The Pulse of Higher Ed

By Vickie S. Cook, Ph.D.

I recently contributed a chapter in the recently released book, AI Applications in Online Higher Education Administration: Strategies for Maximizing Returns and Improving Outcomes edited by Kathleen Ives, Marie Cini, and Ray Schroeder. This blog highlights key take-aways from my chapter. Higher education recruitment is undergoing one of the most significant transformations in decades. Traditional strategies—direct mail campaigns, college fairs, generalized advertising, and broad outreach—once served institutions well. However, these methods are increasingly insufficient in a digital-first environment shaped by new student expectations, demographic shifts, and intense enrollment competition. The 2026 Modern Learner Report re-evaluates how prospective students search, evaluate, and ultimately choose an institution.

Artificial intelligence (AI) is rapidly emerging as a strategic tool for modernizing enrollment management. By leveraging data analytics, predictive modeling, and machine learning, institutions can move beyond broad recruitment tactics that are no longer meeting the needs of today’s students toward personalized, efficient, and scalable engagement strategies.

For enrollment leaders, the question is no longer whether AI will influence recruitment. The real question is how institutions can thoughtfully integrate AI into their enrollment management strategies to provide their prospective students with choices that align to the students’ personal values.

Why Traditional Recruitment Models Are Under Pressure

For decades, recruitment models treated prospective students largely as a homogeneous audience. Institutions relied on broad messaging and generalized outreach strategies designed to reach large pools of potential applicants. While these methods once generated strong results and allowed institution to accurately predict enrollment. Let’s explore why these approaches no longer meet the needs of students or institutions:

1. Traditional outreach often lacks personalization. Students increasingly expect communication tailored to their academic interests, career goals, and personal aspirations. Generic messaging can feel disconnected and may fail to capture student attention.
2. Traditional recruitment efforts are resource-intensive. Activities such as mass mailings or extensive travel to recruitment events require substantial financial and human capital investment, often with limited ability to measure real-time impact.
3. Many institutions underutilize the data they already collect. Admissions systems, marketing platforms, and student information systems contain valuable insights, yet traditional recruitment models rarely leverage predictive analytics to identify high-potential prospects or triangulate the data for prospective students in meaningful ways.

Meeting these current challenges have opened the door for AI-driven recruitment strategies.

AI as a Strategic Enrollment Tools

Artificial intelligence tools offer institutions the ability to transform recruitment practices by combining automation with sophisticated data analysis. AI-powered systems can analyze large datasets to identify patterns in student behavior, predict enrollment more accurately, and automate communication processes that previously required significant manual effort.

When used effectively, AI tools can support enrollment teams by:

• Identifying prospective students most likely to apply or enroll
• Personalizing communication across multiple platforms
• Improving response time to student inquiries
• Streamlining admissions operations
• Enhancing the efficiency of recruitment campaigns

Importantly, AI should not replace the human relationships that remain central to recruitment. Instead, it enables enrollment professionals to spend less time on repetitive tasks and more time engaging directly with students and families.

Personalization at Scale

One of the most significant advantages of using a variety of AI tools in recruitment is its ability to deliver personalized communication at scale.

Today’s students—particularly those from Generation Z and Generation Alpha—expect interactions that are fast, relevant, and tailored to their interests without being overly complex in implementation. Institutions that fail to meet these expectations risk appearing outdated or unresponsive.

Examples of AI-enabled personalization include:

• Chatbots that are accurate and provide 24/7 responses to student questions
• Automated email campaigns tailored to specific academic interests
• Targeted digital advertising aligned with student preferences and values
• Customized recruitment messaging based on engagement patterns
• Institutional communication that respects individual privacy

The Power of Predictive Modeling

Predictive modeling represents another major application of AI in enrollment management. By using machine learning algorithms, institutions can analyze historical data to identify patterns that predict which students are most likely to apply, enroll, persist, and graduate and thus assign more human interaction with those prospective students. This improves recruitment efficiency while allowing institutions to allocate resources more strategically toward yield.

However, effective predictive modeling requires a strong data infrastructure. Institutions must ensure that data from various systems—such as CRM platforms, admissions systems, and marketing tools—are integrated into a unified data environment.

Moving Forward with Responsible AI

Responsible AI adoption requires clear policies governing data privacy, transparency, fairness, and algorithmic accountability. Students must trust that institutions are using their data responsibly and ethically. Training and professional development will also be essential. Enrollment professionals must develop the skills needed to interpret AI insights accurately and integrate these insights into effective recruitment strategy.

The Future of AI in Enrollment Management

Artificial intelligence will continue to reshape student recruitment. As technologies evolve, institutions will see increasing integration between recruitment, admissions processing, financial aid, and student success systems.

Institutions that begin building AI-ready infrastructures today will be better positioned to meet the expectations of future students and remain competitive in a rapidly changing enrollment landscape.

For enrollment leaders, the task ahead is clear: move beyond experimentation and begin developing strategic frameworks for responsible, effective AI adoption.

References:

Cook, V. S. (2026) Artificial Intelligence Employed in Student Recruitment in Ives, K.S., Cini, M, Schroeder, R. (Eds). AI Applications in Online Higher Education Administration: Strategies for Maximizing Returns and Improving Outcomes. New York: Routledge.

Education Dynamics (2026) Modern Learning Report. https://insights.educationdynamics.com/rs/183-YME-928/images/EDDY-Modern-Learner-Report-2026.pdf

Vickie Cook is a Strategic Advisor for UPCEA Research and Consulting.  To learn more about UPCEA Research and Consulting, please contact [email protected].