep learning by adding a batch normalisation layer in the middle of the convolution layer and the pooling layer. This will address the aforementioned shortcomings. Standardize incorrect action samples from physical education training in batches, extract incorrect action features from these, quickly and precisely identify incorrect actions from physical education training, and raise athletes' sporting proficiency.The specific network model structure is shown in The calculation formula of the output of the convolutional layer is shown in the following equation:yjm represents the output value of the j-th unit in the m-th layer of the convolutional layer, xim−1 is the i-th matrix input in the m − 1 th layer in the layer, kijm−1 is the convolution kernel that connects the input i-th matrix and the j-th unit between the m-th and m − 1 layers in the convolutional layer; bjm is the feature map output by the m-th layer. Bias value for j cells. The feature matrix calculated by formula  , 19 takeW represents the weight value from xt to ht, U represents the weight value between the hidden layers ht−1 and ht, and V represents the weight value from ht to the output yt. xt represents the input training sample of the t-th layer; ht represents the hidden feature of the t-th layer, and its value is calculated by xt and ht−1; yt represents the output of the t-th layer, which is only determined by ht. The calculation formula of the RNN model is shown in (shown in  and 4)..W represEquation  represenThe CNN model described above has the property of context memory, but when CNN processes long sequence data, it uses backpropagation to transfer the gradient of this layer to the previous sequence, so CNN cannot memorize long texts and is not suitable for some Long sequence data processing work. To solve this problem, with the continuous improvement of CNN, some improved models have been gradually proposed, of which Long Short-Term Memory (LSTM) is the most widely recognized network.Unit state: indicates the internal memory information of the network unitHidden state: the external hidden state acts on the prediction results of the model calculationInput gate: determine which external all inputs can currently be sent to the unit stateForgetting gate: in the previous unit state, decide which ones can be sent as input to the current unit stateOutput gate: in the current state, decide which units can be input to the hidden stateLSTM is regarded as a variant of a more advanced CNN network, which consists of 5 main parts. In the LSTM model, the current unit state is affected by two aspects: one is dependent on the input in the current state, and the other is dependent on the previous unit state, so the LSTM can perform each element in the unit state at a certain moment. Update or forget processing, forming a stable state-dependent mechanism.In the LSTM model, the unit state calculation of the forget gate is calculated by the following equation:ft is the information lost by the forget gate at time t, ht − 1 is the output state of the hidden layer at time t − 1, xt is the current input, T is ht − 1 and xt A matrix is formed, which is multiplied by the weight matrix ωf, and then added with bf, (bf is the bias matrix).Here, ct can be calculated, and its calculation formula is Then, ct is the current unit state at time t, and ct−1 is the unit state at time t − 1. ct is represented by equation it=σωbi and bc are bias matrices. ω and ωc are weight matrices. σ is the activation function used.In equations  and 8),,8), bi aLSTM can obtain the features of past time series information. In some processing work, future time series information features are required, so a Bi-LSTM model composed of forward LSTM and post LSTM is proposed. The Bi-LSTM model is shown in Due to the shortcomings of RNN in the model calculation, such as many parameters, long training time, and complex calculation, in order to improve the processing efficiency of the model, scholars have proposed a simpler model GRU (Gated Recurrent Unit)  on the bxt at the current moment and the memory information, and the update gate is used to determine the memory content saved above. The reset gate output rt is calculated as equation xt at thePhysical education is currently at a stage where the use of artificial intelligence technology is typically in its early stages. Both the consumer demand for sports artificial intelligence equipment and the sports database that serves as the foundation for physical education enabled by artificial intelligence are still in their infancy. In addition, there is no established regulatory framework for smart devices. Since physical education is a relatively new field, its fundamental theoretical foundation is still lacking. It is essential to continue promoting the development and advancement of sports artificial intelligence equipment. Develop intelligent equipment and support the deep integration of equipment and physical education. In terms of policy, it is necessary to direct or create pertinent incentive policies and provide corresponding support in line with industrial policies. From a technical standpoint, a physical education mechanism that combines data resource sharing and business model sharing can be established, and through technological innovation, the drawbacks that the general public are concerned about, like the protection of personal data, can be resolved. In order to increase consumer interest in purchasing sports artificial intelligence equipment, it is necessary to increase the marketability and cost-effectiveness of products. Artificial intelligence-based sports equipment is in extremely high demand, particularly in the fields of school sports and public fitness. Market demand serves as a catalyst for advancing industrial chain efficiency and technological advancement. A complete production in the field of physical education may result from the development and improvement of the random intelligence algorithm and model. To achieve the technology empowerment effect, artificial intelligence equipment costs will move in the direction of civilianization and affordability. Long-term challenges include how to successfully integrate artificial intelligence into the field of physical education in schools. Artificial intelligence can now create more network models thanks to machine learning, which is also being used more and more in physical education. People who work in sports should actively adopt new technology. Computer-assisted instruction, action demonstration, and student action monitoring are all new concepts in physical education. Real-world sports training data collection has become more precise and scientific with the aid of Internet of Things technology. Every person who works in physical education should actively investigate how computer and artificial intelligence technology can be applied to all facets of physical education.


