8 Ways AI is Used in Education

While AI has been in the education technology space for a while, adoption has been slow. However, during the COVID-19 pandemic, virtual learning forced the industry to shift. AI helps streamline the student education process by offering access to suitable courses, bettering communication with tutors, and giving them more time to focus on other life aspects.

AI enhances the personalization of student learning programs and courses, promotes tutoring by helping students improve their weak spots and sharpen their skills, ensures quick responses between teachers and students, and enhances universal 24/7 learning access. Educators can use AI for task automation, including administrative work, evaluating learning patterns, grading papers, responding to general queries, and more. Here are eight ways AI is used in education.

1. Creating courses

A lot of time and money goes into creating learning courses via a central department. The use of AI streamlines the course creation process, speeding up the process and reducing costs. Whether you’re using premade templates or starting from scratch,AI software for creating courses can help create interactive content seamlessly. You can efficiently work with your entire team via in-app comments from reviewers and co-authors to create perfect training material.

AI simplifies and accelerates course development. By assessing student learning history and abilities, AI gives teachers a clear picture of the lessons and subjects requiring reevaluation. Teachers alter their courses by evaluating every student’s specific needs to address common knowledge gaps. This enables teachers to develop the best learning programs for all students.

2. Offering personalized learning

Personalization is a significant trend in education. AI gives students a customized learning approach depending on their unique preferences and experiences. AI can adjust to every student’s knowledge level, desired goals, and learning speed to help get the most out of their learning. Additionally, AI-powered solutions can assess a student’s learning history, pinpoint weaknesses, and provide courses suitable for improvement, offering many opportunities for personalized learning experiences.

3. Enabling universal access

AI breaks down the silos between schools and traditional grade levels. Through AI tools, classrooms are now globally available to students, including those with visual or hearing impairment or who use different languages. Using a PowerPoint plugin like Presentation Translator, learners get real-time subtitles for all the teacher says, opening up new possibilities for the learners who have to learn at varying levels, want to learn subjects that aren’t in their school, or are absent from school.

4.  Pinpointing where courses should be improved

Teachers may not always know the gaps in their educational materials and lectures, which can confuse learners regarding particular concepts. AI provides a way to solve this issue. For instance, Coursera is already applying this. When many students give the wrong answers to their homework assignments, the system alerts the professor and offers future students customized messages that provide hints to the correct answer.

This kind of system fills the gaps in explanation in courses and ensures every student is building a similar conceptual foundation. Instead of waiting to hear from the teacher, students receive immediate feedback to help them understand concepts better.

5. Automation tasks

Teachers usually have a lot to manage, including classes and other administrative and organizational tasks. They grade tests, evaluate homework, fill the needed paperwork, make progress reports, organize lecture resources and materials, manage teaching materials, and more. This means they might spend too much time on non-teaching activities, leaving them overwhelmed. With the help of automation tools and solutions, educators can automate manual processes giving them more time to concentrate on teaching key competencies.

6. Providing tutoring support

Intelligent tutoring systems, including AI chatbots and tutors, and tutoring programs are designed to handle customized feedback and guidelines for one-on-one teaching. Nonetheless, they can’t replace teachers because they aren’t advanced enough to teach the way humans can. They help in cases where teachers aren’t available for subjects that can be taught and assessed online.

AI is an effective tool that e-learning platforms can use to teach geography, languages, circuits, computer programming, medical diagnosis, physics, mathematics, chemistry, genetics, and more. They’re equipped to consider engagement, grading metrics, and comprehension. AI tools help students sharpen their skills while improving weak areas outside the classroom.

7. Promoting virtual learning

A virtual learning environment can provide group educational experiences, offer counseling services to students, and facilitate immersive learning experiences. With VR technologies, learners can directly connect their laptops or mobile devices to access the content. Using VR headsets, students with ADHD/ ADD can block distractions and increase concentration spans. In addition, students can help others in soft skill coaching, self-development, and life skills with interactive simulations.

