Edtech initiatives enable students to learn in entirely new ways, helping to shape the skilled workforce of the future. From virtual reality to drones to robotics, K-12 districts are investing in new, cutting-edge technology for education environments. Other than providing students with creative learning opportunities, what do all these technologies have in common? They rely on the network.

Often overshadowed by the latest and greatest gadget or device, the network is the singular engine responsible for powering and driving innovation in education. However, not all networks are created equal — many are not designed to support the new technologies found in the modern-day classroom, nor are they sufficiently automated to make life easier for a lean IT staff.

To keep pace with rising demands in education and to make infrastructure work smarter, IT leaders must optimize Wi-Fi with artificial intelligence and machine learning technology to support dynamic school settings, ensure IoT infrastructure is both secure and flexible and prepare their network for the tech of tomorrow.

ABCs of AI and Machine Learning

Across industries, any conversation about innovative technology is bound to include artificial intelligence and machine learning. Leading organizations are turning to AI and machine learning to optimize resources and employee talent, reinforce company security, expedite innovation and improve their bottom line.

Education environments are no exception. From automating tasks like grading to creating personalized lesson plans, one cannot overstate the potential impact of AI and machine learning in schools. In fact, Technavio Research estimates AI use in the U.S. education sector will increase by 48% from 2018-2022. What does this look like in a real-world application? And what does it have to do with the network?

A typical campus has a gym, an auditorium, and a cafeteria. In these settings, occupancy can surge from relatively empty to hundreds or even thousands of people during games, meals and other schoolwide activities. Having an AI-powered network that can automatically improve radio frequency efficiency and add capacity to meet bandwidth demand in precise locations will ensure that this connectivity remains consistent. Put simply, an AI-powered network allows school networks to scale up and down as needed, whether an event is happening or it’s a typical school day.

AI and machine learning will also play a critical role in network diagnostics as more schools complete the shift to digital. For example, many institutions are moving from printed textbooks to online curriculum to save money. If the network goes down, the school day is less productive, setting students back in their studies. Network connectivity is also paramount during more time-sensitive activities, such as state testing. If the network were to crash, student answers could be lost, they’d be unable to complete their exams and turmoil would ensue. In both instances, AI and machine learning technology can empower IT teams with greater network visibility through real-time analytics and automated diagnostics. In turn, they could quickly identify, troubleshoot and address the network deviation.

Reinforce IoT Security without Compromising Flexibility

Gartner forecasted that 14.2 billion connected devices will be in use in 2019 and that the total will reach 25 billion by 2021. Yet, Gartner also predicted IoT security spend will top $3.1 billion by 2021. Why? With IoT growth come security challenges and vulnerabilities.

Most IoT devices are not manufactured with enterprise-grade security in mind and lack embedded security features such as antivirus and firewall capabilities. Additionally, if IoT technology isn’t segmented properly within a school’s network, devices can be breached as a gateway to more sensitive areas of the network — similar to the casino-fish-tank hack last year.

There are also compliance considerations. In education, schools must adhere to numerous standards, such as FERPA and CIPA. The fallout from a cyberattack could not only cripple a school’s functionality, but it could also jeopardize the private data stored on and off campus, and potentially expose the school to legal and financial repercussions.

In short, not only does a school network need to be able to support BYOD initiatives, in-classroom technology, and the school’s administrative infrastructure, but it must also be highly secure. Unfortunately, many IT leaders lack the transparency and control required to protect their networks effectively. IBM reported it takes organizations an average of 191 days to even identify a data breach, let alone mitigate it. Now, more than ever, it’s important for IT leaders to have a 360-degree view of the network, including uses, devices, and applications.

When incorporating new IoT technology, IT leaders should closely evaluate how the technology factors into the broader organization’s security strategy. Centralized, end-to-end network monitoring is critical for timely issue containment and remediation. Establishing network segmentation, applying individualized security profiles for each IoT device and aligning on a real-time analytics and security response protocol are just a few ways institutions can safeguard their technology and mitigate risk to the students and staff.

A Network for the Future

As technology continues to infiltrate the classroom, schools must also look at the impact and benefits for students and adjust their educational strategies accordingly. Laptops, tablets, drones, and other IoT-based technologies can lead to a robust, engaging curriculum for students — as long the network hosting these devices is up for the challenge.

K-12 school districts must begin future-proofing both their infrastructure and curricula by investing in smart networking technologies. Networks that are built with automation, real-time visibility and a standards-based architecture in mind will significantly reduce the strain on IT staff, and thereby reduce overhead. These resources can then be reallocated where they belong: to improve students’ futures.

*This post was originally published to IoT Agenda on February 12, 2019. 

This blog was originally authored by Mike Leibovitz, Senior Director, Product Management.