Status Report on California Schools and Districts for AI in Schools: How Ready Are we?

• In Event: Urban AI Unlocked: Transforming District Systems for Equitable AI Integration

Abstract

Objectives

Based on the news media, it can seem like AI is everywhere. With the longstanding recognition that when it comes to hype around any educational technology, actual conditions for adoption and use can look very different (Ames, 2019; Cuban, 2001). In this session contribution, I speak to a specific body of work to synthesize the status of California’s engagement with AI in schools. California is a notable case as legislation to integrate AI literacy into subject areas and guidance has been passed (AB 2876) as well as formation of a state level workgroup to examine the AI needs for a large, multi-lingual state.

Perspective

The approach is a differentiated view of AI literacy for different subgroups, and specifically teachers who have needs and perceptions from students with respect to AI (Author, 2025b). Therefore systems-level teacher adoption is the core focus. Through mixed methods inquiry, I pursue a grounded understanding from quantitative survey data, coded open written statements, and interviews of what are the current conditions, assumptions, and prioritizations for AI in schools.

Methods

The underlying work involves a mix of existing survey data from prior research, including survey data from Author (2025b) on teachers’ professional development needs related to AI in a large urban school district (N = 1454) and existing publicly shared survey data (N = 645) from third party organizations (i.e., Consortium on School Networking (CoSN)). Interview data, collected in Fall of 2025, from principals provides additional snapshots.

Results

Principals report that most districts still lack uniform policies or guidance, and for these efforts, they are relying heavily on IT department expertise. Survey data suggest teachers are still reluctant about using or allowing AI, with survey data saying elementary teachers having more foundational questions about whether AI is pertinent to their teaching practice and secondary teachers worried about plagiarism and cheating. Broadly, surveys show some optimism that AI can support teachers’ work and is not expected to replace them, but there is an urgent call for practice-specific professional development that recognizes different sets of teacher needs.

Significance

State-level portraits like this help identify common responses across actors in education systems – such as principals and teachers – in specific policy contexts. Much of the focus on AI in education has been on classroom solutions, but this study helps to articulate how multiple levels – including state level and district level – affect the classroom. From this, we can better understand the options for classroom practice.

Author

• Victor R. Lee, Stanford University