8. Creating smart content

Smart content may include digital guides, textbooks, videos, instructional snippets, and AI, which develop customized environments for learning organizations depending on goals and strategies. Personalization in the education sector is a future world trend that can be achieved by pinpointing the areas where AI solutions can play a role. For instance, an educational institution can establish an AR/VR-based learning environment and web-based lessons to go with it.

Artificial Intelligence: Underlining The 7 Most Common Ethical Issues

Ever since the world has stepped forward towards the age of digitalization, things have never been the same. From the introduction of the internet to the expansion of the mobile-first concept and innovations like artificial intelligence and machine learning, people have experienced the highest exposure to technology ever.

Amidst all this development and expansion, one thing that has scaled dynamically is Artificial Intelligence. From the expansion of neural networks to energy use, data sets, and the prevalence of society, the growth of AI has made way for significant ethical concerns.

Before we jump on unraveling the most common ethical issues surrounding artificial intelligence, let us begin with developing an understanding of what ethical AI is.

What is ethical AI?

When it comes to “Ethics in AI”, the term means to investigate and constantly question the technologies that can hamper human life. Be it replacing humans with smart machines or concerns related to sharing personal information on AI-powered systems, the concept of ethical AI has gained all the pace due to the rapid scaling of AI technologies.

From computing power to data fed, AI systems have grown tremendously big in the past few years. Moreover, the rapid growth of AI has even dwarfed the potential of computing that was carried back from the era of the internet and PCs.

The extensive scale of deployment and responsibilities given to AI has now even involved other aspects of technology in the picture. Be it deep learning or scaling of any other advanced technologies that involve the use of AI, the situation has escaped the comprehension capabilities of even the most proficient practitioners.

And therefore, ethical AI brings some really interesting and important factors to the light that need immediate consideration in order to overcome ethical concerns surrounding AI technology:

1. Biases

From the training artificial intelligence algorithms to removing the bias involved, a huge amount of data is needed. Consider an example of any application made to allow editing of pictures. These applications are made to use AI to beautify the pictures and therefore contain a vast amount of data that has more white faces over non-white faces.

Therefore, it is necessary that AI algorithms must be trained to recognize and process non-white faces as efficiently as it does for white faces. The process requires feeding the right balance of faces to the database in order to ensure the algorithm works well to cut the built-in bias for beauty apps.

In other words, eliminating bias is extremely necessary when we need to create technology that can reflect our society with greater precision. Such actions thus require identifying all the potential areas of bias and fixing the AI solutions with the right approach.

2. Infusing Morality, Loss of Control

With more and more use of artificial intelligence, machines are capable of making important decisions. Be it the use of drones for delivery by carrier services or building autonomous rockets/missiles that can potentially kill a banned object. However, there is still a need for human involvement in such decision-making that can work on any rules and regulations that can impact humanity in any form.

The concern here is actually allowing AI to work on quick decisions. However, in operations like financial trading where it is essential to make split-second decisions, giving control to humans leaves no chance to make the right move at the right time.

Another example of the same is autonomous cars as they are made to make immediate reactions to take control of situations. The problem with all these scenarios is the ethical challenge of establishing a balance between control and AI.

3. Privacy

One of the most significant ethical concerns that have been long associated with AI is Privacy. There are many ethical concerns, from training AIs to the use of data and its source. Oftentimes, it is assumed that the data is coming from adults with high mental capabilities making the data used for creating AI that can work on choices. However, the situation is not always the same.

A quick example of the same can be the use of AI-powered toys that are designed to converse with children. Here the ethical concerns are about the algorithms collecting data from those conversations and making way for queries like where and how this data is being used.

The ethical concerns with such conversations grow even bigger when it comes to companies collecting that data and selling it to other companies. There is a need for rules that can justify data collection.

Moreover, there must be strict legislation made to protect the user’s privacy as an object that can collect data from conversations with children could potentially be used for taping the conversations of adults within the same environment.

4. Power Balance

The next significant ethical issue that comes with AI is giants like Amazon and Google using technology to dominate their competitors. More importantly, there are countries like China and Russia competing in the AI landscape, and here the question arises of the power balance.

From equal wealth generation to balancing monopolies, it is very likely that countries that are ahead of AI development and implementation are likely to race ahead of others.

