The textbook reading assignment, teacher-student lectures, and hand-written notes are quickly becoming a thing of the past in modern classrooms. These traditional teaching and learning methods are not working for a majority of students in the twenty-first century. What’s changed?

Rapid technological changes over generations have presented new challenges and demands in our society, workforce, and, consequently, our education system. Analytical thinking, innovation, creative problem-solving, and technological proficiency are now skills that are needed in our future workforce. Likewise, students who have grown up in a technological landscape now crave those visual, auditory, and sensory experiences in their learning styles.

It’s becoming increasingly apparent to many that our current “one shoe fits all” teaching styles are outdated. Teachers need to be able to adapt and accommodate diverse learning styles, and students too will need to have more control over their personal learning journey.

Are we setting our future workforce up for failure if teaching styles don’t change? What are skill sets our students need to learn in the classroom now to prepare to be productive members of society later?

One of the most difficult problems faced in today’s education system is teaching effectiveness. Students are absorbing large amounts of surface-level information—that is often quickly disposed of— without interacting with it in a useful way. Differentiated teaching styles as well as the flexibility to adapt are both needed in modern educators.

VARK is an acronym that stands for Visual, Aural, Read/Write, and Kinesthetic sensory modes of learning. The VARK model suggests these are the four main types of learners. Students may feel drawn to one particular learning style or enjoy a mix. Every student has a unique, personally-preferred learning style.

Individualizing learning plans for students allows each student to learn and engage with the content in a way that is most beneficial to them. Personalized learning in the classroom tailors itself to each student’s individual needs, strengths, and interests in order for that student to learn at a deeper level.

How can teachers adapt to these different learning styles in the classroom? How can VARK learning styles be incorporated to change different teaching styles? What challenges or obstacles do you foresee?

The goal of transmitting knowledge without rote memorization being the sole focus has started to become more accepted and common. In addition, experts in the education field are asking, “What are we testing for?” Students at all levels are still heavily focused on end results—their score on the test, their rank in the class, or their final grade in the class. As we have seen with many entrepreneurs and innovators, test results do not define you.

Are exams really doing anything beneficial in preparing future members of society? What are your thoughts on the future of student testing? Will it become obsolete or remain in curriculum?

Learning environments focused on experiential learning rather than final test scores encourage students to take that knowledge, interact with it, and think critically about it. Students are becoming partners or “co-creators” of their learning journey.

They are able to partake in experiences that allow collaboration, communication, and teamwork on projects that allow the application of knowledge to real-life scenarios. These learning environments encourage students to be innovative, take risks, and simulate real-life problem-solving. Those in the education field hope this type of learning will help students find their passions, find potential career paths, and learn the ability to persevere and find solutions.

Many experts are pushing for learning to be more focused on experience and collaboration.

What do you think of this new learning environment? What obstacles do you think need to be overcome in order to transition to this type of education system?

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What is AI?

Artificial Intelligence, or AI, is a broad term used to describe a machine’s ability to replicate human intelligence. While human and animal intelligence is considered “natural,” computer intelligence is “artificially” created through a series of specially designed algorithms. Actions like learning, reasoning, logic, perception, or creativity—once thought to be exclusive to humans— are now being simulated by technology and used in nearly every industry.

The goal of AI is to mimic human thinking in order to complete both simple and complex tasks. Our current AI is “narrow” and “noncognizant,” meaning that the machines perform objective tasks using data. There is no conscious thinking on the machines’ parts, and they are not driven by emotion like humans. Current narrow AI operates within its pre-determined range of functions.

Where is AI used today?

The future is now—there’s virtually no industry AI hasn’t already affected and changed. Whether we realize it or not, AI is all around us, and it is increasingly playing a more active role in our lives.

When people hear Artificial Intelligence, they almost always think of robots. That’s because mainstream media and big-budget movies tell us thrilling tales of human-like machines slowly becoming self-aware and wrecking havoc. Though this narrative of murderous machines is certainly an entertaining plot line in movies and books, experts don’t expect robotic domination to be a reality.

We use voice-powered assistants like Apple’s Siri, Amazon’s Alexa, or Microsoft’s Cortana without blinking an eye. Asking Siri a question, telling Alexa to add milk to your grocery list, or using a customer service live chat bot is not otherworldly technology to us, but rather commonplace in our everyday lives. This type of narrow, data-based AI has seamlessly integrated itself into the human experience.

AI takes the burden of repetitive action and instant response away from humans, increasing efficiency, performance, and safety. In manufacturing facilities, machines are replacing the tedious tasks on assembly lines. As a result, it is increasingly normal to see humans working alongside machines in manufacturing facilities. The machine’s sensors keep equipment running smoothly, increasing efficiency and productivity while ensuring safety standards are still met.

In the education field, AI has saved teachers tons of time through automated document reading, automated grading, and automated plagiarism checking. Freed from these time-consuming and repetitive tasks, educators now have more time to focus on lesson planning or educational projects that would benefit students.

AI also creates more precise analytics and data, creating the potential to become powerful diagnostic tools. For example, the AI technology in today’s smart watches and fitness trackers gather important data points about the user’s health. These data points can then be analyzed at doctor appointments to gauge the individual’s health.

Perhaps one of the most exciting examples of AI technology in the works is the self-driving car. Equipped with myriads of sensors that capture thousands of data points every millisecond—such as speed, road conditions, and traffic—the autonomous car uses AI to interpret these data points and to act accordingly.

While people readily accept virtual assistants, live chat bots, and robotic manufacturing, many people shrink from the idea of self-driving cars and autonomous airplanes. The idea of glitches and problems in self-driving cars and planes is terrifying, even though humans are equally flawed and capable of creating disaster. There is an expectation ingrained into us that AI is flawless—however, it has flaws just like its creators. It simply must be improved and worked on over time.

Why do you think of the current AI in our society? Does this type of AI remove human control to the point of trepidation? What are your thoughts?

How will AI shape the future of industry?

Regardless of individual industries, AI is everywhere, and it will affect the way we do business across all sectors. It is already used in transportation, manufacturing, finance, healthcare, education, city planning, and much more.

Though media headlines often warn us that AI technology is stealing jobs, experts are optimistic and expect AI to be a vehicle for job creation. Once skilled laborers are freed from repetitive and routine tasks that machines can do—such as, separating things into bins, picking fruit, washing dishes, answering calls—they can then work in new areas and fields that AI technology creates. However, experts do warn that it is essential to retrain and educate displaced workers in these new fields.

Does AI really improve our human experience overall? Or does it complicate it somehow? Is there such a thing as too much AI?

Looking to the future: where is AI going?

Though our current AI is non-cognizant—meaning machines draw conclusions based on inputted data or algorithms—the future does hold the possibility of cognizant AI. Our current AI may not seem narrow or weak, but it lacks human self-awareness, consciousness, and the ability to think for itself.

Machines can certainly process data much faster than we can, but they can’t think abstractly or make decisions informed by memory or experience. There is the possibility that this type of AI could arrive in the future.

What are your thoughts on the future of AI? Are you hopeful, concerned, or worried?

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