The Internalized Ableism Inventory: Scale development using a hybrid artificial intelligence and community-based participatory research design.

This study (a) used OpenAI's GPT-4 large language model to generate an initial item pool for a potential scale measuring internalized ableism, (b) involved disabled community stakeholders in refining the items and prompting additional artificial intelligence-generated items, and (c) psychometrically validated the scale in a large sample of disabled individuals. Following a series of GPT-4 prompts and iterative community-based participatory research feedback, a tentative item pool of 90 statements was developed. A sample of 409 adults with diverse disabilities completed a survey containing the initial item pool, potentially related scales, and demographic questions. An exploratory factor analysis helped identify the final 51 items and subscale structure, and a confirmatory factor analysis then provided evidence of excellent factor structure fit. The scale contained eight subscales with Cronbach's αs that ranged from .85 to .97, with an overall total score α of .98. The total score and subscales showed consistent convergent validity with other measures of internalized stigma for chronic illness and anger and frustration with disability. This study generated for the first time in the known research literature a nuanced, comprehensive, and psychometrically sound scale based on the integration of both artificial intelligence and community-based participatory research methodology: the Internalized Ableism Inventory. The demonstrated methodology generating it has the potential to modernize psychological scale development approaches. (PsycInfo Database Record (c) 2025 APA, all rights reserved).