In the fast-evolving landscape of higher education, institutions are constantly seeking innovative ways to streamline processes and enhance student engagement.

One such institution leading the charge is the School of the Art Institute of Chicago (SAIC). Leveraging the power of machine learning, SAIC has embarked on a transformative journey to revolutionize its admissions process, with a focus on boosting student engagement and directing resources more effectively.

A Vision for Change

In 2019, Kyle O'Connell, director of enrollment analytics and forecasting at SAIC, envisioned a future where technology could be harnessed to strengthen in-person relationships with students. This vision led to the creation of a machine learning system to improve the admissions process, with the aim of focusing the institution's efforts on students more effectively. O'Connell emphasized the goal of using technology to increase personal interactions with students who can benefit most, noting that there's more information about students than can be assessed by an individual.

The Evolution of Machine Learning at SAIC

Despite initial challenges, including data limitations, O'Connell and his team persevered, refining their data-gathering process over the next few years. Their efforts coincided with an opportunity to collaborate with the Chicago technology consulting firm SPR, which specializes in machine learning models. SPR's mission aligned perfectly with SAIC's goal of enhancing student engagement and community impact.

In early 2023, SPR invited organizations to submit pitches on how to better the local community, with the winner receiving $50,000 in honor of the company's 50th anniversary. SAIC's pitch stood out among the diverse range of submissions, ultimately winning the grant and paving the way for a groundbreaking partnership.

Enhancing Student Engagement through Data

The machine learning model developed in collaboration with SPR marks a significant milestone for SAIC. By analyzing stacks of data from applicants who received offers, the model considers over 100 factors, including the number of SAIC events attended by applicants, their program preferences, and high school background. The model generates two key outcomes: the likelihood of a student accepting the admissions offer and the likelihood of the student actually attending the university.

This data-driven approach allows SAIC to gain valuable insights into student behavior, enabling the institution to tailor its engagement strategies accordingly. O'Connell emphasized that the technology is not used to dictate which students should be accepted, but rather to illuminate the likelihood of accepted students choosing to attend SAIC.

Future Implications and Beyond

While the full impact of the machine learning model is yet to be realized, O'Connell and Steven Devoe, SPR's data specialty director, are optimistic about its potential. Devoe believes that the model could lead to budget and time savings for SAIC, allowing the institution to allocate resources more effectively.

For example, if the model indicates that students from a specific country are unlikely to accept offers from SAIC, the institution may adjust its marketing strategies accordingly. Additionally, the model helps in planning for class sizes and sections, providing SAIC with a more accurate outlook on student enrollment.

Devoe explained that the focus was on increasing access to higher education for more students, improving institutional planning, and potentially using funds more efficiently.

Embracing Technology in Higher Education

SAIC's adoption of machine learning in admissions reflects a broader trend in higher education, where institutions are increasingly turning to technology to streamline processes and improve outcomes. According to a survey by Intelligent magazine, more than half of universities are now using AI in their admissions process, with the number expected to rise significantly.

While some may have concerns about the role of technology in admissions, SAIC's approach demonstrates a commitment to leveraging technology to enhance student engagement and support academic success. As Rick Dakan, chair of the AI Task Force at the Ringling College of Art and Design, noted, technology is becoming an essential part of preparing students for the future job market.

Dakan mentioned that even though some illustration faculty dislike the change and prefer traditional methods, they acknowledge the importance of students learning modern technologies for their future careers.

SAIC's innovative use of machine learning in admissions marks a significant step forward in enhancing student engagement and improving outcomes. By leveraging technology to gain deeper insights into student behavior, SAIC is setting a new standard for admissions processes in higher education. As the institution continues to refine its approach, the impact of this technology-driven initiative is expected to be felt for years to come, shaping the future of admissions at SAIC and beyond.