Artificial intelligence (AI) is creating exciting new opportunities for educators to engage students in the classroom, update learning activities, and build comprehensive learning experiences that develop 21st-century skills. For institutions, the rapid launch of various AI tools at scale is a double-edged sword. It creates a wealth of opportunities to improve and support learning, while also elevating the need for crafting a thoughtful policy approach that supports learning programs and ensures holistic, human-informed outcomes that fulfill educational objectives. For those just beginning to explore the pros and cons of AI in their programs, it is beneficial to learn more about how AI has already been utilized in higher education, to grasp the further opportunities available today, as AI has significantly evolved.
AI holds the promise to simplify processes, accelerate work, and automate routine tasks, freeing people for more meaningful, human-centric activities. However, this promise comes with a set of challenges that cannot be ignored. These include biases embedded in AI data sets, the potential for job displacement due to automation, and the complex issue of accountability—namely, who bears responsibility when AI-driven decisions go awry? The reality is that AI does expand the potential to exacerbate inequity. Several recent studies have highlighted how algorithms can further marginalize already underserved groups. Examples range from older women being denied government assistance in the Netherlands (Burgess et al., 2023) to patients receiving inadequate care because they are labeled “too poor” (US–EU Trade and Technology Council, 2022). It is critical that we be aware of how AI is used and be willing to critically evaluate how it is deployed, with an understanding of the real-world consequences of systemic biases in the data set used to build these technologies.
It’s not all doom and gloom. The quick deployment of generative tools and services over the past year has already made a significant impact on various aspects of our lives, and this trend shows no signs of slowing down. In education, AI offers exciting prospects. While much of the current discussion has focused on the latest advancements, there’s value in examining how higher education has been successfully harnessing AI for several years.
In fact, according to a 2019 survey by Microsoft that involved 509 universities in the US, a remarkable 99% indicated that AI would play a key role in helping their institutions stay competitive. Even four years ago, institutions were already considering strategic investments in artificial intelligence to improve student outcomes, support workforce development, prepare students for the future, and accelerate innovation (Jyoti and Sutherland, 2020). Insights from these prior AI successes can serve as a guide to identify current opportunities for leveraging AI and to showcase achievements that can offer valuable lessons to others.
AI Success Stories from Higher Education
Chatbots in Student Services for Success
In 2018, the University of Murcia in Spain implemented an AI chatbot to handle inquiries from new students. This chatbot engaged with over 4,000 students, fielded more than 13,000 questions, and resolved nearly 40,000 issues (Rouhianinen, 2019). Operating around the clock, it answered over 800 questions daily, the majority of which came in outside of standard working hours. Two key takeaways from this case study were the chatbot’s 92% accuracy rate in providing the correct answers and the fact that it did not lead to job losses for human workers. Rather, student support services were able to reclaim those hours and refocus on students’ more critical needs. These early innovations helped to focus the value of AI in institutional settings.
AI to Support Student Outcomes
In 2016, Lige Hensley, the chief technology officer at Ivy Tech, a community college in Indiana with multiple campuses serving 50,000 students, decided to leverage the 26 terabytes of data generated from their learning management systems (Google for Education, 2018). The aim? To transition to the use of educational data for predictive insights. As Hensley put it, education often uses data “backwards,” revealing only in hindsight what worked and what failed.
With a future focus in mind, Ivy Tech analyzed data from 10,000 course sections to develop algorithms that could assess their incoming students. They identified 16,000 students at risk of failing within just two weeks of the semester start. Armed with this information, Student Services was able to proactively reach out and connect with students and raise awareness of resources necessary to stay on track and succeed in learning programs.
For example, some students were unaware that Ivy Tech had a utilities program that could help students who lost electricity, while others simply needed information about how to retrieve essential course materials from the bookstore.
The result was the most significant percentage drop in D and F grades in the institution’s 50-year history. Rather than merely alerting to failures after the fact, the college managed to preempt them. This proactive approach didn’t just rescue grades; it also demonstrated how AI and data analytics could serve as powerful tools for academic support, potentially transforming student experiences and institutional effectiveness.
