The Rising Tide of GenAI in Higher Education

An opportunity for higher education to lead

Generative artificial intelligence (GenAI) is accelerating transformation across industries, and higher education is no exception. 

While the application of GenAI in higher education was initially met with skepticism, academic and technical stakeholders are recognizing the technology's power to enhance learning experiences, drive student outcomes, optimize administrative processes and facilitate research.

Traditionally, higher education has lagged other industries in embracing transformative technologies. However, we believe that GenAI's potential impact on higher education is so great that colleges and universities have a unique opportunity to take a leadership role in GenAI adoption.

Expected outcomes from GenAI in higher education 

We anticipate that GenAI integration can bring about significant academic and operational improvements to higher-education environments. Broadly speaking, we see GenAI driving outcomes such as:

• Personalized learning: GenAI can tailor educational experiences to serve individual student needs, strengths, modes and learning paces. By analyzing data from student interactions with learning platforms, GenAI can provide customized content and recommendations, fostering a more effective and engaging learning experience.
• Personalized student support: GenAI can compile relevant details about individual academic and life circumstances, making it easier for student services staff to provide compelling and effective support for each student. AI-powered chatbots and virtual assistants can provide 24/7 support by answering student questions, providing guidance, and even offering mental health resources. This can significantly improve student success and outcomes — all at a scale not previously possible.
• Advanced research capabilities: We expect GenAI tools to accelerate the pace of discoveries and innovations in academic research and scholarship by analyzing vast amounts of data, identifying patterns, and providing insights faster than human researchers or insights that may otherwise have been missed entirely by humans.
• Administrative efficiency: GenAI can streamline repetitive administrative tasks, thus reducing the amount of manual work demanded of faculty and staff, such as automating admissions processes, managing student records and handling routine inquiries. This can free faculty to focus on tasks typically done outside of traditional work hours or that require a deeper level of critical thinking.

GenAI use cases for higher education

The institutional potential to take advantage of GenAI's value is immense. This is particularly evident as more compelling use cases emerge across a myriad of higher-ed departments and processes, as illustrated below.

To help institutional leaders get started, here are some of GenAI's more popular higher education use cases:

• Intelligent tutoring systems: These systems use GenAI to provide personalized tutoring to students. They can adapt to individual learning styles and paces, offering custom exercises and feedback. Examples include platforms like Carnegie Learning and DreamBox.
• Automated student assessments: AI-powered grading systems can evaluate assignments and exams, providing quick and consistent feedback. This not only saves time for instructors but also ensures unbiased grading. For example, tools like Gradescope use GenAI to assist in grading complex assignments.
• Predictive analytics: Universities and colleges can use GenAI to predict student outcomes and identify those at risk of dropping out. By analyzing data such as attendance, grades and engagement, GenAI can flag early warning signs, allowing institutions to intervene and offer support earlier.
• Administrative chatbots: AI-driven chatbots, like Georgia State University's Pounce, help manage student inquiries and administrative tasks. These chatbots can handle a wide range of questions, from financial aid to course registration, in a way that provides instant support to students.
• Research assistance: GenAI tools like IBM Watson are being used to assist researchers in literature reviews, data analysis and even hypothesis generation. These tools can process and analyze large datasets far more quickly than traditional methods.

Intentionally identifying and prioritizing GenAI use cases enables institutional leaders to create a cohesive portfolio of work that informs design efforts, implementation planning, and implementation overall. This portfolio will help maximize value for institutional stakeholders and drive organizational synergy by producing higher-value outcomes, reducing organizational risk, and supporting more effective change management for all constituents being impacted.

Practical AI: The secret to GenAI implementation  

Higher education institutions can find it overwhelming to decide when and how to implement GenAI use cases. Practical AI offers a well-defined path toward implementation, falling in the sweet spot between implementation extremes.

If we imagine the range of AI solutions in the market as a bell curve, we'll find off-the-shelf (OTS) solutions on the left that can be integrated in a standalone manner, contrasted with hyper-custom AI solutions, on the right, that require significant R&D investment to build.

Practical AI's purposefully balanced approach prioritizes targeted AI solutions that deliver fast outcomes while accounting for the viability, maturity and scalability of long-term AI and data strategies. In practice, practical AI entails determining when it's in your best interest to buy and integrate an OTS AI solution and when it's smarter to invest in customizing and training your own model. There are short- and long-term impacts following whichever approach that institutional leaders choose.

Steps stakeholders can take to ensure the successful adoption of practical AI include:

1. Upfront goal setting and identification of business outcomes.
2. Assessing current capabilities and deficiencies across a range of related technologies.
3. Modernizing IT infrastructure to ensure reliability, handle growing data demands and enhance scalability.
4. Ensure there's enough visibility to accurately forecast an organization's data demands.

It's also important to always remember that the efficacy of practical AI is tied to the maturity and alignment of an institution's AI and data strategies. 

Concerns and risks of GenAI in higher education

Despite its many benefits, the integration of AI into higher education raises several risks that institutional leadership should be prepared to address. These include:

• Data privacy: The use of AI often involves collecting and analyzing vast amounts of sensitive data about students, staff and their associated actions. This raises significant privacy concerns as sensitive information could be targeted and misused by bad actors in a breach. Institutions must ensure they have robust data protection measures in place.
• Bias and fairness: AI systems can inadvertently perpetuate biases that inherently exist in training data. This can lead to unfair treatment of certain groups of students. Ensuring that AI systems are transparent and fair is crucial to prevent discrimination.
• Job displacement: The automation of administrative tasks, and even some teaching roles, could lead to job losses among faculty and staff. While AI can handle repetitive tasks, it is important to strike a balance to ensure that human jobs are not unduly threatened.
• Dependency on technology: Over-reliance on AI could reduce human involvement in key educational processes, potentially leading to a loss of critical thinking and judgment. Maintaining a human-first touch in education is essential to fostering holistic development.
• Cost and accessibility: Implementing AI technologies can be expensive, and not all institutions may have the resources to adopt these tools. This disparity could further widen the gap between well-funded and underfunded institutions, exacerbating existing educational inequalities.

Conclusion

AI is poised to revolutionize higher education, promising numerous benefits in terms of personalized learning, administrative efficiency, student support, and accelerated research. However, it also presents significant challenges related to privacy, bias, job displacement, technology dependence and cost.

By adopting ethical practices that involve key stakeholders, provide training, promote collaboration, and ensure accessibility, higher education institutions can navigate these challenges and fully realize the transformative potential of modern AI solutions.  As the landscape of education continues to evolve, the careful and thoughtful integration of AI will be key to fostering an innovative, equitable, and effective educational environment.