For instance, countries with better access to resources that can develop and implement AI could utilize its power to innovate their war strategies, finance building, and more. Thus, AI creates some serious gaps around the subject of power balance.

5. Ownership

At number five, we have another big ethical challenge that needs to identify people or organizations that can be held accountable for things that AI creates. As Artificial Intelligence has all the potential to develop texts, bots, and video content, it is likely to create things that are misleading. Such material could trigger any violent circumstances for a particular community, ethnicity, or belief and therefore it becomes necessary to understand who could take ownership of the content.

Another example of the same could be AIs that are used to create music pieces or art. Thus, it is necessary that any new piece of content developed with AI that reaches some audience must have some ownership or could have intellectual property rights.

6. Environmental Concerns

Most of the time, the companies working on AI are not so concerned about how AI could impact the environment. Developers working on AI assume that they are using data on the cloud to work on their algorithm and then the data works on, say creating suggestions, recommendations, or making automated decisions. Though the systems are running efficiently and effectively, the computers that are keeping up the AI and cloud infrastructure require immense power.

A quick example of the impact that AI could create on the environment is the fact that training in AI could create 17 times more carbon emissions than an average American does in a year. Therefore, it is important that developers find ways to use this energy for other productive purposes and get over one of the most pressing problems of declining energy resources.

7. Humanity

Last but not least, it is the challenge of how humans feel in the presence of AI. Especially, when AI has been developed to be so powerful and efficient, it triggers the challenge for humans missing the feeling of how it actually feels to be human. As AI is designed and created to work on precision, it diminishes the human morale built through making errors and learning from it.

Especially, when AI has automated jobs for so long, it often leads to the question that what contributions human beings could make to the technology landscape. Though it is not possible for AI to replace humans for all jobs, only the idea of augmenting AI possesses some serious challenges.

To conclude

Humans need to get better when working along with smart machines in order to align with the tech transition. Besides, it is extremely necessary for the people to sustain their dignity and have respect for technology. Therefore, it is necessary that all the ethical challenges surrounding AI must be understood.

Especially, when AI is seen as a technology that has all the capability to create user-oriented and sustainable IT solutions, creating ethical AI could help empower digitalization. Be it advancing the process through AI improved Quality Assurance and software testing or using AI itself to create unbiased technology for users across the world.

More importantly, it is crucial that engineers working on AI technology should always have consideration for the human aspect of using AI. Be it the use of AI machines or software, it is vital that transparency should be maintained with respect to user data consumption and human involvement in decision making, the privacy of data, no biases, and the power balance.

Even if the thought of AI systems surpassing human intelligence may appear scary, the key is to have an early vision of all the ethical issues surrounding AI adoption. It not only needs humans to keep on learning but stay informed of the impact that any potential implementations related to AI could have on society.

The future of AI in Test Prep.

Since Socrates taught Plato and Plato taught Aristotle, humanity has known that the best education is delivered one-to-one by an experienced educator. But that is expensive, labor-intensive and difficult to scale. The result is the imperfect classroom-based instruction that we live with today: large class sizes, overworked and overloaded teachers, a deficiency of resources. Educators focus what little time they have for personal attention either on the best and the brightest or on the bottom of the class. The broad middle is often left to fend for itself.

Educators may have a new tool, A.I., to address those issues. Innovative forms of the technology, based on computer code that mimics the networks of neurons in the human brain, can uncover patterns in how students perform and can help teachers adjust their strategies accordingly. “A.I. tutors” — software systems that students interact with online — promise to give every student individualized attention, potentially remaking education as we know it.

Among the handful of companies leading that transformation is Riiid (pronounced “rid”), a start-up founded in Korea by YJ Jang, a graduate of Haas School of Business at the University of California, Berkeley. Riiid already has a strong presence in the Asian test-prep app market for the Test of English for International Communication, or TOEIC, which measures English-language proficiency for business. Now, Riiid is about to enter the SAT and ACT prep market in the United States.

Artificial Intelligence is Reshaping Life On Earth: 101 Examples

I recently read this article on gettingsmart.com and thought it would be great to share the list.

