John Runte is a principal and practice leader of enterprise transformation and digital solutions at Baker Tilly. John’s experience includes extensive work encompassing complex project management, process redesign, new product development, information system strategic planning, information system design and implementation with a focus on program management of large complex transformation projects, application integration solutions and digital solutions focused on advanced analytics and intelligent automation.
While academics can debate the merits of allowing the use of AI in their courses, higher education institutions need to fix their focus on what digital transformation can do for their future. Artificial intelligence (AI) and predictive analytics are already reshaping higher ed by fostering more adaptive, efficient and student-centered campuses.
Capabilities of AI and Predictive Analytics
By their very nature, AI and predictive analytics play different roles in shaping higher ed recruitment, retention, workflows, and university operations. Predictive analytics looks at historical data to forecast future events. It uses statistical algorithms and machine learning to identify patterns and predict outcomes. In contrast, AI encompasses a broader range of capabilities that simulate human intelligence processes with machines, particularly computer systems. AI processes include learning and reasoning, along with the automation of tasks that typically require human intelligence.
Predictive Analytics in Student Retention and Enrollment Management
In higher ed, predictive analytics is already making significant strides in student retention and enrollment management. By analyzing past student behavior and performance data, institutions can identify students at risk of dropping out or under-performing and intervene proactively with targeted support services. This data-driven approach can help colleges and universities tailor their resources more effectively, ensuring students receive the assistance they need to succeed.
Using predictive analytics, institutions can identify students who are at risk of losing interest or becoming disengaged. For example, analytics can identify a student who has missed class for several weeks, allowing the institution to take immediate action to ensure the student remains involved. Georgia State University has used predictive analytics to analyze data about student demographics, academic performance, and engagement. The university’s early warning tracking system helps identify struggling students leading to timely interventions to get back on track for success in their college courses.
Predictive analytics also plays a pivotal role in enrollment management. By focusing trends on student demographics, skill development needs and educational preferences, institutions can adapt their recruitment strategies to meet changing demands, ensuring a diverse and well-balanced student body. This foresight allows for better resource allocation, from financial aid distribution to campus facilities planning, ensuring universities remain agile and responsive to evolving student needs.
In the future, predictive analytics can also be used to help universities align their academic offerings with anticipated workforce needs in a geographic area, thus providing a pipeline of qualified graduates to the under-staffed industry sectors in the same region where the institution resides.
Potential Applications of AI
The potential applications of AI in higher ed are almost endless. AI-driven chatbots and virtual assistants are already enhancing student support services by providing immediate, 24/7 assistance for a range of inquiries from prospective students. They can answer questions and provide information about programs, admission requirements and campus life.
Many universities are already using some form of AI during the admissions process. The technology allows schools to sift through large data sets, evaluating thousands of applications more efficiently. Colleges can also subscribe to AI-related services that facilitate expedient review of transcripts, extracting exact course, grades, and credits in seconds without error. This, in turn, theoretically frees up admissions staff time, allowing them more time to consider other aspects of submitted material.
Future uses of AI might include more detailed analysis of transcripts. For example, if schools automatically upload transcripts, trained AI may be able to analyze information from them, identify student strengths by grades in specific disciplines, and share that specific information with individual colleges within the university. Because it’s AI driven, there will be a reduction in human error. The key is training the AI to locate and populate the appropriate fields, helping aid in the decision-making process.
AI can be used to streamline workflow in other areas, allowing staff to focus more on complex nuanced tasks. For example, AI can optimize resource allocation across campuses, enhance the efficiency of administration processes, and provide strategic insights based on data analysis, from financial planning to facility management. From the faculty perspective, AI can help automate repetitive tasks, freeing up scholars’ time for more meaningful, impactful research. AI can accelerate data analysis, enabling researchers to work with large datasets more efficiently.
From the student perspective, AI holds the promise of personalizing the higher education journey in unprecedented ways. AI could help customize learning pathways and tailor career counseling by analyzing student data to provide recommendations that align with their strengths, interests, career aspirations and workforce development needs. This, in turn, could help students hone their interests early on in their academic careers, improving on-time graduation rates in areas that are truly meaningful to the students.
Privacy, Security and Ethical Concerns
Higher Ed’s integration of AI and predictive analytics is not without its challenges. Privacy, security, and ethical concerns necessitate transparent and responsible use of data. One of the most significant concerns is the possibility of biased decision-making in AI models. As higher ed institutions integrate AI into their admissions process, they will need to implement procedures or checkpoints to ensure AI isn’t delivering biased outcomes. This includes periodically auditing the algorithms embedded in AI solutions.
Student privacy and data security are also important considerations. AI has the potential to infer sensitive information such as a student’s location, preferences, online activities, and habits. This data can be used to make predictions about students’ future behaviors and outcomes. Some would argue it infringes on students’ privacy rights and autonomy. And security of these vast amounts of data will continue to be paramount. More than ever, higher ed institutions will need to implement robust cybersecurity systems, plan for auditing compliance, and enforce consequences for non-compliance.
As higher ed institutions continue to navigate the complexities of AI and predictive analytics, the implementation of ethical standards will help ensure the adoption of these technologies enhances the educational experience without compromising individual rights and equity.
By harnessing the power of these technologies, universities can create a more adaptive, personalized, and efficient institution. Moving forward, the fusion of AI and predictive analytics holds the promise of unlocking new horizons in higher ed.