College and university administrators and faculty continue to grapple with where to integrate AI and when to resist it. At the forefront of discussions and policymaking is academic integrity. Less discussed is how others – student affairs and classified staff professionals, admissions, human resources, and more – are (or are not) integrating AI into business processes and student services.
The EDU Ledger launched a LinkedIn poll in June 2026 to gauge where AI in higher education stands.
Thirty-four individuals responded to the poll. More than half – 52% of respondents indicated no formal policy on AI use is currently in place to guide AI use in admissions, advising, or hiring decisions. Another 26% said they are unsure whether any policies exist.
Only 8% of respondents indicated that a policy is in place. The final 11% of respondents noted that AI policy is in development.
These results are testament to higher education finding itself at a critical juncture where policies around AI use are urgently needed. Yet the pace of change and lingering questions about what will best serve students and not inadvertently reinforce bias complicate knowing exactly where to go and precisely what to do.
Exploring ways AI can strengthen student services
Colleges and universities have widely adopted AI in areas such as the inclusion of chatbots on their websites. One HBCU, South Carolina State University (Orangeburg), reported regaining 400 staff hours after rolling out chatbots. Automating answers for students through chatbots frees up valuable time for student affairs and classified staff professionals to provide tailored assistance and support to students face-to-face.
In May, the National Student Legal Defense Network published a “do’s and don’ts” guide on using AI to evaluate college applications. Among the do’s are thoroughly training admissions teams on how AI works, preserving human review of application, and being transparent with prospective students about exactly how AI is used.
Among the don’ts are not disclosing its use, investing in “black box” AI with unclear operational practices, and allowing unchecked bias to negatively impact admissions decisions. With AI, even sins of omission are sins of commission. Ignorance and confusion do not revoke responsibility for institutions or individuals.
Similar to Common App’s Oli, McGraw Hill has recognized AI’s potential to help with student retention through early alerts. Its AI-based technology, Sharpen Advantage, is meant to embed into retention processes to reach students with timely reminders (particularly after hours and on weekends, when many colleges and universities are partially or fully closed). These early alerts are designed to also reach advisors and administrators so they can take additional actions to support students in academic trouble (whether they realize it or not).
The nonhuman element of AI can help students feel more comfortable and lower fears of judgment of challenges they’re experiencing. During the peak of COVID, higher education faculty expressed hope that AI might be a scalable way to provide support to students experiencing mental health challenges and even while navigating crises.
Research published last November reported that over 13% of youth turn to AI for mental and emotional support. For people who are 18-21 years old, that number increases to over 22%. For those seeking mental health support, over 65% turned to AI at least once a month. Over 90% of respondents reported satisfaction with the experience and support provided.
Additional research is far less encouraging. Harvard Medical School researchers linked daily AI use with depressive symptoms. The Malone Center for Engineering in Healthcare (Johns Hopkins University) reported AI’s agreeability tendency to lead it to pose as a therapist, validate harmful thoughts expressed by distressed users, and – perhaps most infamously – help draft suicide notes.
In short, AI holds both great promise and great peril.
Human resources and hiring
Human resource offices are wise to move with great caution toward integrating AI into hiring decisions. Implicit bias can impact reviews of applicants’ resumes and CVs is well documented. Invisibility and overlooking some groups goes beyond facial recognition, as Joy Buolamwini demonstrated in Unmasking AI. Similarly, Karen Colbert documented problems emerging from identities being miscoded in data sets in The Algorithm Wasn’t Built for Us.
Even if all candidates are properly seen, colleges and universities still have a fundamental responsibility to protect sensitive data and personal information. Higher education institutions need to limit, if not restrict, agentic AI access to sensitive data, sometimes referred to as purple data.
In an ideal world, AI would help human resource offices (particularly those with small teams, and especially those with a team of one) find dynamic applicants with unique perspectives and diverse experience to the institution. It would ensure that compelling and promising applications are reviewed by hiring committees. It would also streamline and ease the hiring process, getting finalists into their roles quicker and before they are hired elsewhere.
However, it will take time and careful policy development to get to this desirable hiring process with the help of AI.
AI policy in higher ed: Urgency, uncertainty
Leaders, practitioners, and researchers frequently center discussions of AI on academic integrity, ensuring students are learning and avoiding cognitive offloading. Policies and practices that guide responsible AI – including significant faculty input – is necessary to avoid possible pitfalls.
At the same time, colleges and universities stand to improve processes and better serve their students if they can formulate and implement effective policies that guide out-of-class support. They also have the chance to strengthen job candidate pools by training AI to overcome in-built bias and better capture the background and strength of applicants.
