Demystifying AI: A Guide to AI Literacy in Higher Education Enrollment Management

Key Takeaways:

• AI literacy is crucial for enrollment teams to understand, evaluate, and responsibly use AI to enhance recruitment, retention, and operations.
• Core components of AI literacy in education include technical understanding, critical evaluation, practical applications, and ethical considerations.
• AI helps enrollment teams target high-priority prospects, personalize communications, and streamline processes like financial aid distribution and application management.
• Ethical use of AI requires mitigating biases, ensuring data privacy, and maintaining transparency to build trust with stakeholders.
• Building an AI literacy framework involves training, establishing ethical guidelines, promoting collaboration, and implementing AI strategically through pilot projects.

Artificial intelligence is rapidly transforming higher education, particularly in enrollment management. To harness AI’s full potential, enrollment leaders must develop a comprehensive understanding of AI literacy. This encompasses not only technical knowledge but also practical skills and ethical considerations. By cultivating AI literacy, enrollment teams can enhance recruitment strategies, improve student retention, and streamline administrative tasks. This guide provides a detailed framework to help enrollment professionals navigate AI’s complexities and apply its capabilities effectively and responsibly.

What Is AI Literacy, and Why Does It Matter for Enrollment Teams?

Defining AI Literacy in Education

What is AI literacy exactly? In essence, it is the ability to understand, evaluate, and responsibly use AI technologies. For enrollment teams, it’s not enough to simply know what AI is. They need the skills to critically analyze AI-driven insights, integrate AI into their strategies, and address the concerns that come with data-driven, AI-informed decision making.

Key aspects of AI literacy in enrollment management include:

• Technical Understanding: Grasping the fundamental principles of AI, including how algorithms process data, recognize patterns, and generate recommendations. This knowledge enables enrollment professionals to comprehend the capabilities and limitations of AI tools so they are applied effectively within recruitment and retention strategies.
• Practical Application: Developing the ability to interact with AI systems, interpret their outputs, and integrate them into daily operations. This involves selecting appropriate AI technologies, customizing them to meet specific institutional needs, and using them to enhance processes such as student outreach, application evaluations, and enrollment forecasting.
• Ethical Frameworks: Recognizing the ethical implications of AI tools in higher education, including issues related to bias, transparency, and data privacy. Enrollment teams must ensure that AI aligns with institutional values and complies with legal standards, building trust with prospective students and stakeholders.

Frameworks like EDUCAUSE’s AI literacy model offer a useful starting point. These competencies empower enrollment leaders to go beyond surface-level knowledge.

Technical Comprehension: This is the foundation of AI literacy and involves understanding the mechanics of how AI functions, including:

• Algorithms and Data Inputs: Recognizing the types of data that AI systems require and how algorithms interpret this information. For example, enrollment tools may rely on demographic, behavioral, and academic data to predict application outcomes.
• System Limitations: Knowing what AI cannot do is just as important as understanding its capabilities. For instance, AI predictions are only as accurate as the data provided, and missing or biased data can lead to flawed results.

Evaluative Skills: This area focuses on the ability to critically assess the outputs and applications of AI tools. Enrollment teams should be equipped to:

• Examine Outputs: Scrutinize AI-driven findings to make sure they align with enrollment goals. For example, are the recommendations actionable and accurate? Are there unexplained anomalies in the data?
• Impact Assessments: Use AI impact systems to assess how AI influences institutional decision making and whether it produces equitable outcomes.
• Tool Selection: Choose AI tools that meet ethical and operational standards while resolving specific enrollment challenges.

Practical Application: This competency emphasizes the integration and management of AI tools in daily operations, making them an essential resource for achieving institutional goals. For enrollment teams, this involves:

• Tool Integration: Adopting AI tools to streamline processes, such as automating email follow-ups or boosting applicant scoring models.
• Resource Optimization: Using AI-generated insights to allocate time and effort more efficiently, such as prioritizing outreach to prospective students most likely to enroll.
• Continuous Management: Regularly reviewing and refining AI applications so they remain effective as institutional priorities evolve.

Ethical Considerations: The ethical dimension of AI literacy ensures that AI is deployed responsibly and transparently. Enrollment teams should emphasize:

• Bias Mitigation: Recognizing and addressing biases in AI algorithms, particularly those that could unfairly disadvantage certain student groups.
• Transparency: Clearly communicating how AI recommendations are used in decision making, such as explaining the factors behind financial aid offers or admissions decisions.
• Data Privacy: Adhering to robust security protocols to protect sensitive student information, including financial and academic records.

Each competency can be prioritized differently depending on the institution’s unique goals and challenges. For instance, an enrollment office facing a significant drop in applications might emphasize the practical employment of AI for recruitment, while another institution with a focus on equity may prioritize ethics.

