A Look at the Good and Bad of AI in Higher Education with Special Considerations for Health Professions Programs

Lowell Sanders, PharmD, MEd, CAHIMS is a Clinical Associate Professor, Health Informatics; and Stephanie Scales, MSHS is a Clinical Instructor, Health Sciences at Byrdine F. Lewis College of Nursing and Health Professions, Georgia State University; a HIMSS Individual Organizational Affiliate Member

Take a moment and imagine a group of graduate students in a research lab with their eyes glued to their laptop screens. Whereas some of them may be shopping online or booking trips to the Florida panhandle for spring break, a few appear to be doing actual work, or at least pretending to. It isn’t long, though, before the attention of everyone in the room gravitates toward one classmate who launched a special program designed to crunch numbers and dissect data, all within a matter of seconds. After a few more clicks on her keyboard, the software converts the newly analyzed data into visual representations that she will use to steer her research project on methods to improve outcomes for inner-city patients with chronic disease states like hypertension and diabetes.

Although hypothetical, this scenario serves as a reminder of the contributions artificial intelligence (AI) has introduced to higher education, particularly in health professions disciplines and health informatics. And as AI becomes further accepted and integrated over time, it will more than likely change the ways teachers teach and students learn.

AI in Higher Ed: The Here and Now

In simple terms, AI is technology that enables machines to imitate human intelligence and perform tasks like decision-making and problem-solving, which gives it the capacity to impact higher education in a lot of ways. In fact, the AI market in education is expected to exceed $20 billion by 2027, according to Global Market Insights, Inc. This reflects a trend wherein institutions have already embraced AI or are at least contemplating its use.

According to a report on the transformative abilities of AI, 73% of educators feel optimistic that it can positively influence student learning outcomes. Other data not only suggest the same but also indicate that most believe AI will have a positive effect on higher education in general. It must be noted, however, that resistance to adopt AI is very real, and it stems from equally real concerns about a lack of resources for supporting educators as they try to utilize AI in the classroom, not to mention potential negative consequences that may result from it.

While there are suggestions for incorporating AI into medical and health informatics curricula, it is not part of most programs since prevailing accreditation standards neither require AI training nor define specific skills and competencies for students to master.

A Little Something for Everyone

From a student’s perspective, what’s not to like about AI? It makes their academic journey easier than those who endured collegiate work in years past, giving today’s students the advantage of content creation when faced with the drudgery of a research paper or interpretive essay. Likewise, there are benefits for faculty members and university administrators as well.

Faculty and Administration

At the top of the list is AI-driven predictive analytics, a method that helps identify students at risk of academic challenges early on and provides timely assistance to increase their chances of success. By analyzing student performance data like course engagement, these early-warning systems are able to pinpoint individuals who need extra support and tutoring. This proactive approach enables educators to step in before academic issues worsen, which, in turn, helps improve retention rates that would otherwise result in sleepless nights and stress-induced peptic ulcers for some administrators. Similar research even indicates that institutions using predictive analytics often see an increase in their graduation rates.

In addition to AI’s role in student support and attrition, it also functions to enhance the efficiency of tasks related to admissions processes and enrollment management. After all, evaluating hundreds or thousands of prospective students’ credentials is a mundane and time-consuming job. AI, however, can automate the process to not only save time but also improve overall operational effectiveness within this particular division of a university.

Students

In the same manner that AI assists with administrative tasks, it can be used to tailor educational experiences to the individual needs and preferences of students in order to boost engagement and improve educational outcomes. This concept of personalized learning uses adaptive AI systems to assess and analyze response times, accuracy, error patterns, and levels of engagement, all while taking into account each student’s challenges and strengths in an effort to serve up customized content and feedback . Correspondingly, these adaptive systems are designed to augment the learning process by recommending relevant resources and adjusting coursework complexity based on student comprehension.

Of equal importance is simulation technology, which has come a long way since its inception to become an integral part of many programs of study. AI-driven virtual simulations have essentially revolutionized the way medical students learn by letting them practice patient scenarios and refine their decision-making skills without real-world consequences they might otherwise encounter in an actual clinical setting. Of a similar nature are interactive chatbots designed to answer human queries. In higher education, these AI-powered platforms predominantly act as virtual teaching assistants to aid students with questions and suggest resources to help them further explore topics and learn.

With so much hype, what’s not to like?

Despite the many upsides of AI in higher education, there are numerous issues and ethical factors to consider prior to and after implementation. At the top of the list is data privacy, which should not come as a surprise considering the increasing number of data breaches being reported by the media. When it comes to AI in higher education, it is only by analyzing large datasets based on personal identifiers, academic performance metrics, and behavioral data that AI tools can recognize student learning patterns and tailor educational experiences to their individual needs. This leads to questions about the protection of data collected by AI as well as unauthorized use of the data. As it stands, data privacy and cybersecurity professionals as well as those working in higher education leadership consider data security to be a major hindrance to adopting AI technology, which only serves to prompt further questions about data reliability. After all, the accuracy of AI largely depends on the quality and diversity of data used to develop it. In higher education, biases in the data can consequentially skew outcomes in central areas like student assessment and enrollment decision-making, which is why it is essential that steps be taken to maintain transparency by revealing how AI is being used and addressing any biases that come to light. Furthermore, medical and health informatics programs should make an effort to integrate data analytics and AI concepts into the curricula to train students how to develop bias-free AI algorithms and tools.

Adding to the list of concerns are issues about the adoption and integration of AI technology into an established university framework, which requires senior administrators to commit to necessary infrastructure upgrades and ongoing support services for educators. Seeing as how there is already a reluctance toward change due to worries over AI technology and misunderstandings about its uses, some faculty tend to look the other way and disregard the advantages of adopting AI tools. By the same token, AI automation often conjures fears that it will disrupt or even replace traditional academic roles, further substantiating the need for specialized training for faculty and staff to understand the advantages of AI and effectively utilize it, both inside the classroom as well as behind the scenes for course preparation and non-pedagogical tasks. Above all, university leadership must emphasize that the role of AI is to supplement rather than replace educators and other academic professionals.

Outside of academia, it is important to note that AI automation of manual tasks and processes may call for specialized training to equip health information professionals with the necessary technical expertise to effectively manage AI tools.

Ready or not, AI is Here

The future of AI in higher education is full of possibilities that will likely bring about new teaching methods and practices capable of improving outcomes for students and educators alike. To take advantage of what AI has to offer while also addressing its challenges, higher education leadership should consider multiple strategies, including, but not limited to, the following: 

• Support faculty through training and professional development to boost their skill level and confidence for incorporating AI into their teaching practices. Enabling them with the know-how to customize and potentially even create AI resources will streamline routine tasks and ultimately free up more time for meaningful interactions with their students.
• Establish specific rules and ethical standards for the development and application of AI technologies so as to promote transparency and accountability within both academic and administrative functions.
• Encourage AI developers and educators to team up to create AI solutions that adequately tackle some of the unique challenges faced by students and teachers. The same goes for those in other university divisions like admissions, research, and scheduling, all of which can utilize the power of AI to streamline time-consuming processes.
• Continuously assess the impact of AI on teaching effectiveness, student achievement, and overall operational efficiency to ensure that the use of AI tools adequately aligns with and supports educational goals.