We read with interest this review by Ramai et al. , who comFirst some background for context: It is well known that patients with two different types of porphyria, acute intermittent porphyria (AIP) and porphyria cutanea tarda (PCT) have an increased risk of primary liver cancer (PLC), mainly HCC. PCT, the most common type of porphyria, is in most patients an acquired enzyme deficiency caused by underlying liver diseases such as hepatitis C or alcohol-related liver disease. A minority have an inherited enzyme deficiency that causes a disease with similar clinical manifestations. AIP, variegate porphyria (VP), and hereditary coproporphyria (HCP) are commonly grouped together as acute hepatic porphyrias (AHP) due to similar clinical and biochemical features. The AHP are inherited monogenic diseases with low penetrance that affect different enzymes in the heme synthesis pathway and potentially lead to the accumulation of the heme precursors delta-aminolaevulinic acid (ALA) and porphobilinogen (PBG).AHP and PCT are completely different diseases, and the carcinogenic pathways differ significantly. While patients with PCT have an underlying liver disease that may lead to an increased risk of cirrhosis and PLC, AHP patients rarely have significant liver fibrosis. Most AHP-associated PLCs are diagnosed in non-cirrhotic livers and the current understanding is that the carcinogenesis pathway is related to the intrahepatic accumulation of ALA ,3,4. SevWe have several comments on the contents of the review.To start with, the authors chose to include studies on both PCT and AHP in this review: 10 studies include only AHP, 7 only PCT, and 2 studies include both AHP and PCT patients. If doing so, the cohorts must be assessed, analyzed, and discussed separately. To combine cohorts with AHP and PCT, and to only present the common risk estimate is comparable to combining patients with, for instance, viral and autoimmune hepatitis, two diseases with fundamentally different pathogenesis, and only presenting the combined risk estimate.In the first statement of the discussion, the authors state that “patients with AHP had an estimated 5% risk of primary liver cancer”. This is incorrect, since the 5% risk estimate reflects the combined risk of the AHP and PCT cohorts. The risk in AHP is not assessed in this review.In the final paragraph of the discussion, the authors write that “This study provides further evidence to the current understanding of HCC in patients with AHP. This extensive review includes 19 studies from multiple countries, supporting screening guidelines in this patient population”. Again, only the term AHP is used. Only 12 of those 19 studies included patients with AHP, which makes the reference to AHP incorrect. Furthermore, PCT, the porphyria type included in nine of the studies, is not mentioned at all in the discussion. It is unclear whether the authors included the PCT studies erroneously as AHP, or if those studies were in fact not included in the final conclusions.It is hard to follow which patient group that the authors refer to in different parts of this review. The vague term “porphyria” is used alternately with the more precise AHP in an imprecise and, in some instances, erroneous way. Two examples:Neither age nor disease severity are included in the analysis or the discussion, in this study, which makes the risk assessment less accurate. Cohorts included in this review vary significantly in terms of demographics and clinical disease severity. Age is a significant risk factor for the development of HCC . As an eThe main aim of this review was to assess the PLC risk in “porphyria”. Including both PCT and AHP is, as mentioned above, of doubtful relevance. The results nevertheless deserve a thorough examination. The stated 4.8% risk of PLC in “porphyria” was calculated by dividing the total number of patients with PLC (351) by the total number of patients at risk (7381). That estimation is based on incorrect calculations.Starting with the numerator, there are significant errors. The authors included 102 cases of PLC from the study by Hardell et al. from 1984 . This caWe also object to the authors’ approach to the issue of overlapping studies. In the methods, the authors state that “In the event of multiple publications from the same cohort or overlapping cohorts, data from the most recent and/or most appropriate comprehensive report were retained.” This is an important principle that was unfortunately not followed throughout this review.The important study by Baravelli et al. from 2017  on PLC rMany of the studies from Sweden have significant overlap but none were excluded. For example: Lithner 1984  overlapsn = 7381), patients at risk, we again find several significant errors:The 103 cases in the Hardell study  are consAccording to Table 1 in the review  are inclThe authors state that the porphyria cohort size in the study by Andersson 1996  was 2122Concerning the Innala 2011 study , the staConcerning the Lang 2015 study [Continuing with the denominator  is of interest as a possible tool in HCC surveillance. The EASL guidelines  do not rFinally, in the results (Table 2) and in the discussion, the authors state that 56  liver transplantations were performed with PLC as an indication, referring to Elder et al. . This isA list of additional comments regarding this review are presented in Considering the lack of fact-checking, the amount of inaccurate data included in the analyses, and the poor understanding of the different disease entities included in this review, we suggest a thorough revision of this paper. We strongly recommend including researchers with porphyria experience in the revision process, and to invite reviewers with such expertise to ensure a rigorous peer-review process. In its present form, this review by Ramai et al. misleads the readership.


