industry-education integration

Against the backdrop of deepening industry-education integration in vocational education and AI-driven digital transformation, productive teaching projects face not only the perennial challenge of transforming authentic production tasks into teachable activities, but also new challenges such as rapid resource obsolescence, heterogeneous learner foundations, and insufficient evidence for process-based assessment. Taking the “Deployment and Maintenance of a Navigation Data Storage Platform” task from the Yangtze River Trunk-Line Ship Water Pollutant Joint Supervision and Service Information System as a case, this study builds on the six-stage process of Select→Deconstruct→Define→Transform→Integrate→Extend and develops a human–AI collaborative mechanism characterized by real projects as the carrier, job competencies as the main line, and AI-supported cognition as the support. To strengthen AI empowerment across project development and implementation, AI is embedded in project selection, work-process deconstruction, competency definition, teaching-task transformation, curriculum integration, and resource expansion, and is integrated with IDEAL project-based learning, multi-stakeholder whole-process assessment, “dual-teacher + AI assistant” collaboration, and a blended virtual–real training environment. This study provides a reference for IT-related programs seeking to build replicable and iteratively improvable AI-empowered productive teaching projects.

With the continuous advancement of the construction of a national lifelong learning system and the deepening reform of the modern vocational education system, exploring the internal and external driving factors underlying the high-quality development of higher vocational colleges in border regions can effectively address several prominent challenges faced by northern higher vocational institutions, including insufficient connotative development in relation to construction requirements, inadequate motivation for autonomous learning among teachers and students, and difficulties in adapting lifelong education services to the needs of regional industries. Therefore, this research has significant practical implications.Drawing on self-efficacy theory and lifelong education theory, this study clarifies the transmission mechanisms among variables and constructs a theoretical model centered on lifelong education driving factors, teachers’ and students’ self-efficacy, and the high-quality development of higher vocational colleges. The study conducted a survey among 14 public higher vocational colleges in Heilongjiang Province, collecting 628 valid teacher questionnaires and 864 valid student questionnaires. Empirical analysis was conducted using SPSS 26.0 and the PROCESS macro, including reliability and validity tests, correlation analysis, and Bootstrap tests of mediating effects.The empirical results reveal several key findings. First, four driving factors associated with lifelong education—digital learning resource provision, the implementation of industry–education integration, the cultivation of a lifelong learning-oriented campus environment, and modern institutional governance—can all positively promote the high-quality development of higher vocational colleges. Second, these four types of factors significantly enhance the self-efficacy levels of both teachers and students. Finally, teachers’ and students’ self-efficacy plays a mediating role in this process: various lifelong education-related factors not only directly improve the quality of institutional development but also indirectly facilitate the connotative development of colleges by strengthening teachers’ and students’ confidence in learning and career development.Based on the practical context of higher vocational education in Heilongjiang Province, this study enriches the theoretical framework concerning institutional development from the perspective of lifelong education and provides two practical implications for higher vocational colleges in northern border regions. Higher vocational institutions should not only align their educational practices with local industrial characteristics but also focus on stimulating the subjective initiative of teachers and students, thereby establishing a sustainable and stable model of high-quality development.

Addressing the structural imbalance in smart tourism course teaching—characterized by "emphasizing technical principles while neglecting value guidance, and prioritizing case introductions over critical thinking and character cultivation"—this study takes the ideological and political education (IPE) reform of the course "Introduction to Smart Tourism" as its vehicle to construct a "dual-integration driven" teaching model. This model is driven by two wheels: interdisciplinary integration and industry-education integration. It systematically excavates the IPE elements embedded in the course, including cultural confidence, ecological protection, rural revitalization, red heritage, technological self-reliance, and data ethics. By building a full-chain IPE implementation system of "theoretical immersion—case discussion—project practice—evaluation feedback," and relying on a localized typical case database and a government-enterprise-university collaborative education platform, the model enables students to advance from "technical cognition" to "value internalization" and further to "professional practice." A three-year teaching practice shows that this model effectively enhances students' cultural identity, technological ethics awareness, and cross-cultural narrative ability, providing a replicable practical paradigm for similar universities to carry out IPE construction in smart tourism courses.

