This story is from The Hill's Changing America publication. The job of a college admissions officer is not an easy one. For any competitive higher learning institution, the admissions process used to hand-pick each incoming student has also come under increasing scrutiny in recent years. To ensure the ongoing success of an institution, admissions officers are tasked with the nearly impossible task of efficiently evaluating thousands of applications each school year, with the expectation that their choices will reflect the institution's standards, grow diversity and inspire enough students to enroll. The process is a balancing act, and one that is expected to proceed without gender-based or racial bias.

Over the past few years, news of the success of data-fed virtual teaching assistants and smart enrollment counselor chatbots has had the higher education world abuzz with the possibilities inherent in using artificial intelligence on campus. Colleges and universities hope AI will help them offload time-intensive administrative and academic tasks, make IT processes more efficient, boost enrollment in a climate of decline and deliver a better learning experience for students. On some campuses, these improvements are already taking place. While scaling up AI deployment at universities will take time due to the costs involved, some faculty members may also be resistant to AI on campus because they worry it will put them out of their jobs. The best way to convince potential stakeholders of the need for AI is to "opt for a problem-first approach," suggests the Education Advisory Board, an education enrollment services and research company.

Universities are also using predictive analytics to identify students that might struggle socially or academically during the first year. TIMES reports that Georgia State has analyzed 2.5 million grades of former students to find out what may cause a student to fail in their studies. The university developed an early warning system that triggers alerts when students are struggling, so they can be helped before they fail. Georgia State discovered from their data that how students fare in the first course in their majors can predict whether or not they will graduate. For instance, 85% of political science majors who get an A or B end up earning degrees, but only 25% of those who score a C or lower will graduate.

Artificial intelligence is transforming the traditional delivery of legal services. In general terms, the set of tools broadly called "legal analytics" promises to do two things: increase the efficiency of tasks that once required substantial time and human effort, and mine masses of data to discover new insights that were previously inaccessible. As legal scholars, we're excited about the promise of applying these tools to legal research questions. Students are involved, too, so that we can educate the next generation of lawyers to leverage these tools in their own practices. Suppose that a company wants to forecast which employee complaints lead to lawsuits.

There's often a fine line between hero and villain, and by most accounts, artificial intelligence (AI) is on the villain side, sucking jobs out of the economy. These days you can't throw a rock without hitting some pundit prognosticating on the millions of jobs that will be lost from AI. One oft-cited Oxford University study predicted 47% of jobs are in jeopardy. But while AI conjures up robots and dystopian science fiction movies, it isn't magic. Today's AI consists of algorithms developed with "training data" that improve over time, otherwise known as machine learning.

There's often a fine line between hero and villain, and by most accounts, artificial intelligence (AI) is on the villain side, sucking jobs out of the economy. These days you can't throw a rock without hitting some pundit prognosticating on the millions of jobs that will be lost from AI. One oft-cited Oxford University study predicted 47% of jobs are in jeopardy. But while AI conjures up robots and dystopian science fiction movies, it isn't magic. Today's AI consists of algorithms developed with "training data" that improve over time, otherwise known as machine learning.