Artificial intelligence is emerging as one of the most powerful agents of change in higher education, presenting the sector with unprecedented academic, ethical and legal challenges. Through its algorithmic ability to adapt, self-correct and learn, AI is pushing the boundaries of human intelligence, making the future of higher education inextricably intertwined with AI.
To disentangle the intertwined relationship between AI and higher education, I will briefly discuss the opportunities and the challenges of AI, review some of the emerging applications of AI in higher education, and offer some recommendations for the way forward.
AI Is Extending Human Ability to Solve Complex Problems
As an umbrella term that includes machine learning, deep learning and natural language processing, AI relies on extensive computing power and massive amounts of data processed by algorithms. As it continues to seep into the fabric of our society, AI is being used to solve problems in cybersecurity, health care, agriculture, climate change, manufacturing, banking and fraud detection, among other areas. By using computing power to process and to learn from data, AI is augmenting our ability to automate routine tasks; to streamline processes; to recognize and classify images, patterns and speech; to make predictions; and even to make decisions.
Not surprisingly, some AI enthusiasts are predicting the emergence of a super AI intelligence capable of rivaling, if not surpassing, human intelligence. As AI capabilities grow, debates about the moral and ethical implications of its use are likely to accelerate. For example, AI-powered facial and emotion recognition technology often exhibits bias against nonwhite people and women and can be weaponized for mass surveillance. Currently, however, as part of an emerging trend of responsible AI practices, researchers are attempting to address issues of bias and discrimination by improving both the algorithmic transparency and the data that feed AI models.
How Is AI Reshaping Some of the Routine Activities of Higher Education?
AI is quietly disrupting higher education’s administrative, teaching, learning and research activities. Here are a few examples:
- Administrative support: AI tools are being used to crunch data on recruitment, admission and retention, to aid in decision-making processes and to assess productivity and performance.
- Teaching support: AI tools are being used to provide adaptive and automated assessments, practice opportunities, personalized tutoring and feedback and content recommendations. In addition, AI tools are being used to generate content, write code, resolve accessibility issues, reconfigure writing processes and detect plagiarism.
- Learning support: AI tools are used to provide self-service chat bots, flag at-risk students, recommend courses, increase motivation and predict student performance.
- Research support: AI tools are being used to sift through large data sets to identify patterns, build models, recommend relevant articles and prepare manuscripts for publication.
Together, these transformative processes have the potential to redefine and reduce the number of positions in areas such as admissions, administrative support, instructional design, teaching and information technology support. With the continued advancement of AI-generated multimodal content, as demonstrated by the recent release of ChatGPT-4, the efficiency and productivity of these areas will continue to improve.
Although it’s still prone to inaccuracies and sometimes fabrications, ChatGPT, developed by the OpenAI research lab, is now being used to write articles, novels, poems, stories, dialogues, reviews and news reports; to develop web applications; to write programming code; and even to author an academic paper about itself. Educators use ChatGPT to draft course syllabi, lecture content, assignments and grading rubrics. With this ability to produce academic writing, many fear that ChatGPT is poised to disrupt academic scholarship, even though AI is still far from replicating the complex metacognitive activities involved in the scholarly writing process.
Where Do We Go from Here?
Paving the road ahead for the future of AI in higher education requires a strategic and holistic approach that integrates education, planning and research.
First, institutions need to engage in campuswide discussions about the impact of AI on administrative, teaching and research practices. It is crucial to develop administrators’ and faculty members’ understanding of the promises and limitations of AI. These discussions must be transparent and address issues like data collection and ownership, intellectual property, data storage, security, and the rights and privacy of various stakeholders. Additionally, institutions must create a framework for the ethical governance of AI, such as the Rome Call for AI Ethics and the Data Ethics Decision Aid. These frameworks will help institutions use AI to advance teaching, learning and research, while preventing the misuse and unintended consequences of AI.
Furthermore, higher education institutions must carefully assess how AI will affect the labor market in the future. This analysis should lead to a rethinking of educational pathways to prepare students for a hybrid labor market in which AI will play a significant role.
Second, stakeholders ought to explore an AI-across-the-curriculum approach by engaging departments and their faculty development centers to identify ways to integrate current AI applications and competencies into the curriculum. AI competencies should be transdisciplinary, reflecting the various areas of expertise involved in AI development: mathematics, machine learning, deep learning, programming, data science, writing, ethics, business management, etc. This transdisciplinary approach would allow universities to lay the groundwork for a holistic and integrated approach to AI education, while fostering collaboration and partnership between faculty from different disciplines.
Third, universities should consider establishing an interdisciplinary longitudinal research agenda to examine the social, ethical and pedagogical challenges associated with AI, making sure to include experts in the humanities and social sciences in these discussions. It is imperative that universities take the lead in identifying and understanding the complexities and challenges that AI will bring to the academic landscape. Moreover, universities should collaborate with industry and the public sector to create integrated, transparent and impartial AI programs while equipping students with lifelong learning skills to make our soon-to-be AI-driven society both better and more just.