<![CDATA[This qualitative study addresses the limited attention given to learner-generated digital discourse in research on Native English-Speaking Teachers (NESTs) and Non-Native English-Speaking Teachers (NNESTs), particularly in autonomous English learning on YouTube. It investigates how learners perceive NEST and NNEST English-speaking instruction and the instructional impacts they attribute to each teacher type. Rather than focusing on on-screen teaching strategies, the study examines viewer comments as evidence of learner reception and triangulates these perceptions with semi-structured interviews with the content creators. The data comprised comments from six English-speaking instructional videos, three produced by NESTs and three by NNESTs. Thematic content analysis revealed distinct yet complementary patterns of learner perception. NEST-related comments were dominated by linguistic authority and admiration, with viewers valuing native-speaker models for phonetic accuracy, linguistic authenticity, and sociolinguistic exposure. In contrast, NNEST-related comments emphasized relatability and comprehensibility, with viewers valuing teachers who provided accessible models, emotional connection, and reassurance that contributed to reduced speaking anxiety. Interpreted through Krashen’s Affective Filter Hypothesis and Medgyes’ framework on NESTs and NNESTs, the findings suggest that perceived authenticity and affective accessibility represent complementary dimensions of instructional value in digital EFL learning. Interview data further indicated that creators’ discourse adjustments were broadly consistent with learners’ perceived pedagogical and affective needs. The study contributes to digital language pedagogy by demonstrating that effective YouTube-based English instruction need not be defined by native-speaker status alone, but can emerge through complementary forms of linguistic authenticity, relatability, and empathy. These findings also have implications for teacher education, highlighting the need to prepare both NESTs and NNESTs to address learners’ linguistic, affective, and communicative needs in autonomous digital learning environments.]]>
