Resumen: Inclusive education requires tools that are sensitive to neurocognitive diversity and capable of identifying profiles that have historically remained overlooked. In the case of autism, women are frequently underdiagnosed due to more subtle manifestations, social camouflaging strategies, and biases in traditional diagnostic instruments, which have been developed primarily based on male samples. This lack of detection limits access to appropriate educational support and hinders equitable intervention. In response to this need, the present study developed and validated a self-report questionnaire for the detection of Level 1 Autism Spectrum Disorder (ASD) in women over 16 years of age. A total of 47 items were initially created and later reduced to a 19-item unifactorial model after exploratory and confirmatory factor analyses. The model explained 68.2% of the variance and showed good fit indices (RMSEA = 0.061; CFI = 0.920; TLI = 0.905; SRMR = 0.047), as well as high internal consistency (α = 0.962), temporal stability (r = 0.948), and discriminative power (AUC = 0.961). This instrument can contribute to teacher training and the implementation of fairer educational practices by facilitating the identification of the female autism phenotype and promoting learning environments where all individuals can thrive. Idioma: Inglés DOI: 10.3390/educsci15091242 Año: 2025 Publicado en: Education Sciences 15, 9 (2025), 1242 [14 pp.] ISSN: 2227-7102 Tipo y forma: Artículo (Versión definitiva) Área (Departamento): Área Didáctica y Organiz. Esc. (Dpto. Ciencias de la Educación)