Transforming Research Methodology in Audiology and SpeechLanguage Pathology: Emerging Paradigms, Technological Innovation, and Methodological Excellence

Audiology and Speech-Language Pathology (ASLP) research is undergoing substantial methodological transformation through artificial intelligence (AI), machine learning (ML), telepractice, mixed methods, multimodal data, longitudinal assessment, and interoperable data systems. This critical narrative review with structured evidence mapping examined emerging methodological paradigms and their implications for validity, reliability, external validation, reproducibility, data standardisation, cultural and linguistic responsiveness, and clinical translation. The primary evidence window covered January 2015 to August 2026, with earlier foundational methodological sources retained where relevant. Fifty-two core documents were included in the analytical corpus. Recent evidence demonstrates rapid technological expansion: Frosolini et al. (2024) identified 1,359 records and included 104 manuscripts in an AI-audiology scoping review, with 87.5% published during the preceding four years. Scott et al. (2025) identified nine randomised controlled trials comparing telehealth with face-to-face speechlanguage pathology, while Vercammen et al. (2026) reported data-standardisation findings from 82 survey participants and five experts, with 90% of respondents willing to follow or contribute to standardisation efforts. Across the literature, technological development has progressed faster than methodological harmonisation, particularly in external validation, measurement standardisation, interoperability, representation, reproducibility, and implementation. An integrated framework is therefore proposed linking the research question, design, measurement, technology, analysis, validation, contextual responsiveness, implementation, and clinical meaning. Methodological excellence in ASLP should ultimately be defined by rigorous, transparent, reproducible, equitable, and clinically meaningful evidence rather than technological novelty alone.