Social Media Virtual Companion” for educating and supporting an entire community, teenage students, to interact in social media environments in order to achieve desirable conditions, defined in terms of a community-specific and participatory designed measure of Collective Well-Being (CWB). This Companion combines automatic processing with expert intervention and guidance. The virtual Companion will be powered by a Recommender System (CWB-RS) that will optimize a CWB metric instead of engagement or platform profit, which currently largely drives recommender systems thereby disregarding any societal collateral effect. CWB-RS will optimize CWB both in the short term by balancing the level of social media threats the users are exposed to, and in the long term by adopting an Intelligent Tutor System role and enabling adaptive and personalized sequencing of playful learning activities. We put an emphasis on experts and educators in the educationally managed social media community of the Companion. They play five key roles: (a) use the Companion in classroom-based educational activities; (b) guide the definition of the CWB; (c) provide a hierarchical structure of learning strategies, objectives and activities that will support and contain the adaptive sequencing algorithms of the CWB-RS based on hierarchical reinforcement learning; (d) act as moderators of direct conflicts between the members of the community; and, finally, (e) monitor and address ethical and educational issues that are beyond the intelligent agent's competence and control. This framework offers a possible approach to understanding how to design social media systems and embedded educational interventions that favor a more healthy and positive society. Preliminary results on the performance of the Companion's components and studies of the educational and psychological underlying principles are presented.Social media have become an integral part of our lives, expanding our interlinking capabilities to new levels. There is plenty to be said about their positive effects. On the other hand, however, some serious negative implications of social media have been repeatedly highlighted in recent years, pointing at various threats to society and its more vulnerable members, such as teenagers, in particular, ranging from much-discussed problems such as digital addiction and polarization to manipulative influences of algorithms and further to more teenager-specific issues . The impact of social media—both at an individual and societal level—is characterized by the complex interplay between the users' interactions and the intelligent components of the platform. Thus, users' understanding of social media mechanisms plays a determinant role. We thus propose a theoretical framework based on an adaptive “ Looking at the field more broadly, the freedom to post whatever someone judges useful has been described as nothing less than a shift in the communication paradigm  .With this motivation, in this paper, we articulate a framework for educating teenagers in their interaction with SM and synergetically improve and support their experience based on a “CWB metric evaluated directly on the Companion and used as an optimization target by its integrated recommendation engine (CWB-RS), which will allow the support and educational management of the local social media community  definition specific for their community (see Section 4). This will support the coherent formulation of community regulations, objectives and educational activities that involve several ethical issues entailing the definition of boundaries and trade-offs to own personal behavior online (see Section 4.1.1), such as enabling collective satisfaction and preserving the right to free speech  that are grounded in behavioral psychology . This is where boosts come in as an alternative approach. Boosts focus on interventions as an approach to improve people's competence in making their own choices  by interacting with narratives, counter-narratives, and peers. These tasks can be different activities, including free-roaming inside the platform, guided roaming following a narrative, quizzes, playing minigames, or participating in group tasks. Different counter-narratives can be triggered depending on students' detected behavior  and brings forward the positive aspects of an open society and focuses more on “what we are for” and less on “what we are against.” The users will be informed and requested to actively and socially contribute to creating and sharing content and material that fosters and supports the idea of an open, unbiased and tolerant society. Thus, the games can also offer the chance to build connections between the users, which, when isolated, are more vulnerable to online toxic content. One approach is to propose periodically specific tests and activities related to each threat, such as Szymanski et al. .A use case scenario is presented in Section 6 and the outcomes of several pilot studies that lie the basis for the educational activities are presented in Section 7.1.The Companion's location allows it to act as an interface between the educationally managed social media community and the external one. It permits mitigating the effect of external toxic content and offers the opportunity to recreate different interesting experiments about SM phenomena, such as the ones presented in Bail et al.  and StewThe Companion's autonomous mechanisms will support the students in interacting with the social media content both inside (as a support learning activities) and outside  of the classroom. The Companion interface exposes its filtering and recommendation algorithms by allowing direct control of their parameters  has been proposed as an index of the economic well-being of a community.social and emotional literacy in Spratt . They include community members' individual well-being incorporating diverse domains, such as physical and mental health, often stressing the presence of positive conditions. They study which properties of the community affect the members and how much each of these properties adds to a comprehensive measure of collective well-being. We already stressed the importance