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5 ways in which Artificial Intelligence is transforming education system

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a circuit board: How Artificial Intelligence is transforming education system


How Artificial Intelligence is transforming education system

The face of the education system has undergone a sea change in recent years. The present-day educational structure is competitive, challenging, and needs to be capable of meeting international benchmarks. The emerging technologies, such as artificial intelligence, are changing our lives as they are being put to different purposes. And just like other areas, AI is disrupting and creating an impact on the education system as well.

AI is making long strides in the academic world, turning the traditional methods of imparting knowledge into a comprehensive system of learning with the use of simulation and augmented reality tools.

Here are some ways in which AI is transforming education as we know it:

1. Effective management of administrative tasks

Through the automation of administrative work, artificial intelligence allows ample time for teachers that they can utilise to engage with students in an improved manner and assist them through the challenges efficiently. AI helps with school admissions via the automation of the categorization and processing of paperwork. It also helps with the grading of test papers as AI helps assess both objectives as well as subjective answer sheets.

This saves time and efforts of the teacher along with avoiding human errors of lapses in attention or even unconscious biases.

2. Access to quality content

Automation has also made quality education accessible to a larger population in the form of smart content. The professors can compose or design study materials customized according to the different needs of the students in different regions with the help of evolved applications of AI. The learning material can be shared in diverse forms that may consist of virtual formats such as video conferences and lectures.

In addition to the intelligent tutoring system, smart learning content created using AI will assist

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NEC OncoImmunity AS and Oslo University Hospital Team Up to Develop a Diagnostic for COVID-19 Using Artificial Intelligence

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NEC OncoImmunity AS (NOI), a subsidiary of NEC Corporation (NEC), and Oslo University Hospital (OUH) are pleased to announce that they have recently been awarded a prestigious grant from the Research Council of Norway (RCN) to develop an artificial intelligence (AI) platform that will enable the rapid design of T-cell diagnostics for emerging or endemic infectious diseases. The project will develop a novel T-cell diagnostic for the current COVID-19 pandemic to complement the current serological tests. This will improve the ability to identify immune responses and acquired immunity, which is desperately needed to deal with the COVID-19 crisis.

Current technologies involve extensive trial and error to define exactly which parts of the pathogen induces robust immunity. These so-called immunodominant epitopes need to be identified for the general population. These demanding, work-intensive and time-consuming steps are necessary to develop tests to monitor the T-cell response to viruses such as SARS-CoV-2 (the infectious virus that causes COVID-19).

Reliable diagnostic tests to identify immune individuals are critical to overcome the ever-looming threat of COVID-19. The AI-based diagnostic to be developed in this project will complement antibody tests and enable individuals who are naturally immune to the virus following infection with SARS-CoV-2 or other seasonal coronaviruses, or who have acquired immunity following vaccination, to be identified.

“Antibody tests are an important aspect of understanding the immune response to the SARS-CoV-2 infection and will remain a mainstay of its diagnosis. However, protective SARS-CoV-2-specific T-cell responses occur in antibody-negative infected individuals who have successfully resolved the infection. In addition, we may already have underlying immunity in the population due to cross reactivity to endemic seasonal human coronaviruses,” said Professor Ludvig A. Munthe Ph.D., Head of Research and Group Leader, Department of Immunology, Oslo University Hospital.

Although the technology to develop antibody diagnostics is readily

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Artificial intelligence can help protect orchids and other species

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Artificial intelligence can help protect orchids and other species
Calypso Bulbosa is classified as threatened or endangered in Europa and in several US states. It is found in undisturbed northern and montane forests, floodplains and swamps. Credit: Pati Vitt

Many orchid species are threatened by land conversion and illegal harvesting. However, only a fraction of those species is included in the IUCN Red List of Threatened Species, because assessments require a lot of time, resources and expertise. A new approach, an automated assessment developed under the lead of biodiversity researchers from Central Germany, now shows that almost 30% of all orchid species are possibly threatened. The new approach could speed up conservation assessments of all species on Earth.


Orchids are more than just decorative—they are also economically important in horticulture, in the pharmaceutical industry and even in the food industry. For example, vanilla orchids are grown commercially for their seed pods, and the economy on the northeast of Madagascar centers around the vanilla trade. But many of the approximately 29,000 orchid species face immediate threats by land conversion and illegal harvesting, resulting in an urgent need to identify the most endangered species and protect them from extinction. The global Red List of the International Union for the Conservation of Nature (IUCN) is the most widely used scheme to evaluate species’ risk of extinction. The assessments are based on rigorous criteria and the best available scientific information, making them resource-intensive and, therefore, only available for a fraction of the species worldwide. To date, only about 1,400 of all orchid species have IUCN Red List assessments.

An international team led by researchers from the German Centre for Integrative Biodiversity Research (iDiv), Leipzig University (UL), Martin Luther University Halle-Wittenberg (MLU) and the Helmholtz Centre for Environmental Research (UFZ) addressed this issue with the help of an automated assessment approach including the use