A Student Centric approach to AI in Higher Ed
Machine Learning is being added to software used by Universities and Colleges for student admissions, determining financial aid, grading, and placing students in specific programs. COVID has accelerated the use of such algorithms by eliminating in-person testing, teaching, and interviews. But, these systems are programmed to achieve institutional goals, not student or societal goals. It is time to stop using many of these algorithms and reimagine how to use student-centric AI instead of institution-centric. A Student-centric AI approach can empower students to take control of their educational experience and reimagine the university's role.
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Additional Supporting Materials
- Higher education needs to immediately institute AI Review Boards (AIRBs) to evaluate and disclose how AI is being used on campus.
- The incentives for an institution are not always aligned with the students or faculty and ML/AI used by the instition be harmful to educational goals.
- ML/AI can be a powerful tool for students, empowering them to get an education when and where they need it, and from the best providers.
- J Scott Christianson, Associate Teaching Professor, University of Missouri
J Scott Christianson, Associate Teaching Professor, University of Missouri