Why Enrollment Leaders Must Prioritize AI Literacy

Enrollment teams operate within a rapidly changing environment, shaped by several complex challenges. One of the most pressing issues is the demographic cliff—a projected and ongoing decline in the number of high school graduates across the United States. With fewer prospective students entering the pipeline, competition between institutions is intensifying. Enrollment offices must now work harder to attract students, requiring innovative strategies to stand out and communicate value. AI tools, when paired with a strong AI literacy framework, empower enrollment leaders to proactively identify and target high-potential student populations, giving them a competitive edge in this increasingly crowded landscape.

Another significant challenge is shifting student expectations. Today’s prospective students expect far more from their enrollment journey than in years past. They want personalized interactions, prompt responses to inquiries, and seamless experiences that reflect their individual goals and aspirations. Meeting these expectations requires a sophisticated knowledge of student behavior and preferences—an area where AI excels. Predictive analytics, for example, can help enrollment teams tailor communication and outreach to match what students are looking for at every step of the process.

Finally, limited resources remain a critical challenge for most enrollment offices. Budget cuts and staffing shortages place additional strain on teams, making it difficult to achieve ambitious enrollment and retention goals. This is where AI-powered systems can shine by automating repetitive administrative tasks, simplifying operations, and allowing staff to concentrate on higher-impact activities.

The teams that prioritize AI literacy will be better positioned to overcome these hurdles and thrive in a competitive higher education environment, proving that understanding AI isn’t just an advantage—it’s a necessity.

Core Components of AI Literacy for Enrollment Management

To fully realize the benefits of AI technology in enrollment management, leaders must cultivate a comprehensive understanding of its foundational elements. Each of the below components plays a critical role in enabling teams to navigate the complexities of AI.

Component 1: Technical Understanding for Enrollment Success
Technical know-how is the cornerstone of AI literacy, enabling enrollment teams to recognize the capabilities and limitations of AI. Key concepts include:

• Predictive Analytics: These models analyze historical data to forecast student behavior. For example, predicting which applicants are likely to enroll lets enrollment counselors target their efforts.
• Prescriptive Analytics: These tools recommend actions based on data trends, such as adjusting scholarship offers or recruitment events to improve outcomes. Liaison’s Othot AI solution leverages both predictive and prescriptive analytics to help institutions optimize enrollment strategies. By analyzing past trends and real-time data, Othot lets enrollment leaders target high-priority prospects with tailored messaging.
• Generative AI: Chatbots and virtual assistants can answer prospective student inquiries, providing instant support and freeing up staff time.
• Data Integrity: Teams must verify that the data feeding into AI systems is accurate, diverse, and representative to avoid misleading insights.

Component 2: Evaluative Skills for Effective AI Use
Evaluating AI tools requires a critical lens to ensure that they’re effective and aligned with institutional goals. Enrollment teams should focus on:

• Assessing Data Quality: Is the data diverse enough to avoid bias? Are there gaps that could lead to inaccurate predictions?
• Reviewing Tool Performance: Are AI outputs accurate, actionable, and reliable?
• Considering Ethical Implications: Do AI-driven decisions reflect institutional values? For example, does a predictive model consider equity when identifying high-priority prospects?

Enrollment offices can implement regular audits of AI, upholding transparency and accountability in their operations. These audits should assess the accuracy and relevance of AI-generated information so teams can identify any discrepancies or biases that might affect decision making. Audits can also determine whether the tools align with evolving institutional goals, keeping them effective and ethical as enrollment priorities shift. Regular reviews not only enhance trust in AI-driven processes but also create a culture of continuous improvement within enrollment teams.

Component 3: Practical Applications for Real-World Enrollment Goals
Practical application is where the theoretical potential of AI translates into measurable outcomes for enrollment teams. While understanding how AI works is essential, its true value lies in its ability to streamline processes, refine student interactions, and inform strategic decision making. Enrollment offices can use AI to:

• Target Recruitment Efforts: Predict which students are most likely to enroll and allocate resources accordingly.
• Enhance Communication: Automate personalized messages to prospective students based on their interests and behavior.
• Simulate Enrollment Scenarios: Test the impact of different strategies, such as adjusting financial aid offers, before making decisions. [GU1] Othot’s financial aid matrix optimization provides the insights needed to mitigate risk, make informed decisions, and implement packaging strategies with confidence.

