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

Cost is also emerging as a decisive factor for many colleges and universities, Robert says.

“We know from our research that when generative AI first exploded into the consciousness of higher ed leaders, a lot of the pilots and the experimentation were coming out of flexible budget line items — things like carryover funds or innovation funds,” she says. “Those aren’t sustainable sources of funding for long-term investment. Now, we’re getting to a point where IT leaders need to make long-term decisions about investments, so there’s a lot of new and evolving concern about the cost of AI tools, which is increasing interest in conversations around ROI.”

Academic AI Productivity Tools: Writing, Research and Study Assistance

AI-driven academic productivity tools are designed to help students and researchers work more efficiently by automating routine tasks, synthesizing information and delivering actionable insights. These tools can support activities such as idea generation, literature review and time management while also enabling more personalized learning experiences. There are also AI-enabled tools that provide writing assistance, such as Grammarly.

The biggest productivity platforms in higher education — Google Workspace and Microsoft 365 — have embedded generative AI capabilities into their core applications. Google Gemini and Microsoft Copilot are embedded within familiar tools such as word processors, spreadsheets and presentation software.

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“We see students using our tools as ‘thought partners’ to deepen understanding rather than just generate answers,” says Steven Butschi, director of Google for Education.

For example, Butschi says students are using Gemini to “get unstuck” in their workflows, whether that’s by asking clarifying questions, brainstorming ideas or requesting feedback on drafts.

For more intensive projects, Butschi says, Gemini's Deep Research feature “is designed to be a heavy-lifting helper that synthesizes information to generate comprehensive reports, helping students find and process vast amounts of data — such as 1,500 pages of file uploads — so they can understand complex topics rather than having the AI simply do the work for them.”

Graduate finance students at the University of Maryland are using Google’s AI suite “to process large amounts of public data for credit risk analysis, effectively reimagining how they approach complex financial modeling,” Butschi says.

Built on Google Gemini, NotebookLM “is rapidly becoming a favorite for deep study and research because it grounds AI responses in the user's selected sources,” he adds.

This enterprise solution enables students to consolidate course materials and original content within secure, domain-restricted environments, addressing critical data privacy requirements while delivering sophisticated organizational capabilities. NotebookLM gives students a safer environment for supplementing study resources and practice assessments within the college or university domain.

“The tool allows students to create audio overviews, mind maps and study guides grounded specifically in their own course materials,” Butschi says. For example, students at the University of California, Riverside are using NotebookLM to upload reading interpretations or position papers and then “debate” with the AI to identify weaknesses in their arguments or test their comprehension, he says.

“At San Diego State University, students use NotebookLM for real-time study assistance and to streamline their research processes, while learners at Indiana University are creating study companions that quiz them and role-play scenarios to prepare for leadership conversations,” he adds.

Establishing Evaluation Criteria for College Student AI Tool Selection

Julie Schell, assistant vice provost and director of the Office of Academic Technology at the University of Texas at Austin and a 2024 EdTech IT influencer, says she and her colleagues evaluate potential AI tools and other types of learning technology using an adoption process that considers three pillars: pedagogical efficacy, user experience and campus sustainability. This multifaceted evaluation underscores not only the technical and educational benefits of AI tools but also prioritizes student experience and institutional values in every adoption decision.

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“By pedagogical efficacy, we mean, how will this tool breathe life into the subject matter in different and more helpful ways than if this technology was not used?” Schell says. Second, the school evaluates whether the tool is going to promote a student’s learning and digital experience, or whether it’s going to be frustrating to use.

“The third pillar looks at how well the vendor fits with our mission and is aligned with what we do,” Schell says. “We’re looking at how willing the vendor is going to be to work with us to promote academic excellence.”

Balancing Innovation With Academic Integrity and Responsible Use

As institutions expand access to AI-powered tools, IT leaders must balance the push for innovation with the responsibility to support ethical use, safeguard student data and establish clear, consistent guidelines for how AI can be used across campus.

EDUCAUSE’s Robert says the way colleges and universities communicate with students about the correct or incorrect ways to use AI can look very different depending on the institution. “Some institutions are all in — AI first, AI everything. How can you use AI to solve this problem? If you can, you should,” she says. “Other institutions, though not as many as a couple of years ago, are still trying to heavily restrict AI use.”

Regardless of where an institution stands on the issue, Robert says it’s important to “seek alignment from the highest level of institutional policy all the way down to classroom level policy. When there’s not alignment there, that's where things can get really challenging for students, and that's what we really want to try to avoid. So, you’ve got to clearly communicate that policy to students so they know what those expectations are and can follow those expectations.”

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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.

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“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.”