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The AI Powered Campus

Artificial Intelligence has been in the news a lot lately, even on the higher education side. MIT recently rolled out a billion-dollar investment in a college of AI and Computer Science (though what that will actually mean in the long term has yet to be seen). Carnegie Mellon deployed the first undergraduate degree in AI. Google created and shared curriculum on machine learning. It’s hard to escape the buzz. Some of the best innovations in AI are those that aim to improve how we teach and support our students and we’d like to share some thoughts from our new research brief, AI in Higher Education, regarding how AI could improve aspects of university systems in the near future.

The Chatbot as an Enrollment Counselor and Teaching Assistant

Getting into college is hard. It doesn’t help, especially for the adult learner, that most institutions operate during regular working hours when (shocker) the applicants are at work as well. Georgia State University has been making headlines as it’s rolled out Pounce, its AI-powered chatbot (developed with the help of Admithub) that can help students work through all of those tricky questions outside of office hours, demystifying the application process for students and helping them enroll with confidence.

There must be something in the water in Atlanta, because Georgia Tech is working on chatbots as well, using a version of IBM’s Watson AI to facilitate online courses as a Teaching Assistant. By fielding administrative questions (when is my paper due, what sources can I cite, etc.) Jill Watson frees faculty to spend direct time engaging with students on things that impact learning (why did I get this question wrong, how do these two ideas merge, where can I add to research in this field, etc.).

Giving Teachers Time to Teach

Grading and assessment take up a good portion of teaching faculty’s time each semester. Assessment is a critical part of instruction – students can’t grow if their misconceptions aren’t identified and addressed. Feedback needs to happen quickly, particularly if material builds on itself throughout the course and it’s crucial that students master each block before they proceed. Offering timely feedback may be all well and good for 20 students, but imagine doing that for a 200-student survey course, or a 1,000-person MOOC. It’s easy for faculty to get buried.

AI can help. Gradescope, for example, can review assignments from all students and group similar answers so the professor only has to grade it and provide feedback once. Feedback is then disseminated to every student who answered the assignment in the same manner. For writers, M-Write (being developed at the University of Michigan) can read papers and highlight problem areas, allowing professors to target their attention, address issues at hand and return feedback in time for students to improve their next assignment.

Combining Humans and Machines

If AI can take over these tasks, are fewer people are needed to run a university? It’s possible, but not likely. Teaching at its core is a very human task – it requires relationship building, mentoring and the ability to inspire. The same is true for the advising and coaching our student affairs personnel undertake every day. We’ve worked with institutions for years, and we’ve yet to meet a department (academic or administrative) that isn’t already understaffed in some way. AI isn’t about smarter machines; it’s about smarter organizations. It’s collective intelligence, putting knowledge and people together to make a bigger impact than they would have made in their silo. We will be able to address student needs faster, provide more targeted feedback, share insights across departments to improve practices and use the time we save in the process to build more productive and lasting relationships with the people we’re teaching.

In our mind, that has been what education was always about, and AI will help everybody do that at scale.


Learn More

If you want to learn more about the current state of AI in higher education, and its potential impact on the future, register for our free webinar at 1 p.m. EST Thursday, Dec. 6.

About Justin Klutka

Justin is senior vice president of technology at The Learning House, Inc. He leads teams responsible for supporting technology-enabled marketing, enrollment and retention services. Throughout his career, Klutka has led digital transformation and analytics programs. He has a background in enterprise architecture and is currently researching artificial intelligence, machine learning and internet of things technology.

About Nathan Ackerly

Nathan is the curriculum program strategist at The Learning House, Inc. He advises partner institutions on many aspects of their academic initiatives, from new approaches to online programs and academic governance to regulatory and accreditation concerns and innovation in teaching. Prior to this role, Ackerly was an instructional designer for both Learning House and Bisk Education.

About Andrew J. Magda

Andrew leads in the development of custom and large-scale market research studies and assists partner institutions with their research needs. Prior to Learning House, he was a senior analyst at Eduventures and a project manager at the Center for Survey Research and Analysis at the University of Connecticut.