Component 4: Ethical and Responsible Practices
Ethics are critical to AI literacy, as they make sure AI technologies are utilized responsibly and build trust with stakeholders. Ethical considerations are not just about mitigating risks—they are also about centering equity, accountability, and fairness in AI implementation strategies. Key considerations include:

• Data Privacy: Enrollment teams handle sensitive student information, making robust security protocols essential.
• Bias Mitigation: AI tools must be evaluated for potential biases that could unfairly impact certain student groups.
• Human Oversight: While AI provides valuable insights, human judgment is essential to ensure fair and empathetic decision making.

Tailoring AI Literacy Frameworks to Enrollment Management Goals

Transforming the Enrollment Funnel

Enrollment teams face growing pressure to achieve ambitious recruitment goals despite shrinking resources and heightened competition. AI-powered tools solve this problem by streamlining recruitment and enabling more strategic approaches.

These tools can analyze applicant behavior to forecast which prospective students are most likely to enroll, allowing teams to focus their efforts on high-impact opportunities. AI also enhances communication by personalizing outreach, tailoring messages to align with individual student interests and preferences. Liaison Search, for example, is a key tool for optimizing recruitment strategies, identifying high-priority prospects, and delivering personalized communications that drive engagement and enrollment. Beyond these benefits, AI helps enrollment leaders identify underperforming stages in the enrollment funnel, offering actionable findings to refine strategies and improve outcomes. By leveraging AI strategically, enrollment offices can boost efficiency and recruitment numbers.

One success story comes from a small state institution in the Northeast that deployed Othot to revamp its enrollment strategy. By tracking prospective students throughout the application funnel, the institution increased FAFSA completion rates, event attendance, and overall engagement. The result was a remarkable 20% boost in first-year enrollment—a testament to the power of AI-powered recruitment efforts.

Improving Retention Through Early Intervention

Retention is as crucial to institutional success as recruitment, and AI is an invaluable tool for helping students stay on track. Predictive analytics, for example, can identify students who may be at risk of dropping out by analyzing behavioral patterns, academic performance, and levels of engagement. Once these at-risk students are identified, AI can recommend targeted interventions to meet their specific needs. These might include academic support services, such as tutoring or study workshops, or financial aid adjustments to alleviate economic barriers.

One university recently analyzed retention data to identify a correlation between high school GPA and first-year retention rates. Using predictive modeling, the university discovered that students with high school GPAs between 3.0 and 3.3 were at a higher risk of facing academic difficulties. Based on this revelation, administrators adjusted support services to include students within this GPA range, emphasizing resources like tutoring and mentorship earlier in their academic journey. This strategy led to a 4% increase in retention for the at-risk group, demonstrating how AI-driven insights can inform salient decisions and targeted interventions.

Streamlining Administrative Processes

Administrative inefficiencies can drain valuable resources and negatively impact the student experience. AI addresses these challenges by automating routine and time-consuming tasks, helping enrollment teams operate more effectively. For example, AI can process applications and manage follow-ups with prospective students, ensuring timely and consistent communication. It can also generate detailed reports on enrollment trends and outcomes, providing actionable information without requiring manual data analysis. AI technologies can streamline financial aid distribution, leading to faster and more accurate resource allocation.

By automating this repetitive work, AI frees staff to give time to higher-value activities, such as building meaningful relationships with prospective students and improving the overall enrollment experience.

Tailoring AI Literacy Frameworks to Enrollment Management Goals

Transforming the Enrollment Funnel

Enrollment teams face growing pressure to achieve ambitious recruitment goals despite shrinking resources and heightened competition. AI-powered tools solve this problem by streamlining recruitment and enabling more strategic approaches.

These tools can analyze applicant behavior to forecast which prospective students are most likely to enroll, allowing teams to focus their efforts on high-impact opportunities. AI also enhances communication by personalizing outreach, tailoring messages to align with individual student interests and preferences. Liaison Search, for example, is a key tool for optimizing recruitment strategies, identifying high-priority prospects, and delivering personalized communications that drive engagement and enrollment. Beyond these benefits, AI helps enrollment leaders identify underperforming stages in the enrollment funnel, offering actionable findings to refine strategies and improve outcomes. By leveraging AI strategically, enrollment offices can boost efficiency and recruitment numbers.

One success story comes from a small state institution in the Northeast that deployed Othot to revamp its enrollment strategy. By tracking prospective students throughout the application funnel, the institution increased FAFSA completion rates, event attendance, and overall engagement. The result was a remarkable 20% boost in first-year enrollment—a testament to the power of AI-powered recruitment efforts.

Improving Retention Through Early Intervention

Retention is as crucial to institutional success as recruitment, and AI is an invaluable tool for helping students stay on track. Predictive analytics, for example, can identify students who may be at risk of dropping out by analyzing behavioral patterns, academic performance, and levels of engagement. Once these at-risk students are identified, AI can recommend targeted interventions to meet their specific needs. These might include academic support services, such as tutoring or study workshops, or financial aid adjustments to alleviate economic barriers.

