I was recently invited to attend the SIIA Edtech Industry Conference in San Francisco as part of a panel on the uses of artificial intelligence (AI) in higher education. I was a last-minute replacement for Dee Kanejiya at Cognii, and since I’m not a software designer, I felt I should focus on the role of AI as an end user.
According to their website, the “Software & Information Industry Association [SIIA] is the principal trade association for the software and digital content industry. SIIA provides global services in government relations, business development, corporate education, and intellectual property protection to the leading companies that are setting the pace for the digital age.”
There were many topics covered in the panels at the conference, including the use of block chain (check out my podcast with Maria Spies of HolonIQ on this topic), the role of games in K-12 classrooms, and several panels which discussed issues related to diversity and inclusion. I was very impressed with the speakers who discussed their approaches to diversity, in particular those who focused on the need for inclusivity in the ways that we deal with data, algorithms, and equity issues in marketing. As one school superintendent warned the software suppliers, if you show up and talk about students without incorporating equity issues, his district isn’t likely to look at the product.
Along with my own panel on AI, I was also interested in learning more about the latest in data analytics. I have been exploring the different applications for these technologies. Interestingly, I found there was a great deal of overlap between machine learning, AI, and data analytics. They all rely heavily on data and high-powered computing. As one of the panelists noted, “Data analytics is the process of collecting, analyzing, visualizing, and deriving actionable insights from datasets.”
Data itself is an important part of the discussion. We are still dealing with the ongoing issue of “garbage in, garbage out.” Determining which data is needed for making predictions is an important piece of the puzzle. As a political scientist who used statistics and regression analysis in my own research, I understand the perils of missing data, missing variables, and understanding to what extent a particular analysis can actually predict outcomes (don’t remind me about the 2016 election).
In my talk, I focused on four areas where artificial intelligence could prove most beneficial in higher education are:
- Student acquisition, where AI could personalize the enrollment experience for students and help schools target those applicants most likely to succeed in their programs;
- Learning and instruction, where AI could help instructors do grading and provide instructional help to students;
- Student affairs, where AI could assist in degree planning and intervene with struggling students to provide advising or other resources; and
- Institutional efficiency, where AI could compile data from multiple systems to guide administration decision-making.
There are a variety of tools coming onto the market that target these four areas, and we will be examining many of these in future newsletters. One example I mentioned in my talk was the area of assessment, where AI is being developed by companies like Cognii, which is focusing on “helping students around the world by enabling personalized deeper learning, intelligent tutoring, open response assessments, and pedagogically rich analytics.”
It is important for higher ed leaders to follow the evolution of software and the tools that can help students succeed in their institutions. I will be sharing my insights and what I am learning along the way. Thank you for joining us on this journey.
About the author
Terri E. Givens is the former Provost at Menlo College in the San Francisco Bay Area; Professor of Government and European studies at The University of Texas at Austin; Vice Provost overseeing undergraduate curriculum and spearheading global initiatives as its chief international officer. She formed The Center for Higher Education Leadership (CHEL) to provide academic leaders with information and a supportive community for improving management and leadership skills in an environment of changing demographics, financial challenges, and advances in educational technology. CHEL was born of Terri’s experiences navigating these fields and learning along her journey through academe, from professor to vice-provost and provost at universities in Texas and California.