Last year, Thad Lurie, senior vice president of digital and technology at the American Geophysical Union (AGU), began looking into ways to use AI to deliver personalized information, education, and connection recommendations to members. “We have literally hundreds of thousands of peer-reviewed articles and scientific abstracts,” he says. “People were saying, ‘We know the information is there, we just can’t find it.’”
Through a technology partner, AGU developed a process that uses a machine-learning technique called vectorization, which identities the unique “fingerprint” of a piece of content, and finds matches along other pieces of content, as well as members working in those areas. It created a pilot where it vectorized a year’s worth of content — 35,000 items — and shared its findings with a select group of members. “We said we’re building a recommendations engine that’s AI based on natural language processing, and here’s what it would recommend for you,” Lurie says. “Are these pieces of content and these connections relevant to you and your research?’”
Overwhelmingly, the answer was yes.
AGU’s experience speaks to one of a number of ways that associations are looking to streamline and personalize their education pathways. Though a fully personalized credentialing and certification learning path hasn’t yet emerged, education professionals in the association world are finding places to bring a personal touch to their processes.
Jeff Cobb, cofounder and managing director of Tagoras, an educational consulting firm, points to an acronym in the instructional design world: ADDIE, which stands for analysis, design, development, implementation, and evaluation. He’s already seen associations make strides in the development arena, because AI delivers efficiencies for the subject-matter experts who are responsible for developing and vetting educational content.