Challenges
Scaling and integrating AI technologies in higher education is not without its challenges. One significant barrier is a process called shared governance where faculty have
significant input and control over academic decisions and curricular changes. While this governance model is critical for maintaining academic integrity and quality, it can also lead to resistance against changes perceived as threatening to traditional teaching methods or academic autonomy.
Oftentimes, fear of change leads to little more than colleges and universities adopting general policy statements or guidelines that leave the decision about the use of AI to individual faculty members or administrative staff. Other times, colleges and universities engage in extensive consultations and pilot programs before initiating action – as was the case with the University of California, Berkeley, before it gained faculty support for AI-driven grading systems. Ensuring that emerging AI initiatives align with the values and priorities of institutional faculty is crucial for this technology’s success on college and university campuses.
Equally, there is no doubt developing and managing AI tools in higher education require skilled professionals with substantial intellectual capital in data science, machine learning, and information technology who can make judicious changes quickly. As notoriously slow-to-change entities, many colleges and universities often face a shortage of these experts, which ultimately hinders the effective deployment and utilization of AI technologies. Implementing new technologies often requires navigating layers of administration and securing buy-in from various stakeholders (faculty, staff, students, and alumni).
Moreover, implementing and maintaining AI systems is above all extremely expensive, requiring substantial investments in software, hardware, and ongoing technical support. Georgia State University’s implementation of the Pounce chatbot cost approximately $500,000 in its initial phase, with ongoing costs for updates and maintenance. Similarly, Arizona State University has invested close to $3 million in adaptive learning platforms over several years, which includes licensing fees, infrastructure, and training. Institutional funding approvals and budget allocations can also be lengthy and challenging processes that are dependent upon uncertain labor market conditions, changing political forces, and fluctuating enrollment trends.
Some institutions have started to address these issues by heavily investing in data science programs and interdisciplinary data centers to support the growth of AI on their campuses and develop a pipeline of innovation, but these are largely renowned research universities or well-endowed institutions who are capable of such growth. The University of Virginia is currently working with the largest gift in school history, a $120 million grant, to launch an interdisciplinary school of data science. MIT announced the development of a $1 billion computing college in 2018 integrating data science, humanities, and social sciences to
By embracing AI as a tool to enhance all our human capabilities and focusing on thoughtful integration instead of human replacement, colleges and universities should exchange skepticism and fear with optimism and hope.
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