AI AGENTS IN HIGHER EDUCATION: WHAT THEY ARE AND HOW CAMPUSES ARE DEPLOYING THEM

AI Agents · Higher Education · Student Success · Advising · Technology

There was a time, not long ago, when “AI in higher education” meant a chatbot on a university homepage that could answer three questions before redirecting everything else to a phone number. Those tools had their place, but anyone who worked with them regularly knew the frustration — they were narrow, rigid, and quick to reach the edge of their usefulness. What’s happening on campuses today looks fundamentally different, and the shift is significant enough that it deserves a clear-eyed look.

What Makes an AI Agent Different from a Chatbot

A traditional chatbot operates on a script. It matches what you type against a list of expected inputs and returns a predetermined response. AI agents, by contrast, can reason, plan, and act autonomously across multiple steps. They don’t just answer a question — they can look something up, compare options, execute a task, and report back. The distinction isn’t purely technical. It changes what’s actually possible.

In practice, an AI agent in a financial aid office might handle a student inquiry not just by explaining policy but by pulling up the student’s account, identifying a missing document, flagging a deadline, and sending a follow-up reminder — all without a staff member manually initiating each step. That kind of autonomous, multi-step action is what separates agents from earlier AI tools. Researchers studying AI in higher education have consistently found that the most meaningful implementations are those that move beyond information retrieval toward genuine task execution (Zawacki-Richter et al., 2019).

Where Campuses Are Putting Agents to Work

Student advising has been the earliest and most visible area. Georgia State University became something of a national case study when it introduced Pounce, an AI-powered advising assistant, well before “AI agent” entered the mainstream vocabulary. The institution documented meaningful reductions in summer melt — the phenomenon where admitted students fail to complete enrollment — in part because Pounce could reach thousands of students simultaneously, respond to questions at two in the morning, and nudge students toward completing specific enrollment tasks (Page et al., 2017). The model has since been replicated at institutions across the country.

Financial aid offices have been quick to follow. The sheer volume of repetitive questions — verification requirements, disbursement timelines, satisfactory academic progress policies — creates a strong case for automation. An agent that handles first-line inquiries and escalates only what requires human judgment can meaningfully reduce the burden on financial aid counselors, freeing them for the conversations that actually need a person. IT helpdesks have seen similar results: when a mid-size university is fielding hundreds of password reset and account access requests per week, an agent handling those autonomously gives technical staff back time for more complex problems.

Library and research support services represent a growing frontier. Reference librarians have long guided students through the research process, but staffing constraints mean coverage is uneven. AI agents can now handle initial research guidance — identifying relevant databases, suggesting search strategies, flagging source credibility concerns — while surfacing the more nuanced questions for professional librarians. The EDUCAUSE 2024 Horizon Report identified this kind of agentic support as one of the defining near-term shifts in how institutions deliver academic services (EDUCAUSE, 2024).

The Platforms Behind the Shift

Several vendors have moved quickly to embed agent capabilities into tools that higher education institutions already use. Ellucian, which serves more than 2,700 institutions through its Colleague and Banner platforms, has been building AI and intelligent automation features into its product suite under the Ellucian Intelligent Experiences initiative. Microsoft’s Copilot has entered campus environments through existing Microsoft 365 and Azure agreements, allowing institutions to extend agent capabilities into email, document workflows, and student-facing portals. Salesforce’s Education Cloud incorporates Einstein AI for enrollment management, advising, and alumni engagement use cases.

What’s notable about this wave is that many institutions aren’t building custom AI systems from scratch. They’re activating agent capabilities inside platforms they already own — which lowers the barrier significantly. That said, it introduces its own complexity around data governance, integration architecture, and training agents on institution-specific context and policy.

The Honest Challenges

Data quality is the challenge that comes up most consistently in implementation conversations. An AI agent is only as useful as the data it can access and trust, and higher education institutions notoriously have data distributed across disconnected systems — student information systems, learning management platforms, CRM tools, financial aid processors, and ERP platforms — that don’t always communicate with each other reliably. Before an agent can do meaningful advising or financial aid work, someone has to solve the integration problem underneath it. This is not a small lift.

There are equity questions worth taking seriously as well. Students who are most at risk of not persisting — first-generation students, students managing financial pressures, students balancing work and family alongside coursework — are often the same students most likely to receive AI-mediated support rather than human attention. That’s not inherently a problem, but it requires institutions to be deliberate about where the human relationship still matters most. Faculty governance concerns about academic integrity, student privacy, and the broader ethics of automated decision-making in education are also real and ongoing. Institutions that have navigated this successfully have typically involved faculty and student voices early, rather than deploying first and explaining later.

What to Watch For

The question for most institutions heading into 2026 and beyond is no longer whether to deploy AI agents, but how to do it in a way that actually serves their specific student population. The campuses figuring this out fastest won’t necessarily be the largest or most well-resourced. They’ll be the ones with clarity about the problem they’re solving, the organizational will to build the data infrastructure that makes agents genuinely useful, and a governance structure that keeps the right stakeholders at the table.

What’s becoming clear is that AI agents aren’t a replacement for the people who do the work of higher education. At their best, they extend what those people can do — reaching more students, responding faster, and surfacing the information that makes human judgment more effective when it counts.

Beidat LLC partners with higher education institutions on technology strategy, ERP implementation, and digital transformation — including the growing complexity of AI readiness and system integration. If your institution is working through questions about where AI agents fit in your operational model, the team at Beidat would be glad to talk. Reach out at info@beidat.com or call 888.384.1992.

References

EDUCAUSE. (2024). 2024 EDUCAUSE Horizon Report: Teaching and Learning Edition. EDUCAUSE.

Page, L. C., Castleman, B. L., & Meyer, K. (2017). Customized nudging to improve FAFSA completion and college enrollment. American Behavioral Scientist, 64(9), 1182–1200. https://doi.org/10.1177/0002764217730066

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0

Last updated on June 5, 2026