Expanding student participation and operations
Artificial intelligence (AI), machine learning (ML), and deep learning are disrupting and impacting every field. Higher education is no exception. All university departments can use these technologies to improve efficiency and promote overall student success. There are three main areas where AI can be applied in higher education.
management. What to teach. learn.
Figure 1. Strategic impact areas for AI in higher education
Applying AI first to the management field can reap early benefits. On the other hand, AI for teaching and learning, such as virtual tutoring, is still in its infancy and could take years to become mainstream. However, when it comes to management, there are many routine tasks that can be simplified or even completely transformed by AI. Processes such as student counseling, applications, registration, financial aid/scholarships, testing, grading, and evaluation have the potential to leverage AI to help universities achieve efficiency and operational scale.
Consider a student counseling department that typically receives hundreds of inquiries from current and prospective students. When it comes to recruiting new students, things can be difficult. When multiple universities compete for the same student, you have no choice but to respond to each inquiry as quickly as possible. The speed and scale of response are important. However, counseling teams are unable to scale and often struggle to serve students.
Using the counselor bot
AI can completely rewrite this scenario. Smart AI-powered “counselor bots” can augment and enhance the new student recruitment capabilities of human admissions/career counselors. Counselor bots are available 24/7 and think and respond just like their human counterparts. Also, as the number of queries changes, the bot can also scale proportionately. Counselor bots can interact with prospective students like a real human and suggest the best course that fits the student’s background, career interests, goals, budget, and time commitments. The key here is the personalization of the bot’s responses and the accuracy of its suggestions, solutions, and recommendations.
Beyond counseling
Now, suppose we have a student who finds a suitable course with the help of a smart counselor bot. What’s next? How can we make it easier for students to submit applications? Can AI help convert students who are interested in a course into students who have applied? Student engagement plays a key role in the interaction and subsequent conversion. And AI can be a powerful factor in improving conversions. AI allows you to send the right message on the right channel at the right time, increasing your chances of getting the desired results. It’s all about taking students on a personal journey based on their behavioral patterns.
As an example of how AI can help, consider: Not all students respond to email reminders in the same way. The AI can take different actions depending on whether the student opens the email or clicks on a specific link within the time remaining to complete the application/registration. You can also learn from past campaigns, predict success rates from specific engagement journeys, and redesign processes to achieve specific campaign goals.
Once a program application is submitted, the university must evaluate it. Again, AI can review most, if not all, applications and make decisions about student acceptance.
Machine learning and algorithms get better with age
Consider an MBA application received by an Ivy League university. Universities typically receive thousands of applications each year for a few hundred spots. Here, the admissions department’s goal may be to weed out applicants and keep only the best applicants for further scrutiny. AI can help you automatically screen and eliminate applications that score poorly on one or more criteria. For example, automated essay scoring (an application of natural language processing in AI) can help score essays submitted by applicants and instantly reject essays that score below a certain number. The complexity of application screening algorithms depends on the number of acceptance criteria a university considers and the level of accuracy required. Such algorithms can be further trained and fine-tuned by providing feedback on the authorization decisions made by the algorithm. The algorithm can learn from the completion rates of students who are automatically accepted into the program over a period of time. If a large number of students fail to successfully complete a program after automatic admission, an algorithm may be able to automatically adjust acceptance rules and student success criteria.
Although students are already participating in the program, it is always important to proactively identify those at risk and develop appropriate strategies to engage students to ensure their success in the program. Again, AI can help in this area as well. You can monitor and predict which students are at risk based on specific behavior patterns and trigger the right student engagement plan at the right time to get them back on track.
Figure 2. Transforming AI in higher education
A step to Tomorrowland
AI, machine learning, and other advanced technologies can quickly learn processes and continuously improve them in a nonlinear manner. AI has the potential to open new frontiers of success for universities. And definitely, now is the time.
Image credits Images in the article were created/provided by the author.
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