AI for Accessibility
The Rochester Institute of Technology took a significant step to make education more accessible by using AI-powered live captions. Serving a student body of over 15,000, nearly 1,000 of whom are deaf or hard of hearing, the school issued educators in science and engineering departments with headsets equipped with Microsoft’s early-stage automatic live captioning. The impact on students was profound. One student, Joseph Adjei, from Ghana, had lost his hearing as an adult and struggled with ASL. The introduction of real-time caption technology allowed him to meet learning goals and stay on track in courses (Microsoft, 2018). Further, real-time captions benefit everyone. A metanalysis of 100 empirical studies found that captions benefit all, improving reading skills, supporting comprehension, and reducing confusion for listeners (Gernsbacher, 2015).
AI to Support Language Development
Finally, it’s worth noting that some of us are already leveraging AI to bolster student success. For instance, Grammarly, an AI-powered writing assistant, is in use at over 3,000 schools to improve student writing, often among those for whom English is a second language. As AI continues to evolve, with sophisticated models like ChatGPT potentially changing the writing landscape, the foundational importance of good writing in both academics and the workforce endures.
AI and Future Skills
Studies indicate that people in knowledge-intensive roles like business analysts and data scientists are increasingly vulnerable to displacement by generative AI tools such as ChatGPT, Bard, and Claude (Kochhar, 2023). The implications for educational settings are becoming increasingly clear. The way we teach writing, for instance, will need a reevaluation, given that AI tools can now both generate and assess content. As AI becomes a core digital skill, the impact on teaching writing and other curriculum areas will be transformative.
This is more than a tech trend; it’s a global workforce shift. By 2030, technology will have altered 1.1 billion jobs worldwide, according to the World Economic Forum and OECD (OECD, 2021). Equipping our learners with both technical and human skills becomes essential for success in a technology-transformed landscape. So, what are the next steps? How can we leverage these technologies to the advantage of institutions, educators, and most importantly, students?
The Competitive Advantage of AI
For institutions that want to leverage AI for a competitive edge, start by thinking holistically. Consider the range of human skills that will still matter and how to integrate them into an AI-inclusive curriculum. The World Economic Forum’s Taxonomy of Education 4.0 (World Economic Forum, 2023) is a handy road map, detailing cognitive, social, physical, and self-regulatory skills. It encapsulates the multidimensional skill set our students will need in an AI-driven world, integrating and expanding on various skills and knowledge frameworks from around the globe.
Why does this matter? Dr. David Wiley of Lumen Learning puts it bluntly: students adept at leveraging AI tools like LLMs for their creativity and productivity will be in high demand. In contrast, those who’ve been restricted from such tools won’t have the same appeal (Wiley, 2023). In sum, the “how” and “why” of teaching and learning are undergoing radical transformation, and it is imperative to have a plan to stay ahead and implement successful solutions.
Preparing Your Institutions for AI
Institutions understand how important human skills are to the future; let’s look at some specific ways institutions can prepare to leverage AI technology today. There are a few steps that institutional leaders should review to ensure their programs are ready for AI and that educators and students have a clear idea of how the use of AI-driven tools and resources will be leveraged as part of the learning experience.
Using a five-point framework, institutional leaders can be proactive in how AI tools are utilized, consistently and with a focus on improving quality of learning experiences and supporting the overall efficacy of institutional offerings.
This five-point framework revolves around vision, people, process, technology, and data readiness. Each is crucial for an institution’s successful implementation and scaling of AI initiatives. Starting with a clear vision, institutions should inventory the skills available among staff, identify processes ripe for AI-driven improvement, assess existing technology, and determine the level of data readiness. Addressing these pillars methodically sets the groundwork for an AI transition that elevates both learning experiences and institutional capabilities.
The following questions can be used to analyze the current preparedness of institutions to work with AI successfully and help inform discussion for creating AI policies to support institutional success:
Vision – What is your strategy for AI? How will this impact your culture and help you achieve ROI appropriate for your business?
People – What skills do you have? What will you need? How will human/AI collaboration impact your organization’s structure?
Process – What processes can be improved or revamped? How will improvements be governed? How will you measure impact?