of education and educational objectives to support constructive interactions and achieve desirable community conditions, i.e., a high level of well-being. However, education itself is often already part of well-being frameworks White, . The conn Spratt .Roy et al.  present Opportunity domain is related to “the perceived opportunity to achieve life goals and socioeconomic mobility”  and negative ones (see Section 2) need to be explicitly considered as part of the CWB as they strongly affect social media users and in particular teenagers.collective well-being metric for social media by combining suitable components of classical CWB and SM threat measures. The measures of these components could be measured by periodically proposing specific surveys and activities  Content Shared measures the aspect-specific value of the content shared by the user;CE Content Exposure measures the aspect-specific value of the content observed by the user;CC Contact Creation measures the aspect-specific value of new connections based on the participants' CS and CE.These elements account for the double role of each member of the social media community as both receivers and producers of content. In our educational setup, where only the community of interest is in contact with an external social media community, we distinguish between “endogenous” and “exogenous” aspects. The community can be exposed to threats that are generated outside but a community can also generate such threats inside as part of the interactions in the social medium. In this case, the feeds from external sources may be weighted differently.While the CS can be seen as a direct expression of the state of the user, it strongly depends on the user's style of interaction. Moreover, only relying on the content shared by users would induce a substantial delay compared to the moment when a user got actually affected by observing a piece of content (CE). Conversely, the user is exposed to a multitude of diverse inputs hindering the interpretation of the overall effect only from the CE, while the user's reactions (CS) may be more indicative of the most impacting events. Indeed, current affective state estimators and toxic/positive content detectors can only provide noisy estimations of the current user state and the content quality. However, the availability of complementary data with higher reliability is limited.Once each event is scored for each aspect of interest, it must be decided how to aggregate these terms over users, time, and the different aspects to obtain an estimation of the total CWB of the community. Indeed, the definition of an actual metric following this strategy requires making a number of choices. For example, about the scale for the terms of different aspects considered. Regarding aggregation over time, CC, CE, and CS values could be simply averaged. Other approaches could be considered to take into account the frequency of the events or the diversity of opinions presented or give more relevance to extreme events, which may be more accurately detected and evaluated. In particular, the value of being exposed to multiple opinions (time-aggregated CE) may be augmented with a measure of diversity   may lead to overuse of the platform or tire the user who will lose the opportunity to learn about more important content .The most complex aggregation to design is over users because it has to balance the well-being of different individuals and groups of users taking into account their conflicting interactions along different dimensions. It is important to consider the different features of each user while respecting privacy constraints. For example, vulnerable users are often victims of toxic content but also producers Bessi, , which aApart from the weighting issue issues, another important format issue is the selection of the actual aggregation function across users. Adopting the naive average a society where a few radicalized users share extremely hateful content may have a higher CWB score than one with a number of users sharing content about action movies with slightly violent scenes. Another reason why a linear combination of components may not be suitable in the definition of a well-being measure is that it will simply induce maximizing the terms with positive weights and minimizing terms with negative ones, without allowing a balance. For example, if interactions between drastically opposite opinions are considered negative because of possible backfire effects and flames . The CWB-RS extends social media RS intending to maximize the cumulative long-term CWB metric instead of self-referential platform objectives. Compared to previous efforts in dealing with possible negative effects of RSs  are ubiquitous in online activities and are crucial for interacting with the endless sea of information that the Internet and social media present today. In social media platforms, they have introduced the possibility of personalizing suggestions of both content and connections based on the use of user profiles containing also social features  Intelligent Tutoring System  evaluate the future effects of different sequences of content re-rankings and recommendations through predictive models of users' conditions; 4) select the actions that account for the highest expected, long-term, cumulative CWB metric.As shown in content analyzers and threat detectors allowing to easily combine educational and regular CWB objectives . They can be encoded in terms of measures related to specific threats or other well-being variables, such as those extracted by high-level CWB-RS educational strategies will be hand defined by the educators and experts to choose between the different educational objectives for each student in an effective and contextualized manner. Lower-level educational strategies for the CWB-RS comprise hand-defined learning activities and minigames as well as modulation of the recommendations, for example, showing diverse content as tests to explore students' preferences.Educators and experts will also define interaction strategies specific to each objective  paradigm adoption to drive the adaptation and personalization of the CWB-RS behavior  