This month I’ve been tracking news headlines to get a sense for how widespread AI applications have become. With a couple news alerts and searches I spotted 101 current applications–no SciFi here, these are tools people are using today. And these aren’t just clever algorithms–they are getting better and smarter the more data they interact with.

Life & Media

1. Mapping apps and satellite view (Google Earth)
2. Speech recognition (TechCrunch)
3. Dating apps (Vancouver Sun)
4. Language translation (Silicon Valley Business Journal)
5. Image recognition (Fei-Fei Li on TED)
6. AI composition and music recommendations (Newsweek)
7. Make reservations at a restaurant. (Techcrunch)
8. Filters content and make recommendations (Hubspot podcast)
9. Write articles (Recode)
10. Optimize website-building (TechCrunch)
11. Manages prayer requests (Deseret News)
12. Track and store the movements of automobiles, trains, planes and mobile phones (IBM)
13. Track real-time sentiments of billions of people through social media (IBM)
14. Beat the best humans in chess and Go (CCTV)
15. Coaching social emotional relationships (Phys)

Safety & security

1. Security-driven AI systems can easily detect and identify bad behaviors from good behaviors. (Economic Times)
2. Quickly find security vulnerabilities (Defense One)
3. Criminal justice system is increasingly using algorithms to predict a defendant’s future criminality (Propublica)
4. Anomaly detection using machine vision (IBM)
5. Predictive models for crime (IBM)
6. Autonomous aerial and undersea warfighting (Nextgov)
7. Guide cruise missiles (Express)

Industry & Agriculture

1. Optimize crop yield (Amr Awadallah in Forbes)
2. LettuceBot reduces chemical use by 90% (Wired)
3. Driverless tractors (Business Wire)
4. Manufacturers predict which machines will breakdown (Amr Awadallah in Forbes)
5. Smart robots for repetitive jobs from apple picking and  sneaker maker, (Techcrunch)
6. Robots develop and share new skills (MIT)

Transportation

1. Driverless cars (Nature) coming to Pittsburgh this fall (Wired)
2. Driverless trucks (TechCrunch)
3. Managing drone traffic (Yahoo)
4. Making bus routes smarter (Shanghai Daily)
5. Improve the efficiency of public transportation systems (IBM)
6. Oil exploration efficiency (Oil Price)

Environment

1. Prediction and management of pollutants and carbon footprints. (IBM)
2. Make data centers, power plants, and energy grids more efficient (Money)

Medicine & Health

1. Digitized health records and all medical knowledge to improve diagnosis (IBM)
2. Performing cohort analysis, identifying micro-segments of similar patients, evaluating standard-of-care practices and available treatment options, ranking by relevance, risk and preference, and ultimately recommending the most effective treatments for their patients (IBM)
3. Power precision medicine (NIH)
4. Reduction of medical errors (H&HN)
5. Study the genetics of Autism (SAC)
6. Analyze genomic sequences to develop therapies (Amr Awadallah in Forbes)
7. Read x-rays better than a radiologist (nanalyze)
8. Genomic editing–which may be the most important (and scariest) item on the list (Time)
9. Use social media to diagnosis depression and mental illness (Tech Times)

Organizational Management

1. Speeding and improving Identity verification and background checking (PE Hub)
2. Monitor employee satisfaction and predict staff turnover (HuffPost)
3. Sorting through stacks of résumés from job seekers. (Propublica)
4. Replace handcrafted rule-based systems (TechRepublic)
5. Smart virtual assistants (Slate)
6. Enterprise tech companies provide deep learning as a service–AI on demand (TweakTown)
7. Humans and robots will increasingly collaborate on problem solving (Quartz)
8. Automated floor cleaning (Slate)

Art & Architecture

54. Organic algorithms in architecture (Greg Lynn on TED)

1. Virtual reality art (Wired)
2. Synthesized music (Newsweek)

Social services & Infrastructure

1. Timely and relevant answers to citizens (IBM)
2. Predict the needs of individuals and population groups, and develop plans for efficient deployment of resources. (IBM)
3. Prediction of demand, supply, and use of infrastructure (IBM)
4. Mobile phone network services (RCRwireless)
5. Analysis of lead contamination in Flint water (Talking Machines)
6. improve building and city design (Property Report)
7. Poverty map of Africa to improve services delivery (Yahoo)

