Artificial Intelligence in Higher Education: Transforming Practice

Increasing Student Engagement and Careers

Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning are disrupting and impacting every field. Higher education is like that. All university departments can use this technology to improve efficiency and increase overall student achievement. There are three major areas where AI can be used in higher education:

1. Administration.
2. Teaching.
3. Reading.

Figure 1. AI Strategic Impact Areas in Higher Education

Applying AI first in administrative areas can help achieve early benefits. On the other hand, AI for teaching and learning—like virtual tutors—is still in its early stages and may take several years to become mainstream. However, in the case of management, there are many common tasks that AI can simplify and completely transform. Processes such as student advising, applications, registration, financial aid/scholarships, exams, grading and assessment have the potential to improve AI and help universities achieve efficiency and scale.

Consider the student advising department that is often flooded with hundreds of questions from current/prospective students. The situation can be difficult in the area of ​​recruitment of new students. When many universities/colleges are competing to recruit the same student, they have no choice but to answer each question as quickly as possible. Speed ​​and response rate are important. But advisory groups can’t scale and often have difficulty responding to students.

Using Counselor Bots

AI can completely rewrite this scenario. Smart AI-powered “counsellor bots” can augment and improve the skills of admissions/career advisors for freshman recruitment. An advisor bot, available 24X7 can think and respond like a human counterpart. And as the number of questions changes, the bot can grow proportionally. Counselor bots can interact with prospective students like a real person and suggest the best courses based on the student’s background, interests, goals, budget, and time commitment. The key here is the personalization of the bot’s responses and the perceived accuracy of its suggestions, solutions, and recommendations.

Overcoming Counseling

Now, imagine you have a student who has found the right course with the help of your smart advisor bot. What’s next? How do you make it easy for a student to submit an application? Can AI help convert a student interested in a course into an applicant? Student engagement plays an important role in engagement and subsequent change. And AI can be a strong factor in improving conversions. Using AI, you can send the right messages through the right channel at the right time to increase the chances of getting the results you want. It’s all about taking students on a personal journey based on their behavioral patterns.

As an example of how AI can help, consider this: Not every reader responds to email reminders in the same way. Depending on whether students have opened the email or clicked on a specific link in the remaining time to complete the application/registration, the AI ​​can take different lessons. It can also learn from past campaigns, predict the success rate of certain engagement journeys and redesign processes to achieve specific campaign goals.

Once the program application has been submitted, the university needs to evaluate it. Again, AI can step in to evaluate most, if not all, applications and make decisions about student admissions.

Machine Learning And Algorithms Are Increasingly Advanced

Consider an MBA application received by an Ivy League university. Normally the university receives thousands of applications each year for a few hundred seats. Here, the goal of the admissions department may be to weed out applicants, leaving behind the best for further screening. AI can help automatically evaluate and remove applications that score low on one or more criteria. For example, automated essay scorers (a Natural Language Processing application in AI) can help grade essays submitted by applicants and quickly reject those that score below a certain number. The complexity of the application evaluation algorithm may depend on the number of admissions processes envisaged by the university and the level of accuracy desired. Such algorithms can be further trained and fine-tuned by providing feedback on the acceptance decisions made by the algorithm. Over time, the algorithm can learn from the completion rates of students who are automatically accepted into the program. If there are many students who do not successfully complete the program after being automatically admitted the algorithm may be allowed to adjust its own admission rules and student success criteria.

While students are already in the program, it is always important to early identify those at risk and come in with the right strategy to ensure student success in the program. Also, AI can help in this area. It can monitor and predict at-risk students based on specific behavior patterns and trigger appropriate student engagement programs at the right time to get students back on track.

Figure 2. AI Transformation in Higher Education

We’re Entering Tomorrowland

AI, Machine Learning and other such advanced technologies are able to learn processes quickly and continuously improve them in an indirect way. AI can open new frontiers for the success of universities; and without a doubt, your time is now.

Photo Credits

• Images within the body of the article were created/provided by the author.