Artificial Intelligence is rapidly transforming every industry, including higher education. Institutions are adopting AI-driven platforms to enhance student engagement, streamline advising, and create more personalized learning pathways.
But while the benefits are undeniable, AI systems also introduce complex risks that can impact equity, trust, and institutional integrity. In higher education, it’s not enough for AI to be intelligent. It must also be ethical, transparent, and trustworthy.
Below are five risks institutions must manage as AI becomes more deeply integrated into student services ecosystems.
One of the most misunderstood ideas about AI is that it can replace human interaction. In reality, education is built on relationships: the mentors, guides, champions, and connectors who help students navigate complexity and feel seen along the way.
Artificial Intelligence is not sophisticated enough to understand social and emotional cues and respond appropriately. Therefore, a chatbot cannot help a student navigate a personal crisis, deal with anxiety about choosing a major, or understand the nuances of financial hardship. Relying on a chatbot for socio-emotional support only exacerbates students’ sense of loneliness and stress, which can further lead to stopping and dropping out. Human connection is the foundation of trust, and no algorithm can replace that.
The goal, therefore, isn’t to replace advisors or educators; it’s to amplify their capacity. AI should handle administrative or repetitive tasks so humans can focus on meaningful conversations, outreach, and support.
AI systems learn from data,but if that data reflects existing societal or institutional inequities, bias can be amplified.
Imagine an AI system recommending biology to a student because “students with your background” tend to do well in that field. If that recommendation is based on demographic patterns—not interests, skills, or aspirations—, it limits opportunity rather than expanding it.
Bias in AI isn’t just about what data we use; it’s also about what data we choose not to collect. Sensitive attributes like race, gender, income level, or geography can unintentionally shape predictions in ways that disadvantage certain groups.
To ensure your AI system isn't perpetuating bias, make sure to:
Transparency is the foundation of accountability. If an AI system recommends a course, a major, or a career path, both the student and the advisor should be able to understand why.
Opaque or “black box” models make it impossible to verify whether advice is fair, accurate, or appropriate. If a student doubts the advice, they may not continue to engage with the software. And once trust is lost, it’s nearly impossible to rebuild.
Transparency means clearly communicating the reasoning behind AI-driven insights. It’s about showing the relevant data points and explaining the logic behind each recommendation in an accessible language.
When students and advisors understand how a system works, they are empowered to engage with it confidently.
AI is only as reliable as the data that powers it. Fragmented, outdated, or inconsistent information can lead to AI delivering flawed advice or insights..
If a system draws from incomplete academic records or unverified student data, it can misrepresent students’ academic degree progress or direct students to outdated policies and resources. Worse yet, it can steer students toward inappropriate options that may set them on a path of academic challenges that may delay time to degree or encourage stopping out.
Data governance in AI must begin with integrity. Institutions should strive to:
AI adoption in higher education is accelerating faster than policy development. While many industries have clear compliance frameworks for algorithmic accountability, education is still catching up.
In banking, for example, AI models used to assess credit or detect fraud must adhere to strict laws, such as the Fair Credit Reporting Act and the Equal Credit Opportunity Act, which mandate transparency, explainability, and regular audits to prevent bias (see example).
Higher education, by contrast, has no such guardrails yet, leaving institutions to self-regulate complex AI systems that influence admissions, advising, and student support. Without clear standards, this fragmented approach risks uneven safeguards, hidden bias, and loss of trust among students.
Proper oversight of AI in higher education involves auditing algorithms for bias, verifying that recommendations align with institutional policies, protecting student data, and maintaining explainability so that decisions can be understood and challenged if necessary. This oversight safeguards students, maintains institutional integrity, and builds trust in AI-driven support.
Some questions that institutional leaders are already asking:
Institutions must develop clear AI governance frameworks that include:
Managing risk is a shared responsibility between universities and vendors.
Universities must set policy, ensure compliance, and monitor institutional risk—important roles for provosts and CIOs. Meanwhile, policy deans, faculty, and advisors can review AI outputs to ensure alignment with academic policy, university resources, and advising standards.
Vendors have responsibility, too, and can be a partner to universities in risk management and compliance. As an example, universities should ensure that the vendors develop transparent, auditable systems, providing documentation, and supporting fairness and compliance standards.
Click here for more information about best practices for ethical AI.
AI offers enormous promise for transforming the student experience, from personalized advising to streamlined career navigation. But with great promise comes great responsibility.
The true measure of AI’s success will not be in how many tasks it automates, but in how responsibly it helps students reach their purpose and potential.