Understanding Public K-12 Indigenous Student Interest and Access to CS and AI Education through Teacher Perspectives and National CS Data

• In Event: Promoting Equitable CS and AI Education: Strategies to Expand Capacity and Access

Abstract

Objectives or Purposes
This study focused on public K-12 Indigenous student interest and access to computer science (CS) and artificial intelligence (AI) education opportunities through Indigenous and Western research methodologies. This poster highlights two methods used in the larger study: a survey of teachers who are Indigenous and/or are teachers of K-12 Indigenous students and a retrospective analysis of extant Code.org data (from their annual State of CS reports) with a focus on K-12 Indigenous students.

Theoretical Framework
The analyses presented are part of a larger study coalition that was collectively rooted in Indigenous Research Methodologies (IRMs) and the core tenets of community-based research (CBR). Smith [24] and Brayboy et al. [25] emphasize that IRMs are deeply tied to collective responsibility, respect, and relationships; at the same time CBR emphasizes shared decision making, reciprocity, and relevance to community priorities [26].

Methods and Data Sources
This poster describes two modes of inquiry: (1) an Indigenous-serving CS teacher survey and (2) retrospective analysis of Code.org data related to Indigenous students. For both lines of inquiry, IRMs and CBR were used to design and collect a survey that was culturally-responsive to Indigenous teachers and/or teachers of Indigenous students and to conduct a culturally-pertinent analysis of code.org data. From April-May 2025, the survey was electronically administrated to teachers who attended the 2024 Four Corners Computer Science Convening and/or received CS professional development through Indigitize. A total of 124 teachers completed the survey. The survey asked about teacher perspective, beliefs, and understandings of CS and AI and Indigenous student access to CS and AI learning opportunities. The analysis of extant Code.org’s 2021–24 State of CS reports focused on the top ten states with the highest enrollment of public K-12 Indigenous students using the following data elements: Indigenous student access to foundational CS courses, participation in foundational CS courses, access to AP CS courses, and scores on AP CS courses, as well as Code.org’s CS education policy adoption rubric to evaluate how many policies each of these top ten states have adopted.

Results
Although teachers emphasized the importance of in-school CS and AI education for Indigenous students, the code.org analysis demonstrated that Indigenous students remain the least likely of their ethnic/racial peers to have access to CS education while also residing in states that have low CS education policies adopted, signifying not only significant racial and geographic inequity to CS and AI education but also access to equitable state CS education policies. Results demonstrate the desire and opportunity for educators, district leadership and state policy makers to establish equitable CS and AI education and policy that empower Indigenous students to succeed in CS.

Scholarly Significance
This inquiry sought to understand public K-12 Indigenous student interest and access to CS and AI education opportunities and to provide critical baseline data that is needed to inform the field, K-12 education practitioners and systems, and state policy makers regarding the urgent need to increase equitable access to CS and AI education for Indigenous students.

Authors

• Marissa Spang, American Institutes for Research

• Brenda D. Arellano, American Institutes for Research

• Joseph P. Wilson, American Institutes for Research