framework  on which the RS must act. This results both in erroneous recommendations  and stochastic rewards . While the RL method accounts for noisy actions' results, they still affect the performance of the system, both in terms of execution and learning time. Regarding noisy rewards, literature has only recently started to provide solutions   is available  and personality traits . A link between learners' judgments and personality traits could only be weakly found for authoritarianism  or not (control group), we will analyze the effects on users' knowledge and awareness regarding racism.Also, we are constantly working on a scenario for empathy training which shall sensitize young users regarding the negative effects of cyberbullying. For this training, for instance, a video showing an example and providing a definition of empathy will be shown to students in an experimental study in Germany and Spain. We hypothesize that students who completed the empathy training will be more sensitive to cyberbullying and are less likely to intend to bully in the future.Finally, in Taibi et al.  we preseSeveral components and applications, that will later be integrated in the Companion, are being developed and tested.A number of mini-games to increase social media awareness, covering topics such as the digital footprint, social media addiction, misinformation and body image dissatisfaction have been designed and tested. For instance, the serious mini-game “SwipeIt” for sensitizing students to toxic content , was endowed with additional features like a multi-language interface.One of the scenarios using the Companion aims at raising learners' awareness of fake content in an Instagram-like social media environment . The first results of the data collected evaluate how the adaptive educational intervention embedded in the Narrative Scripts facilitates a suitable approach to educating adolescents about body image and stereotyping in social media. In particular, the analysis examines and compares approaches to identify the dominant body image stereotype in students' social media. Results showed that the use of xAPI (tracking user behavior in Pixelfed) combined with self-reported answers can provide a satisfactory detection of adolescents' educational needs, so as to enable automatic distribution of suitable counter-narratives (out of a collection) to students in the scripts , which sequences educational activities and balances the content presented to the students in order to maximize the CWB (see Section 5).From a technical point of view, the problems are multiple. Starting from the formulation of the CWB measure, the number of aspects to balance and the likely non-linear interactions between the single and the community sub-groups will require an iterative design approach. Moreover, while the state of the art for the components that detect the relevant quantities is constantly improving (e.g., sharing of hate speech in the community, see WWW.Our contribution in this paper, in particular our experimental studies, are specifically designed for relatively small communities that can tailor the approach to their own specific needs. One may be tempted to think about scaling up the whole approach and integrating the educator in the loop, the CWB and CWBRS on the global social media platforms. This would prohibitively escalate the moderation costs that are already very demanding (Steiger et al., Currently, the international community is undertaking a substantial effort in understanding and regulate the ethical implication of AI systems EUC, . UnmistaWhile it is crucial that the international community continues its effort and targets social media Gorwa, , we highDifferently from previous other interventions with a similar aim, this paradigm enabled by the educational virtual Companion for social media has indeed the potential to provide an educational experience on a scale comparable to that of the social media platforms. Indeed, it will be challenging to define an educational path that covers most of the numerous points of interest in digital citizenship (Jones and Mitchell, Finally, this approach is the perfect means to bootstrap and test the concept of CWB-RS systems, verify their feasibility, stability and robustness, and create suitable datasets. The data collected from this initiative may not only be useful for replicating and extending this type of educational approach, but it could also be a first step to provide evidence that social media's impact on society can be improved by taking the community needs more into account in their design. A characteristic feature of social media is that they are crucially the result of a community activity, which both consumes and produces their content. It is daunting that platforms' objectives are so detached from those of the community. Therefore, we hope that our results can support the process of introducing new evidence-based regulations both for the platforms and their algorithms, beginning with requesting the platforms to release their data for scientific research and enable large-scale studies, which have been curbed after the limits they recently set following Cambridge Analytica and other scandals Hemsley, .The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.DO conceived of the presented ideas and concepts and contributed to the design of the educational platform architecture. RW and GD supported, revised the design, and developed the content machine learning aspects. UK proposed, described the integration of behavioral economics methodologies for education, and supervised the information retrieval aspect of the contribution. FL and SB helped with the aspects of network dynamics and dataset collection. DH-L, JS-R, ET, and RL contributed with the proposal of playful educational methodology, the cooperative social media design, and narrative scripts. UH, NM, FA, VS, JB, and SE overviewed the conceptual development. DT contributed to the design of the educational platform architecture, DT and LS contributed with expertise on digital addiction and digital literacy intervention design. All authors contributed to the as article and approved the submitted version.