The digital transformation of pharmaceutical manufacturing has generated an urgent need for graduates who possess competencies in intelligent manufacturing, data-driven process control, digital quality systems, regulatory compliance, and innovation-oriented problem solving. However, conventional pharmaceutical education often remains theory-centered and insufficiently connected with real industrial scenarios. This study developed and evaluated an Intelligent Pharmaceutical Manufacturing curriculum module under the framework of industry–education integration. The module incorporated Quality by Design (QbD), Process Analytical Technology (PAT), Manufacturing Execution Systems (MES), electronic batch records (EBR), digital twins, virtual simulation, AI-assisted process optimization, Good Manufacturing Practice (GMP) compliance, validation, and data integrity. A four-integration teaching model was proposed to connect curriculum content with industrial standards, classroom learning with production scenarios, physical practice with virtual simulation, and knowledge acquisition with innovation projects. A quasi-experimental pretest–posttest control group design was used to evaluate the module. Students in the intervention group received the newly developed curriculum module, whereas students in the control group received conventional instruction. Results showed that the intervention group achieved greater learning gains than the control group in knowledge acquisition, practical competency, digital manufacturing competency, and learning engagement. The largest improvement was observed in digital manufacturing competency, suggesting that the module effectively addressed a key limitation of traditional pharmaceutical manufacturing education. Qualitative feedback further indicated that students perceived the module as authentic, industry-relevant, and beneficial for future professional development. This study provides a transferable curriculum framework—centered on the proposed four-integration teaching model and industry–education co-cultivation mechanism—for cultivating pharmaceutical professionals capable of adapting to intelligent manufacturing, and offers practical implications for industry–education collaboration in higher education.

تحول دیجیتال در تولید دارویی نیاز فوری به فارغ‌التحصیلانی ایجاد کرده است که دارای شایستگی‌هایی در تولید هوشمند، کنترل فرآیند مبتنی بر داده، سیستم‌های کیفیت دیجیتال، رعایت مقررات و حل مسئله مبتنی بر نوآوری باشند. با این حال، آموزش داروسازی سنتی اغلب نظری‌محور باقی می‌ماند و ارتباط کافی با سناریوهای واقعی صنعتی ندارد. این مطالعه یک ماژول درسی تولید دارویی هوشمند را تحت چارچوب ادغام صنعت و آموزش توسعه داده و ارزیابی کرده است. این ماژول شامل Quality by Design (QbD)، فناوری تحلیلی فرآیند (PAT)، سیستم‌های اجرایی تولید (MES)، سوابق الکترونیکی بچ (EBR)، دوقلوهای دیجیتال، شبیه‌سازی مجازی، بهینه‌سازی فرآیند با کمک هوش مصنوعی، رعایت Good Manufacturing Practice (GMP)، اعتبارسنجی و یکپارچگی داده‌ها بود. یک مدل آموزشی چهارادغامی پیشنهاد شد تا محتوای درسی را با استانداردهای صنعتی، یادگیری کلاسی را با سناریوهای تولید، تمرین عملی را با شبیه‌سازی مجازی و کسب دانش را با پروژه‌های نوآورانه پیوند دهد. برای ارزیابی این ماژول، از طرح شبه‌آزمایشی پیش‌آزمون–پس‌آزمون با گروه کنترل استفاده شد. دانشجویان گروه مداخله ماژول درسی جدید را دریافت کردند، در حالی که دانشجویان گروه کنترل آموزش متعارف را دریافت نمودند. نتایج نشان داد که گروه مداخله در کسب دانش، شایستگی عملی، شایستگی تولید دیجیتال و مشارکت در یادگیری نسبت به گروه کنترل پیشرفت بیشتری داشتند. بیشترین بهبود در شایستگی تولید دیجیتال مشاهده شد که نشان می‌دهد این ماژول به طور مؤثری یکی از محدودیت‌های کلیدی آموزش سنتی تولید دارویی را برطرف کرده است. بازخورد کیفی همچنین نشان داد که دانشجویان این ماژول را اصیل، مرتبط با صنعت و مفید برای توسعه حرفه‌ای آینده خود تلقی کردند. این مطالعه یک چارچوب درسی قابل انتقال—مبتنی بر مدل آموزشی چهارادغامی پیشنهادی و سازوکار هم‌پروری صنعت و آموزش—برای پرورش متخصصان دارویی توانمند در سازگاری با تولید هوشمند ارائه می‌دهد و پیامدهای عملی برای همکاری صنعت و آموزش عالی فراهم می‌کند.