One university recently analyzed retention data to identify a correlation between high school GPA and first-year retention rates. Using predictive modeling, the university discovered that students with high school GPAs between 3.0 and 3.3 were at a higher risk of facing academic difficulties. Based on this revelation, administrators adjusted support services to include students within this GPA range, emphasizing resources like tutoring and mentorship earlier in their academic journey. This strategy led to a 4% increase in retention for the at-risk group, demonstrating how AI-driven insights can inform salient decisions and targeted interventions.

Streamlining Administrative Processes

Administrative inefficiencies can drain valuable resources and negatively impact the student experience. AI addresses these challenges by automating routine and time-consuming tasks, helping enrollment teams operate more effectively. For example, AI can process applications and manage follow-ups with prospective students, ensuring timely and consistent communication. It can also generate detailed reports on enrollment trends and outcomes, providing actionable information without requiring manual data analysis. AI technologies can streamline financial aid distribution, leading to faster and more accurate resource allocation.

By automating this repetitive work, AI frees staff to give time to higher-value activities, such as building meaningful relationships with prospective students and improving the overall enrollment experience.

A Framework for Action: Steps to Build AI Literacy in Enrollment Offices

While the benefits of AI technology are significant, institutions must take deliberate steps to make sure their teams have the knowledge, skills, and ethical grounding to use AI effectively. The below steps give enrollment leaders a roadmap for building AI literacy, thereby encouraging teams to make data-informed decisions that drive recruitment, retention, and operational success.

Step 1: Build foundational knowledge

The first step in developing AI literacy is helping enrollment professionals grasp the basics of AI technologies and their applications. Institutions can offer workshops, webinars, and certifications tailored to higher education enrollment to build this foundation. Partnering with AI experts, such as Liaison, can provide valuable insights and training resources.

Action item: Encourage cross-departmental learning by organizing sessions where IT professionals and institutional researchers collaborate with enrollment teams. This creates a shared understanding of how AI works and how it can be applied to solve specific enrollment challenges. Teams should also create a glossary of AI terminology to demystify jargon and build confidence in using AI tools.

Step 2: Establish ethical guidelines

As AI becomes more integrated into enrollment processes, institutions must establish clear ethical guidelines to maintain responsible usage. These guidelines should address data privacy, equity, and transparency, emphasizing the importance of protecting sensitive student information.

Action item: Write a code of conduct for AI use that includes protocols for data handling and outlines acceptable AI applications in enrollment. Regularly audit AI tools for bias and conduct impact assessments to verify that they reflect institutional values and do not disadvantage specific student groups. Make these guidelines accessible to all staff and stakeholders to create a culture of accountability.

Step 3: Promote cross-functional collaboration

Successful AI implementation requires collaboration between enrollment teams, IT departments, institutional researchers, and other key stakeholders. Cross-functional collaboration ensures that all perspectives are considered, from technical feasibility to enrollment strategy alignment.

Action item: Create a dedicated task force that includes representatives from different departments to oversee AI-related initiatives. This group can work together to evaluate potential tools, set shared goals, and tackle challenges. Regular meetings or workshops can foster communication and trust across teams, maintaining agreement on AI literacy priorities.

Step 4: Implement AI strategically

After building foundational knowledge and creating an ethical framework, enrollment offices should focus on strategic implementation. Starting small with pilot projects allows institutions to measure the effectiveness of AI and refine their approach before scaling initiatives.

Action item: Begin with targeted pilots, such as using a chatbot to manage prospective student inquiries or employing predictive analytics to identify high-priority applicants. Track metrics, including response times, enrollment conversion rates, or student engagement, to evaluate success. Use the insights from these pilots to identify best practices, mitigate challenges, and expand AI usage across enrollment operations. Continually revisit and update AI strategies as institutional goals evolve.

Unlocking the Potential of AI Literacy in Enrollment Management

AI is reshaping higher education, offering enrollment leaders unparalleled opportunities to optimize recruitment, enhance retention, and streamline administrative processes. However, to fully leverage these benefits, institutions must prioritize AI literacy as a core competency. By understanding AI’s technical underpinnings, examining its outputs critically, applying it strategically, and upholding ethical practices, enrollment teams can ensure that AI is implemented effectively and responsibly.

Is your institution ready to embrace the future of AI in enrollment management? Partner with Liaison to equip your teams with the tools, training, and support they need to thrive in an AI-driven landscape. Our solutions help you build a data-informed strategy that achieves your enrollment and retention goals. Connect with us today to learn how AI literacy can transform your institution’s approach to student success.