Technology – Where can AI be operationalized? How will those models be built/acquired? Who will be responsible?
Data readiness – What data do you need to prepare? How will you guard against bias? What practices will you employ to reduce risks (e.g., privacy, security)?
Institutions must outline a clear AI strategy, identify existing skills and gaps, consider process improvements, assess technology infrastructure, and ensure data readiness. Careful policy planning in these areas sets the stage for positive impacts in multiple strategic domains, from student recruitment to learning experiences and beyond. It’s worth noting that while AI has vast potential to reshape the educational landscape, it will never replace the uniquely human capacities for imagination, creativity, critical thinking, and interpersonal interaction.
AI can be a tool for change and opportunity, but it serves to augment, not replace, the essential elements of human ingenuity and inspiration.
AI and the Future of Learning
The integration of AI into higher education is a complex undertaking that presents a range of ethical, technical, and strategic challenges for educators and policymakers. While AI has the potential to significantly impact higher education—from reducing administrative burdens through chatbots to providing real-time support for diverse student needs—it is not without its complications. Careful planning and consideration are essential for its successful adoption and deployment.
We are in an exciting and critical moment for institutional leaders to define their AI strategies, considering the needs of their people, the readiness of their technology infrastructure, and the availability of data. By doing so, they can ensure that AI is used strategically to enhance learning experiences without compromising educational outcomes. Four years ago, higher education institutions were already recognizing the potential of AI to support students. Now, in this pivotal moment for AI implementation, the opportunity to foster a dynamic, future-oriented learning environment is within everyone’s reach.
References
Burgess, M., Schot, E., and Geiger, G. (2023). “This Algorithm Could Ruin Your Life.” Wired. www.wired.co.uk/article/welfare-algorithms-discrimination
Gernsbacher, M. (2015). “Video Captions Benefit Everyone.” Policy Insights from the Behavioral and Brain Sciences, 195–202. doi:10.1177/2372732215602130
Google for Education (2018). “Ivy Tech Develops Machine Learning Algorithm to Identify At-Risk Students and Provide Early Intervention.” https://edu.google.com/why-google/customer-stories/ivytech-gcp
Jyoti, R., and Sutherland, H. (2020). Future-Ready Institutions. IDC.
Microsoft (2018). “AI Technology Helps Students Who Are Deaf Learn.” The AI Blog. https://blogs.microsoft.com/ai/ai-powered-captioning
OECD (2021). “Artificial Intelligence and Employment.” OECD. www.oecd.org/future-of-work/reports-and-data/AI-Employment-brief-2021.pdf
Rouhianinen, L. (2019). Artificial Intelligence: 1010 Things You Must Know Today about Our Future. Amazon.
Shiohira, K. (2021). Understanding the Impact of Artificial Intelligence on Skills Development. UNESCO-UNEVOC.
US–EU Trade and Technology Council. (2022). The Impact of Artificial Intelligence on the Future of Workforces in the European Union and the United States of America. The White House. www.whitehouse.gov/wp-content/uploads/2022/12/TTC-EC-CEA-AI-Report-12052022-1.pdf
Wiley, D. (2023). “What the Past Can Teach Us about the Future of AI and Education.” Campus Technology. https://campustechnology.com/articles/2023/04/06/what-the-past-can-teach-us-about-the-future-of-ai-and-education.aspx?s=ct_pulse_120423&oly_enc_id=1438A5729301G3V
Cann, O. (2020). “The Reskilling Revolution: Better skills, better jobs, better education for a billion people by 2030.” World Economic Forum. www.weforum.org/press/2020/01/the-reskilling-revolution-better-skills-better-jobs-better-education-for-a-billion-people-by-2030
Based in Chicago, Sara Davila is the ESL research and assessment policy analyst for IELTS USA. She focuses on the intersection of pedagogy, assessment, and emerging technologies to enhance language acquisition. The British Council recognized her contributions by nominating her for an ELTons in Digital Innovation in 2021, for her work in supporting the launch of the world’s first real-time interactive virtual reality language-learning application. Sara continues to contribute to the field through her website at saradavila.com.
