AI in education
AI and generative AI are enabling educational institutions to enhance student achievement. These technologies help leaders innovate, measure learning recovery, identify at-risk students, and improve enrollment and retention rates – all while preparing for future challenges.
AI use cases for education
Advance educational innovation with data and AI solutions from SAS. Our solutions help you plan more thoroughly, run programs more resourcefully, confidently maintain compliance and predict and prepare more accurately.
Course schedule optimization
Use SAS to optimize course scheduling. Match seat supply with student demand, maximize the options and opportunities for both traditional and nontraditional students and improve student outcomes.
The value of this solution:
- Improved student outcomes.
- Maximized operational efficiency.
AI techniques used in this solution:
- Machine learning models in conjunction with advanced analytics.
How AI helps:
- Improve student outcomes. Create classroom schedules that are better aligned with student needs to improve retention, decrease DFW rates and reduce time to completion.
- Better utilize campus facilities. Reduce capital expenses by more efficiently using the classrooms already available. Institutions can creatively overcome constrained spaces and limited faculty availability through efficient course scheduling.
- Reduce schedule turnaround time for dynamic changes. Flexibility allows you to pivot due to unexpected changes during the academic year.
- Gain administrative efficiencies. Automated schedule generation can eliminate the time-consuming manual effort of adjusting course schedules.
The AI models provide:
- Multivariate regression models with stepwise selection at different levels of granularity and multi-objective mixed integer linear programming models.
- Generate an automated feasible course schedule and maximize student performance.
- Provide decision support on the assignment of courses to professors, rooms and time slots.
- Predict student performance.
Enrollment projections
Forecast enrollment counts using institutional data along with economic data.
The value of this solution:
- Faster decision making.
- Increased revenue.
AI techniques used in this solution:
- Machine learning
How AI helps:
- Improved budgeting.
- Better preparation for the incoming number of students.
- Improved proactive intervention.
The AI models provide:
- Projections for underlying factor importance, confidence interval and outyear factors.
Reynolds Community College uses SAS to better understand trends in their facilities' utilization and capacity with an interactive dashboard.
"Near-grad" graduation guidance
Encourage students to re-enroll with chatbots that identify that they have nearly met graduation requirements. This system enables an academic institution to reach thousands of students in real time.
The value of this solution:
- Better outcomes.
- Increased revenue.
- Improved customer service.
AI techniques used in this solution:
- Machine learning models are used to prepare data on students who are "near grads."
- Data is fed into an LLM-powered chatbot that reaches out to students, encouraging them to re-enroll.
How AI helps:
- Lists students who meet the "near grad" criteria.
- Unites data from disparate sources.
- Chatbot provides an intelligent communication outreach tool at scale.
The AI models provide:
- The deep learning model performs underlying factor importance, confidence interval and outyear projections.
Financial aid optimization
Use synthetic data to fuel a machine learning model to shuffle around flexible financial aid to maximize key performance indicators (KPIs) such as enrollment and net tuition revenue.
The value of this solution:
- Improved student outcomes.
- Better outcomes.
- Increased revenue.
AI techniques used in this solution:
How AI helps:
- Greater student retention.
- Higher enrollment.
- Save funds.
- Maximizes net tuition revenue.
The AI models provide:
- Optimal financial aid by student.
- Optimizes overall financial aid pool, total enrollment and net tuition revenue.
By using predictive analytics, a large state university had its largest and most academically prepared student body ever.
