Students, faculty, and staff at colleges and universities are eager to incorporate artificial intelligence (AI) into their work and studies. AI use cases are poised to streamline admissions processes, maximize efficiency for researchers, and enhance learning in and out of the classroom, but some universities’ underlying networks may not have the capacity to support data-rich AI applications. Our colleagues at Government Technology Insider recently published an article on college and university AI readiness and how to establish an agile, scalable AI foundation.
Colleges and universities are under a tremendous amount of pressure to demonstrate their value. They’re tasked with delivering education to undergraduate students and supporting the complex research agendas of top scholars, all while enrollment is dropping and budgets are shrinking. At the heart of managing these pressures is implementing artificial intelligence (AI)—from generative AI (GenAI) that eases the burdens of the application and admissions process to predictive AI that speeds research and superintelligent AIs that we have yet to imagine. While researchers and administrators alike are eager to harness the power of AI, four elements of readiness will determine whether college and university networks and technology infrastructure have the capacity to deliver AI applications at speed and scale.
AI Use Cases for Higher Education
Students, researchers, and staff at various universities are already exploring AI use cases to facilitate collaborative research, streamline admissions and enrollment, and make study sessions more impactful:
- The time management and productivity use cases that have become synonymous with GenAI can help faculty and staff streamline routine tasks and dedicate more time to lesson planning, making admission decisions, and building relationships with students.
- Custom GenAI models can help students review class materials, close skill and knowledge gaps, and answer questions for students at any time.
- Students and researchers can conduct preliminary searches for sources and employ AI for data analysis, allowing them to spend more time verifying information and interpreting results.
- Unified communications and collaboration solutions with AI-powered transcription, meeting summaries, and action items enable users to focus on conversations rather than notetaking.
While students, staff, and researchers are eager to explore AI, supporting thousands of AI and GenAI applications requires a robust network foundation. AI readiness comprises both the network to facilitate AI and an organization’s strategy for implementation and use.
Foundations of AI Readiness
“A university’s AI strategy needs to come from leadership, but the process of planning and defining an approach to AI also needs to be collaborative,” said Matt Kenslea, Senior Account Director, Education at Lumen. “Having a centrally defined set of goals, rules, and procedures that unites all of the various user populations is essential to finding an efficient path forward.” This includes alignment with the IT and administrative teams that will be leading implementation to ensure that the infrastructure, network architecture, and policies they design all support that strategy.
Infrastructure and Performance
“Once the ethical strategy and governance are in place, the focus turns to data,” said Craig Cupach, Senior Business Development Director, Higher Education at Lumen. “AI tools are creating and consuming an unprecedented amount of data, and current infrastructure may not be equipped to manage the volume.” AI applications demand high bandwidth, low latency networks and powerful compute infrastructure to manage data efficiently, avoid performance bottlenecks, and ensure seamless real-time experiences across campus environments.
Network Architecture
A modern network infrastructure is necessary for colleges and universities to increase capacity for AI, but to maximize efficiency that network needs to be architected appropriately. According to Cupach, an agile, software-defined network prioritizes AI traffic and integrates across a hybrid cloud and multi-cloud environment. With the right network architecture, users can seamlessly navigate workflows across disparate networks and achieve results regardless of where the data is stored.
Security
Colleges and universities are already attractive targets for data theft because of the number of users and endpoint devices, the amount of personal, financial, and medical information they hold, and the scale of their networks. But as AI use expands, educational institutions become susceptible to data poisoning, in which threat actors manipulate or corrupt AI training data, leading to data misclassification, altered model behavior, and new system vulnerabilities. IT teams have various mitigation strategies at their disposal, including data validation, adversarial training, access controls, and continuous monitoring and anomaly detection.
Ongoing Maintenance
With network infrastructure, architecture, and security established, IT teams need operations and monitoring protocols in place to maintain performance and proactively identify problems. AI-native management tools can enable colleges and universities to monitor their entire IT environment with reduced human intervention, freeing up IT staff to address complex or urgent issues.
Bonus: The Human Component
While some students and staff may already use AI tools, encouraging responsible AI use at the organizational level requires coordinated effort from colleges and universities. AI literacy programs can teach users to engage with AI and GenAI as tools rather than an opportunity to offload critical analysis. Frequent, consistent communication of organizational policies and best practices builds on these programs to support school populations in benefiting from AI use cases.
Conclusion
Investing in agile, scalable technology is a key part of AI readiness, ensuring the network can continue to meet demand even as AI use cases grow more sophisticated. Establishing clear, consistent policies and investing in a robust and secure infrastructure foundation positions colleges and universities for successful AI deployments today and in the future.