In Brief
A guide to potential applications and how to get started
- From enhancing recruitment to streamlining research compliance, artificial intelligence (AI) offers endless possibilities for higher education and research institutions.
- When choosing where to apply AI, institutions should carefully consider areas where it can have the most significant impact - including operational efficiency and cost-savings, improved decision making, enhanced student support, and more effective resource management.
- Critical initial implementation steps include establishing guiding principles, selecting highly repetitive or data-intensive tasks, and empowering employees.
- A guide for implementing AI solutions in multiple areas, including recruitment marketing, admissions, student success, operations, advancement and fundraising, and research operations and compliance, is provided.
At colleges, universities, and research institutions — where challenges are complex and resources are limited — the possibilities for artificial intelligence (AI) at the institutional and individual levels are vast.
AI can help empower faculty, staff, researchers, and students to achieve more and assist institutions with delivering their missions more efficiently and effectively, particularly in the face of resource scarcity and uncertainty.
But where to begin? As with any technology implementation, careful consideration should be given to parts of campus where AI can drive the most efficiency in support of the core mission. Evaluate how AI can be integrated into operations that support research, academics, and community impact.
The following are seven potential focus areas and initial steps for implementing AI solutions in higher education and research.
Potential focus areas
Admissions
By automating administrative tasks and helping admissions teams review large application volumes quickly, AI is paving the way for a more equitable and efficient selection process.
Sample challenges
- Managing and reviewing thousands of applications within short timelines
- Ensuring decisions are objective and free from unconscious bias
How AI can help
- Automated application review systems: Streamline candidate screening using predefined criteria, such as GPA or coursework rigor.
- Natural Language Processing (NLP) tools: Analyze essays and personal statements, evaluating linguistic patterns and key themes aligned with admissions criteria.
- AI recommendation systems: Personalize admissions offers based on an applicant's likelihood of enrollment or scholarship eligibility.
- Fraud detection tools: Use algorithms to uncover anomalies or inconsistencies in submissions, safeguarding institutional integrity.
Advancement and fundraising
Cultivating donor relationships and running successful fundraising campaigns are essential for institutional sustainability. AI optimizes both areas, ensuring no potential opportunity is missed.
Sample challenges
- Identifying and engaging major donors
- Measuring and improving campaign effectiveness
How AI can help
- Predictive donor analytics: Identify alumni and potential donors whose giving history, wealth indicators, or affiliations suggest high engagement potential.
- Natural Language Generation (NLG): Craft personalized outreach communications while freeing advancement teams to focus on relationship building.
- AI performance tracking tools: Measure what works in fundraising campaigns and pinpoint where improvement is needed.
- Sentiment analysis: Monitor donor feedback to maintain strong, trust-based relationships.