Best AI Tools for College Students: A Guide for Higher Ed IT

Schell agrees: “We’re in such a complex space right now that having guiding principles, even though they’re not perfect, is really helpful.”

In 2025, UT Austin released guidelines on the use of AI tools in teaching and learning, including the statement that AI should be used “in alignment with our honor code and fundamental scholarly values such honesty, respect and authenticity, taking ownership and claiming authorship of the output of tools when appropriate.”

Schells says addressing academic integrity means focusing on “what is learning help, what are the learning outcomes, what do we want students to know and be able to do, and how might AI advance that or hurt it — and articulating that clearly to students at a global as well as granular level.”

“It’s never OK to deceive, so making that clear is important,” Schell says, “but there’s a lot of gray area around whether it’s OK to use AI for something.” It’s no longer as simple as saying, its OK or it’s not OK to use AI in a class, she says. You have to be even more granular within an assignment.

WATCH: Higher ed institutions are embracing the future of AI.

“For example, I have an assignment where I have students write a statement on their teaching philosophy. It’s OK to ask AI what a teaching philosophy is, and it’s OK to enter in some text and say, ‘I need some help brainstorming what my teaching philosophy is,’ but it’s not OK to ask it to generate the teaching philosophy for you,” she says.

What’s also important, Schell says, is to “support the development of the ethical muscle, because I think we as humans are still figuring out what that muscle is, and we need to help our students develop that.”

Technical Integration Requirements for AI Tools and Campus Infrastructure Compatibility

Before AI tools can deliver meaningful value in higher education, they must integrate seamlessly into complex campus technology ecosystems.

For IT leaders, this means evaluating how new platforms align with identity and access management, security and compliance requirements, and enterprise systems such as learning management systems and data warehouses, while also confronting the infrastructure challenges of deploying AI at scale.

Robert says the rapid expansion of AI in higher education is amplifying long-standing challenges, including concerns about data governance. “One of the biggest issues that higher ed has been trying to figure out for a long time is what are the best data governance structures that work for us,” she says. “How are we protecting data privacy and data security and not limiting innovation?”

Integral to solving these issues is making sure that everyone has a seat at the table to weigh in on AI tool decisions, Robert says. “Every institution faces its own unique set of technical challenges, and so it’s a matter of figuring out who at the institution can uncover those challenges, and who can help tackle them.”