AI literacy development among special education graduate students: a mixed-methods exploration

A mixed-methods study exploring AI literacy among master's-level special education graduate students revealed a significant conceptual shift. Initially perceiving generative AI as an administrative efficiency tool, participants increasingly recognized its potential as an adaptive instructional scaffold for students with high-incidence disabilities. This evolving understanding was coupled with heightened awareness of critical implementation considerations, including human oversight, data privacy, and individualized student alignment.

The evolving perception of generative AI among future educators highlights its potential to transform instructional methodologies, particularly for diverse learning populations. Understanding these shifts and associated concerns is crucial for developing robust frameworks that ensure ethical, effective, and equitable integration of AI technologies across educational settings. This has broad implications for future workforce readiness and service delivery models.

Special education graduate students demonstrated an evolution in their understanding of generative AI, moving from viewing it as an administrative tool to an adaptive instructional scaffold.

Forward consideration, not a verified fact.