Kidney transplantation (KT) is the best treatment for chronic kidney disease. In Brazil, there are currently more than 26 thousand patients on the waitlist. Kidney Paired Donation (KPD) offers an incompatible donor-recipient pair the possibility to exchange with another pair in the same situation, it is a strategy to raise the number of KT.KPD ceased being merely an idea over 20 years ago. It currently accounts for 16.2% of living donors KT (LDKT) in the USA and 8% in Europe. The results are similar to other LDKT. It is a promising alternative especially for highly sensitized recipients, who tend to accumulate on the waitlist. KPD is not limited to developed countries, as excellent results were already published in India in 2014. In Guatemala, the first LDKT through KPD was performed in 2011. However, the practice remains limited to isolated cases in Latin America.KPD programs with different dimensions, acceptance rules and allocation criteria are being developed and expanded worldwide to meet the demands of patients. The rise in transplantability brought about by KPD mostly meets the needs of highly sensitized patients. The Brazilian transplant program is mature enough to accept the challenge of starting its KPD program, intended primarily to benefit patients who have a low probability of receiving a transplant from a deceased donor. Kidney Transplantation (KT) is the best treatment for patients with end stage kidney disease, offering better life expectancy and quality of life to patientsThe number of KT performed in Brazil is increasing, although it is still less than half of the annual need estimated by the Brazilian Association of Organ Transplantation. Thus, the number of patients on the waitlist grows annually, having surpassed 26 thousand in 2020The likelihood to receive a KT becomes even smaller for highly sensitized patients. Before the new allocation policy in the US, the rate of KT with a deceased donor decreased drastically in patients with panel-reactive antibody (PRA) higher than 80% (72% reduction for every 10 points added to the PRA),,,-Living donor KT (LDKT), regardless of kinship, offers better patient and graft survival, presenting an alternative to increase the number of organs offered,Kidney Paired Donation (KPD) offers incompatible donor-recipient pairs the possibility to exchange with another pair in the same situation and performing the transplants, benefiting both recipients-Since the 2000s, KPD programs have been expanding globally, and exist in different types and sizes, and as local, regional or national programs,,,The first description involved only a simple swap between two incompatible pairs. With experience, different ways to perform the exchanges were developed to optimize the benefits for the recipients enrolled for KPD"Closed chains", involving 3 or more pairs, carried out in a way similar to what was described by Rapaport;"Endless chains", started by a deceased or non-direct donor and the last living donor donates to a recipient on the waitlist or becomes a link to begin a new chain in the future;Exchanges involving a deceased donor, in which the incompatible living donor donates to a listed recipient and, in exchange, the incompatible recipient is prioritized on the waitlist;Unbalanced exchanges, when a compatible pair chooses KPD seeking a benefit ;Advanced donation, when there is a chronological incompatibility between donor and recipient, the donation is conducted to a recipient on the waitlist or to start of a new chain. The recipient is given a "voucher" to be prioritized when necessary.,The transplantation capacity of a KPD program depends on the number of pairs registered, on the rate between pairs with ABO incompatibility and positive crossmatch, on the sensitization level of recipients, on the algorithm used for allocation , on the accepted performance models, and on the frequency of match runs for pair allocation,With that in mind, in many countries, the programs are performed on a regional or national level, mainly favoring highly sensitized recipients who tend to accumulate on the waitlistIn 1999, a South Korean study presented their KPD results: patient and graft survival in 5 years were similar to that of haploidentical LDKT and there was no difference in acute rejectionThe first kidney exchange in Europe was conducted in Switzerland in 1999, and 5 years later, the national KPD programs from the Netherlands, the United Kingdom, and Canada had facilitated more than 200 LDKT in each country (29-44% of those registered)In 2016, there were 10 KPD programs in Europe, with different sizes and criteria for acceptance