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As technology continues to disrupt industries, the education sector is also about to change. EduTech refers to the use of technology in the education industry in order to improve classroom experience and enhance educational outcomes (Frankenfield, 2018). Although the EduTech industry is still at the early phase of development, there is an increasing number of companies investing in innovative ways of education (Srivastava, 2018). For example, Microsoft has recently launched Artificial Intelligence-driven digital labs in collaboration with ten Indian educational institutions (Mete, 2019). In the near future, kids might not have the same learning experience as we used to have, and teachers might have to change their teaching methods or tools. There are four ways of how the education sector is transforming.

First, Augmented Reality (AR) in the education sector enables an immersive experience. Learning and teaching will no longer be limited by time and space. Instead, AR/VR-driven lectures will allow students to virtually experience through simulations, virtual field trips, real-life experiences. Teachers will also be able to effectively explain abstract and complex theories and concepts with the use of 3D models and digital modeling (ThinkMobiles, n.d.). For instance, in Portugal, the Polytechnic institute of Leiria is currently using AR in mathematics classes which have significantly increased student engagement and motivation (ThinkMobiles, n.d.).

Second, big data allows personalized learning. One of the limitations of traditional education used to be the lack of personalized content. However, in the near future, machine learning will be used to analyze students’ learning processes, interest and improvements in various aspects in detail (Das, 2019). By measuring progress and suggesting personalized study content, students will be able to reduce the time wasted on unnecessary content and focus more on customized learning materials (Chew, 2019). This customization of content and schedule is also expected to increase students’ participation, interests and educational outcomes.

Lastly, blockchain in the education sector might enhance security and reduce fraud as the use of blockchain can identify cheating and certificate or grade manipulation (Chew, 2019). In fact, starting in 2019, students from 18 Singaporean educational institutions will be receiving blockchain-based certificates (Chew, 2019). Additionally, blockchain will significantly reduce redundant administration procedures by instantly and automatically verifying credentials and certifications (Raval, n.d.).

 

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Nowadays, artificial intelligence-driven facial recognition technology is used for detecting criminals in public. The facial recognition technology is often used by governments and some private businesses in order to strengthen security in public and this is how facial recognition works for public surveillance (Nye, 2019). First, the police provides identifications of criminals to the database. Faces in police photos are mapped in a software system. When possible matches in public are detected by the facial recognition system, a person with matching face may be stopped or blocked by security (Nye, 2019). This way, AI-driven facial recognition protects the public by detecting criminals or terrorists.

Despite its security use of facial recognition, there have been controversies over discrimination. Especially, advocates of civil rights concern about the accuracy of the technology and high rate of errors since women and people with darker skin colours are more likely to be detected by algorithms (Woodie, 2019). This is because there is high chance of software system getting confused by makeup and dramatic tone contrast on the face. In 2018, a student from the Massachusetts Institution of Technology Media Lab presented results showing the inaccuracy of facial recognition (Woodie, 2019). According to the results, women with dark skin tone had an error rate of 34% whereas men with light skin tone had an error rate of less than 1% (Woodie, 2019). According to a head Future Advocacy, this is due to the fact that some AI systems do not have enough data of certain demographics of people (Nye, 2019). Consequently, there is a high chance of minorities being stopped by police and security although they are innocent and have not committed a crime in the past (Nye, 2019). In fact, the America Civil Liberties Union reported that 28 innocent people of Congress were arrested due to incorrect match by facial recognition (Chin, 2019).

Recently, Oakland, California, San Francisco and Massachusetts have banned the use of facial recognition technology due to privacy issues (Hamblen, 2019). Additionally, Portland is also trying to prevent private companies from deploying the technology in public (Van, 2019). With the movement to ban facial recognition, clear standard and legal framework are needed for facial recognition in order to protect people from bias and detect criminals (Nye, 2019).

 

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