Finance and Banking

1. Fraud detection (Business Insider)
2. Scan news, spot trends and adjust portfolios (Hubspot podcast)
3. Determine credit scores and qualify applicants (Propublica)
4. Find the best insurance coverage at the right cost (IBM)
5. Deliver personalized service with reduce error rates (Finextra)
6. Handle 30k banking customer services transaction/month (American Banker)
7. Answer 100M financial questions involving complex data (Fast Company)
8. Auto-adjudication of insurance claims (Fast Company)
9. Tax preparation (CFO)

Marketing & Customer Service

1. Power chatbot customer service (CB Insights)
2. Product recommendations (Digital Marketing Blog)
3. Manage White House comments (Techcrunch)
4. Chatbot lawyer contests parking tickets (Guardian)
5. Robot inventory checker (NY Times)
6. Recognise customer behavior and provide predictive customer service (Brand Equity)
7. Predict eBay sales (HeatST)
8. Improve sales funnel conversion (Digital Marketing Blog)

Entertainment

1. Fantasy football picks (Fake Teams)
2. Writing screenplays (Entertainment)

Education

1. Recommend next best learning experience (Forbes)
2. Personalized learning programs (IBM)
3. Intelligent tutoring (Forbes)
4. Provides six trait writing feedback (Hubspot podcast)
5. Digitized the world’s literature enabling search/analysis (IBM)
6. Embedded adaptive assessments promotes competency-based learning (Google’s Jonathan Rochelle in Business Insider)
7. Improve career education (Google’s Jonathan Rochelle in Business Insider)
8. AI is boosting HigherEd persistence with text nudges (Rose Luckin in Times HigherEd)
9. Process intelligence tools identify and visualize opportunities (MIT)
10. Matching teachers and schools (Getting Smart)
11. Bus scheduling (Getting Smart)

Smart Home

1. Smart home control systems (Fortune)

Check out these smart home startups. A lot of these use AI behind the scene to get smarter over time.

The Changing Employment Landscape

This has been a weird recovery–sluggish and slow to produce jobs and higher wages. In addition to a bunch of unusual international circumstances, the global economy has been incorporating exponential technology, particularly all the artificial intelligence applications above. While the bots are eating away at some predictable job categories, all this technology has yielded frustratingly slow productivity growth.

The economy remains a head scratcher, but web research yields pretty consistent advice for young people and job seekers in this new age of automation:

1. Do what AI doesn’t do well: give a hug, solve a mystery, tell a story. (NPR)
2. Analytical and interpersonal skills will likely become more important. (Brookings)
3. Focus on adding value in novel situations. (Anthony Goldbloom on TED)
4. Focus on computational thinking not just coding. (Edsurge)

99. Critical thinking, systems analysis, and inductive reasoning are increasingly important in “mixes and depths” that are currently rare. (Quartz)

100. Social interaction and co-working increasingly important (Business Insider)

1. Equip graduates to work effectively alongside AI (Rose Luckin in Times HigherEd)

What’s the new hot field? When you pull junior aside at the pool party (like The Graduate), instead of “plastics” you should be recommending “machine learning.”

The first half of the information age was programming computers to do what we want. In the second half of the information age computers are programming themselves. Machine learning is the practical subset of artificial intelligence–the new hub of efforts to add intelligence to every facet of life. And it’s hot.

“It’s huge. It’s really one of the biggest shifts that we have seen in the last few decades. And it’s creating demand, off the charts demand, and this is across all industries,” said Amr Awadallah of Cloudera.

Amr added, “The future need for people with high technical skills when it comes to data analytics, artificial intelligence and machine learning is going to be off the charts.”

More broadly Google’s Jonathan Rochelle knows it takes a smart application of cutting-edge products to help kids learn. He encourages teachers to help kids use the latest products effectively. Those are the skills that will give them the greatest leg up as citizens, Rochelle says. “Imagine if we could teach kids all the tools that are at their disposal,” he says, “and let them take the next step to stand on the shoulders of giants.”