Lessons Learned from the Pandemic to Inform Decision-Making About AI in K-12 Schools

• In Event: Virtual Poster Session
  In Virtual iPresentation Only: Division A - Section 1: Leadership Virtual Poster Session

Abstract

Rapid advances in Artificial Intelligence (AI) and its implications for teaching and learning are forcing K-12 leaders to make quick decisions about uses of AI in their schools under great uncertainty and perceived urgency – not unlike what happened during the COVID-19 pandemic. In these situations, traditional decision-making approaches may not be applicable. To support K-12 leaders’ current decision-making around AI, in this paper we revisit data collected from 58 interviews with Western New York school leaders in 2020-21, to identify how these leaders approached the decisions they had to make during the pandemic and then derive lessons about decision-making in time of uncertainty more generally and around AI more specifically.

Authors

• Andrea Cutt, University of Rochester

• David E. Miller, University of Rochester

• Raffaella Borasi, University of Rochester

• Zenon Borys, University of Rochester