This entry presents an alternative approach to prevailing methods for assessing and analyzing Autism Spectrum Disorder (ASD). It introduces an analytical framework grounded in Multimodal Conversation Analysis (CA) (Mondada, Cambridge Handbook of Methods in Conversation Analysis. Cambridge University Press, 2024), a qualitative method that systematically examines both verbal and nonverbal resources used in everyday interaction. By offering more nuanced, culturally sensitive, and interactionally grounded insights, this framework supports scholars in advancing ASD research and helps practitioners in conducting more comprehensive and ethically informed assessments. Multimodal CA can grant significant access to communication patterns among ASD individuals. Its data-driven, fine-grained analysis ensures a richer, context-sensitive understanding of communication that traditional, standardized assessments often overlook. By focusing on naturally occurring interactions, this approach highlights individuals’ communicative strengths and competencies—rather than pathologizing their differences. The proposed framework is illustrated using authentic data from a corpus of Mandarin Chinese therapy sessions between ASD children and their therapists. This work advocates for a shift toward interaction-based, participatory models of assessment that emphasizes ethical engagement and social justice. Ultimately, it calls for broader recognition of a shared evidence-based framework—rooted in the cumulative knowledge of Conversation Analysis—that allows for neurodivergent individuals to have their own “order in interaction” (Sacks, Lectures on conversation. Blackwell, 1992) acknowledged and centered in assessments of communication competence.