The article reveals the gap between the large-scale implementation of digital platforms in university language education in China and their limited pedagogical impact. It aims to systematize actual practices, identify the most effective models and barriers, and offer evidence-based recommendations for scaling up. The empirical base covers 30 universities of the Double First-Class program (2021-2023), combining a survey of 2,150 students and 450 teachers, 45 semi-structured interviews with administrators and methodologists, content analysis of 150 syllabi, and analysis of LMS log files at 5 institutions. SPSS 28 (descriptive statistics, correlation analysis, ANOVA) and NVivo 12 (thematic coding) were applied, with SAMR and TPACK models serving as the theoretical framework. The findings confirm a high level of infrastructural saturation alongside low intensity and mostly substitutive use of basic tools: LMS (adoption rate 98,4%) and video conferencing (99,1%) are used on average 2,15 and 1,85 hours per week, with moderate satisfaction. In contrast, mobile applications (5,75 hours/week, satisfaction 4,61) and interactive AI platforms (3.20 hours/week, 4,18) show a statistically significant positive correlation with academic performance (r=0.38 and r=0.52, p 0.001). Teachers’ assessments point to a functional differentiation of tools by competencies: multimedia for listening (9,14/10), interactive texts for reading (8,91), collaborative editors for writing (9,27), while the development of spontaneous oral speech is best supported by conversational AI platforms (8,76), yet remains methodologically challenging. The barriers are mainly organizational and methodological: insufficient teacher training (71,3%), lack of time (65,4%), and mismatch between digital resources and curricula (45,1%). The discussion emphasizes the need to shift from extensive digitalization to an ecosystemic course design linking tools to didactic goals, restructure methodological work, establish digital pedagogy centers, and create motivation mechanisms and effectiveness metrics. The conclusions confirm that sustainable quality growth is achievable through investment in human capital, integration of adaptive analytics, and expansion of practices based on active interaction, personalization, and reflection.