STUDENT PERCEPTIONS OF TRANSLATION LEARNING IN AI ERA: AN INQUIRY INTO THE NEEDS, MOTIVATIONS, EXPECTATIONS, AND PEDAGOGICAL IMPLICATIONS IN A CHINESE UNIVERSITY - Liang, Minna; Panyanuwat, Anurak; Rupavijetra, Phetcharee (วารสารวิชาการ มหาวิทยาลัยราชภัฏอุตรดิตถ์, 2026)
The objectives of this research were 1) examine undergraduate students’ translation learning needs, 2) investigate their motivations, learning preferences, and pedagogical expectations, 3) integrate quantitative and qualitative findings to understand their developmental needs, and 4) develop and evaluate a CoI-informed model for translation competence development. The study involved 359 foreign language students at Guangxi University of Foreign Languages (GUFL), China. Quantitative data on students’ self-assessed translation competence across ten dimensions were collected through a structured questionnaire and analyzed using paired-sample t-tests and Pearson’s correlation coefficient, while qualitative data were analyzed using reflexive thematic analysis. Significant gaps between current levels and desired levels were found across all dimensions (p < .001), with the largest gaps observed in language competence, strategic competence, and translation knowledge. Correlation analysis revealed critical thinking emerged as the most structurally central competence dimension. The thematic analysis generated seven core themes, including developmental progression, value-driven motivation, teacher guidance, communicative interaction, digital learning support, situated practice, and affective safety. Integrated findings led to the development of the Integrated Human and Digital Communication (IHDC) model, emphasizing pedagogical guidance, communicative interaction, and cognitive inquiry, with critical thinking functioning as the integrative core. Expert evaluation (N =20) confirmed the model’s appropriateness, particularly in contextual relevance, pedagogical suitability, and adaptability. The findings support AI-assisted translation learning, the integration of AI literacy and critical thinking into curricula, and authentic or scenario-based translation learning in multilingual university contexts such as GUFL.