With the rapid development of digital technology, the use of digital learning resources has become increasingly widespread and is now an integral part of higher education. However, there is a lack of research on engineering students’ behavioral intention to use digital resources and their digital literacy. This study, which is based on two waves of longitudinal survey data from 422 Chinese engineering students, employs the unified theory of acceptance and use of technology 2 (UTAUT2) model to systematically analyze the factors influencing engineering students’ behavioral intention to use digital learning resources and explore the longitudinal relationship between this intention and digital literacy. The results show that engineering students’ behavioral intention to use digital learning resources positively predicts their digital literacy. Effort expectancy, social influence, facilitating conditions, hedonic motivation, and habit are positively related to engineering students’ behavioral intention, whereas performance expectancy and price value do not have significant effects. The findings extend the application of the UTAUT2 model in the context of digital education and reveal a longitudinal link between the use of digital learning resources and digital literacy. This provides theoretical support and practical guidance for optimizing digital learning environments, enhancing digital literacy in engineering students, and improving the design of teaching resources in higher education, contributing to the development of engineering education in China in the digital age.

The current outcomes in terms of career cognition, career planning, and practical participation among engineering talent in China are not optimistic, and the development of career adaptability is influenced by many factors, which is detrimental to their academic success. This study takes Chinese engineering college students as the research subjects and explores the factors influencing career adaptability among Chinese engineering college students through correlation analysis and importance analysis based on a random forest regression model. The study revealed that school resources, family resources, and personal resources are significantly correlated with career adaptability. The use of machine learning to rank the importance of influencing factors revealed that learning methods (deep learning methods, strategic learning methods), school resources (school satisfaction, evaluation of professional teachers, major satisfaction, evaluation of course offerings), and social relationships (relationships with classmates, relationships with teachers) have a more significant impact on the career adaptability of engineering college students.

Preparing engineers for rapidly changing sociotechnical environments requires a clear account of the competencies that undergraduate engineering programs should foster. This study used the term future outstanding engineers to refer to engineering undergraduates being prepared for engineering excellence and examined the structure and correlates of their self-reported core competence. Cross-sectional survey data were obtained from 578 engineering undergraduates at a Chinese research university. A reflective–formative higher-order model was estimated using partial least squares structural equation modeling (PLS-SEM); partial least squares multigroup analysis (PLS-MGA) compared grade cohorts; and multiple linear regression examined associations with student background and educational experience. Adaptive, professional, and basic competence formed the higher-order construct, with adaptive competence receiving the largest outer weight. The outer weight of adaptive competence was larger, and that of professional competence smaller, among third-year than first-year students; the outer weight of basic competence did not differ significantly. Selected co-curricular and language-learning experiences were associated with adaptive competence, whereas family background and leadership experience were associated with professional competence. Basic competence was associated with a broader set of student background and engagement variables. These findings support a differentiated, stage-sensitive approach to competence development. Because the data are cross-sectional, self-reported, and drawn from one institution, the results should be interpreted as structural and associational rather than developmental or causal.