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Higher-education institutions will have to increasingly rely on AI-based technologies to improve efficiencies, scale operations, and improve the student experience. And the time for it is now!

Artificial Intelligence technologies like Machine Learning and Deep Learning are disrupting every sector – and higher education is no exception! Every department in higher-ed institutions can effectively leverage these technologies to improve their efficiencies, enhance teaching outcomes, and boost student experience. The current COVID-19 situation worldwide has raised new challenges, and we believe these technologies have the potential to combat the disruptive impact that the higher-ed industry is facing due to the pandemic.

There are three major areas where AI can be applied in higher education:

  • Administration
  • Teaching
  • Learning

AI for teaching and learning—like virtual tutors—is still in its nascent stages and may take several years to become mainstream. However, in the case of administration, there are many routine tasks that AI can simplify and even transform completely. Processes such as student counseling, applications, enrollments, financial aid/scholarship processing, examinations, grading, and student evaluations offer great potential benefit by leveraging AI, that in turn could help universities achieve efficiencies and scale operations. Reduced student enrollment, scaled-down college operations, and widespread budget cuts in the wake of the pandemic have rendered university administrative functions to be the perfect place for leveraging AI-based technologies.

A Case for AI in Student Counseling

A student counseling department in any higher-ed institution is often flooded with hundreds, if not thousands, of queries from current/prospective students ahead of the admissions session. When multiple institutions compete to recruit the same student, they don’t have a choice but to respond to each query as early as possible. Speed and scale of response are critical. But counseling teams can’t scale and often struggle to respond to the students. At the same time, waiting for many minutes to reach a human counselor over a chat/phone is certainly not an experience that institutions wish to provide their potential students. Hence, augmenting human counselors’ capacity with AI Counselor Bots has become more important than ever.

Counselor Bots

AI can completely rewrite the current scenario. Smart AI-driven `Counselor Bots’ can augment and enhance the ability of ‘human’ admissions/career counselors for new student recruitments. The counselor bot, available 24X7, would think and respond just like its human counterpart. And as the number of queries change, the bot could scale in proportion.

Counselor bots can interact with prospective students just like a human and suggest the best courses matching the student’s background, career interests, objectives, budget, and time commitment. The key here is the personalization of responses by the bot and the perceived accuracy of its suggestions, solutions, and recommendations.

Student Application Bots

Now, assume you have a student who found the right course with the help of the smart counselor bot. What next? How do you make it easy for the student to submit an application? Can AI help in converting a student who is interested in a course to one who has applied? Student engagement plays a crucial role in the interaction and subsequent conversion, and AI can play a pivotal role in improving the conversion. With AI, you can analyze the behavioral patterns of applicants, so you can send the right message - through the right channel - at the right time. This increases student engagement and the possibility of converting an interested student to an applicant. And all this without adding any extra staff!

For instance, not every student responds to email reminders in the same way! Depending on whether students have opened an email or clicked on a specific link in the time left to complete application/enrollment, AI can analyze and take different courses of action. It can also learn from past campaigns, predict the rate of success from specific engagement journeys, and redesign processes to achieve specific campaign goals. Not just that, once a program application has been submitted, the university needs to evaluate it. AI can also be leveraged to screen applications and take decisions on student acceptance.

Bots for Maximizing Student Engagement and Learning Outcomes

Consider the number of applications received by an Ivy League B-school university. Typically, the university receives thousands of applications each year for a few hundred seats. Here, the goal of the admissions department could be to eliminate applicants, leaving behind only the very best for further scrutiny. Even here AI can help automatically screen and eliminate applications that score low on one or more criteria. For example, automated essay scorers (an application of Natural Language Processing in AI) can help grade essays submitted by applicants and straight away reject those who scored less than a specific number. The complexity of the application screening algorithm may depend upon the number of acceptance criteria considered by the university and also the level of accuracy desired. Such algorithms can be further trained and fine-tuned by providing feedback on the admission decisions taken by the algorithm. Over a period of time, the algorithm can learn from the completion rates of the students who get auto accepted in the program. If there are many students who don’t complete the program after getting auto-admitted, then the algorithm may be allowed to self-adjust acceptance rules and the criteria for student success.

While students are already in a program, it’s always important to identify in advance those who are at risk and pitch in with the right strategy to ensure student success in the program. The current pandemic has forced millions of students across the globe to adopt the remote learning route and this new way of learning may have a significant impact on student success. More universities are worried about this one aspect than anything else and are trying to find ways to minimize the impact. Again, AI can help in this area. It can monitor and predict at-risk students based on specific behavioral patterns and trigger the right student engagement plans at the right time to bring students back on track.

Stepping Into Tomorrow

AI, ML, and other such advanced technologies are able to learn processes quickly and improve them continuously in a non-linear fashion. AI can open many new frontiers of success for higher-ed institutions; and without doubt, the time for it is now.


About the Author

Amit Golwala
Business Analyst

Amit is a Business Analyst in Mindtree's Communications, Media & Education and Technology industry group. He is a certified Salesforce consultant who helps Mindtree clients leverage the power of the Salesforce cloud computing platform beyond CRM. He comes with over 10 years of experience working with clients in Higher Education, Property and Casualty Insurance, and ALM product domains.

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