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It is indisputable that AI will transform the world of education; indeed, it already has. Like many technological advancements before it, generative AI may either be utilized positively or negatively in academia. AI presents significant oppo unities, but also challenges and even threats. Institutions that work proactively to embrace the challenges and navigate the threats will be in the best position to continue to thrive. As it stands, guidance is necessary – at federal, state, and institutional levels – to confirm AI works for, rather than against, educational purposes, and education-sector organizations should consider developing internal policies sooner rather than later.

To provide just a couple of many examples, we describe below the risks and benefits of AI to two core academic functions: admissions and instruction.
  

Admissions
  

AI has powerful potential to support colleges and universities in their admissions processes, enhancing both the efficiency and fairness of applicant evaluation. Many schools are already utilizing AI to perform perfunctory tasks – like automating GPA re-calculation – saving admissions teams thousands of hours of quantitative labor. Schools can also use AI for more subjective tasks, such as summarizing essays and recommendation letters to identify personality traits and soft skills. And by analyzing a broader set of data points, schools can look beyond numbers and more comprehensively predict an applicant’s likelihood of success. But AI’s ability to create a more holistic admissions process requires thoughtful implementation. Because AI models are typically trained on historical data, it is possible for the outputs of those models to reflect biases present in the historical training data. And while it is impossible to remove bias entirely from the admissions process, thoughtless or unchecked reliance on AI models opens institutions to legal and ethical risk. As AI companies self-regulate to minimize this bias, internal policies to understand how AI tools operate, including with respect to bias, and to confirm that AI is used to complement, rather than replace, human judgment in the admissions process may be warranted.
  

Instruction
  

AI also has powerful potential to support educators and students inside the classroom. For example, many AI platforms generate questions that are responsive to students’ individual needs and performance, thus improving the quality of academic assessment. But many stakeholders fear the consequences of student dependence on this technological advancement and the potential impacts of AI tools on assessment integrity. As institutions determine whether and to what extent generative AI tools can be used in the classroom, zero-tolerance policies that provide little guidance on practical ethical usage may not be the appropriate approach. AI is here to stay, and will be an increasingly important tool in almost every industry sector; thus, it is critical to train students to ethically maximize its utility. Rather than resist this changing landscape, institutions should deploy responsive policies that encourage responsible integration of AI in instruction and learning consistent with all applicable privacy, accreditation, and academic integrity requirements. 

Given AI’s transformative influence in the world of education, it is imperative for education professionals to pay close attention to how this evolving technology will shape the broader industry. Institutions that seem likely to win the future will find ways to harness AI to drive forward their missions without creating legal liability.

Melody Zargari and Greg Kimak contributed to this trend.