Chapter 8: Recommendations ● With regard to equity, as research advances and brings more context into AI, we will be better able to use AI to support goals that require customization of learning resources, such as enabling teachers to more easily transform materials to support neurodiverse learners and increase responsiveness to local communities and cultures. Going forward, educational leaders need to bring these and their own policy priorities to the table at every discussion about AI, driving the conversation around human priorities and not only their excitement about what new technology might do. Fundamentally, AI seeks to automate processes that achieve goals, and yet, AI should never set goals. The goals must come from educators’ vision of teaching and learning and educators’ understanding of students’ strengths and needs. 8.2. Calling Education Leaders to Action We summarize seven recommendations for policy action. These recommendations are for education leaders. In the introduction, we note the necessity of involving education constituents in determining policies for AI. We also observed throughout our listening sessions that people coming from many different roles in education all have passion, knowledge, and insights to contribute. In our view, all types of constituents can be education leaders. We are reluctant to suggest any constituent role is more important to advance any of the recommendations, but we call out specific needs for action within some of the recommendations where it is warranted. 8.3. Recommendation #1: Emphasize Humans in the Loop We start with a central recommendation throughout this report. This recommendation was a clear constituent favorite. Indeed, 88 | P a g e
99 Publizr Home