and allocation. Until that year, more than 1300 transplants had been performed in the programs, representing 8% of the LDKT in the continentIn 2020, 20 years after the first transplant with KPD in the US, the outcomes in up to 7 years of the transplanted recipients were analyzed through the National Kidney Registry (NKR), compared to other LDKT recipientsKPD programs are no longer limited to developed countries: in 2017, more than 300 LDKT had been facilitated through KPD in IndiaIn 2010, during the Transplantation Bioethics Forum supported by the Latin American and Caribbean Transplantation Society (STALYC), the "Aguascalientes Document'' was drafted recognizing the legitimacy of KPD( 35).One of the main concerns of programs dedicated to creating longer chains is the possibility of withdrawal by the donor after the start of exchanges, as it is difficult to conduct the procedures simultaneously. In 2017, a study assessing such "break" in the chains of KPD was published. In the analysis performed by the NKR on transplantations carried out in the USA between 2008 and 2016, it became evident that the rate of chain breaks was low and mostly due to a medical contraindication on the part of donorsTo be ethically justified, the KPD program must consider the 4 principles of biomedical ethics: beneficence, nonmaleficence, justice, and autonomy. Living kidney donation (related or unrelated) is justified when beneficence outweighs nonmaleficence and donor autonomy is preserved. These principles are universal and can be extrapolated to KPDBrazilian legislation does not contemplate the possibility of KPD. Law no. 9.434 from February 4, 1997 states that the removal of tissues, organs and body parts of a person in exchancge for payment or promise of reward, as well as for frivolous motives, constitutes a criminal offenceIn 2018, the Brazilian Federal Council of Medicine issued a statement opposing the implementation of KPD in BrazilGlobally, KPD ceased being merely an idea over 20 years ago. Programs with different dimensions, rules of acceptance, and criteria for allocation are being developed and expanded, aiming to meet the demands of patients. The rise in transplantability brought about by KPD meets, especially, the needs of highly sensitized patients, with a possibility of combining KPD with desensitization protocols, seeking the best possible result for that currently vulnerable group(39 )but robust programs that are used almost everywhere in the world and show excellent results, comparable to other LDKT, despite focusing on a population with higher risk and a possible increase in CIT. Another critical issue relates to the main part of the affected population, the highly sensitized people who are sometimes referred to as non-transplantable. A national study in a single center estimated an increase of 7% in the total number of transplants with KPD (which is consistent with the results in the aforementioned countries), and an increase of more than 70% in the number of transplanted recipients with PRA > 80%We believe we have clarified in this review that, contrary to what has been said, KPD programs are no longer "controversial concepts in programs under development"Thus, there seems to be no reason for Brazil not to join KPD, even if initially only locally and then implemented regionally/nationally according to the acceptance of the centers and the necessary logistical adaptation.At the HCFMUSP, KPD research seeks to determine the percentage of living donors rejected due to incompatibility and are eligible for KPD and to determine how many recipients would benefit from such a strategy. As part of this program, the first kidney exchange was carried out in Brazil in March 2020 and 28 additional pairs are currently under evaluation.Nowadays, all the leading countries in world are practicing this procedure and continue to develop it to include more recipients thanks to their excellent results. The importance of this procedure is so great and recognized that in 2012 Alvin Roth and Lloyd Shapley received the Nobel Prize in Economics for their worldwide contribution to the development of algorithms necessary to match a large number of donors and recipients through KDPFinally, it is essential to emphasize that KPD also benefits those on the waitlist who do not have a donor, as it reduces the number of recipients waiting for an organ from a deceased donor. We believe that the Brazilian transplant program is mature enough to take up the challenge of starting a KPD program, primarily to benefit patients who have a low probability of receiving a transplant from a deceased donor.