Evaluating Ethics Education in Green Intelligent Manufacturing
Green intelligent (GI) manufacturing merges two major engineering paradigms—green manufacturing’s focus on resource efficiency and environmental stewardship, and intelligent manufacturing’s emphasis on data-driven automation and system self-optimization. This integration reshapes traditional industrial processes, introducing new functions such as self-organization and dynamic resource planning, while simultaneously raising complex ethical challenges. The rapid deployment of AI, robotics, and advanced digital systems in manufacturing impacts moral responsibility boundaries, privacy protections, and societal norms, making GI engineering ethics education (EEE) a critical priority.
GI EEE aims to equip future engineers with the awareness, knowledge, and willpower to navigate ethical dilemmas inherent in advanced manufacturing. Ethical sensitivity fosters recognition of issues involving people, society, and nature. Ethical knowledge builds competence in applying principles of honesty, fairness, sustainability, and safety. Ethical willpower ensures confidence and courage to act on these principles. The teaching strategies span independent ethics courses, embedded modules within technical subjects, and integration with humanities curricula. Methods include case studies of incidents like the Challenger disaster, role-playing, service learning, and project-based learning, supported by diverse assessment tools from written reports to creative product development.
Recognizing the need for systematic evaluation, researchers constructed an index system across three dimensions: cultivation education (micro-level training quality), collaborative education (meso-level coordination among stakeholders), and situational education (macro-level adaptation to local and global contexts). Experts from engineering ethics, education management, and philosophy refined the indicators to 3 primary, 8 secondary, and 27 tertiary measures. Weights were assigned using a fuzzy analytic hierarchy process (AHP) with triangular fuzzy numbers to handle qualitative judgments, and implementation was assessed via fuzzy comprehensive evaluation.
Data from 219 respondents—primarily university teachers with backgrounds in digital and green engineering—revealed that training education held the highest weight at 38.9%, followed by collaborative education at 32.4%, and situational education at 28.7%. Despite this prioritization, performance across all three dimensions was rated “general” by the maximum membership degree principle, with average scores hovering around 70–71 on a 100-point scale. Cultivation and collaborative education slightly outperformed situational education, yet gaps between theoretical emphasis and practical execution were evident.
Situational education scores reflected limited differentiation between international and local perspectives, underscoring the need for both global engagement and deep localization. Collaborative education indicators showed that policy support was weakest, with government participation in GI EEE lagging. This lack of emphasis on ethical literacy at policy level hampers the diffusion of ethics as a societal value. Enterprise involvement, while recognized as vital for aligning ethics with career preparation, remains peripheral in many programs.
Training education indicators—teaching staff quality, process, and output—were relatively strong, but optimization opportunities exist in expanding case-based learning and implementing full-cycle ethics education that follows projects from conception through decommissioning. Strengthening interdisciplinary collaboration with humanities and social sciences departments is essential to address the multifaceted nature of GI ethics.
Recommendations focus on three fronts. First, dynamically balancing training supply and demand by aligning educational concepts, teaching practices, and faculty development with the evolving needs of GI manufacturing. This includes fostering student-centered, interdisciplinary approaches and leveraging alumni engineers as mentors. Second, enhancing governance through multi-stakeholder collaboration, integrating universities, enterprises, and government in co-constructing resources, funding systems, and practice platforms. Industry-university cooperation should bring students into real manufacturing environments to contextualize abstract ethical concepts. Third, deepening situational education by harmonizing international vision with regional discourse, absorbing global best practices while building a distinctive local ethical framework. Embedding GI EEE into broader ideological and political education can elevate its institutional status and ensure that professional courses reflect embedded ethical norms and values.
The empirical evaluation confirmed that while GI EEE’s foundational mechanisms are in place, its implementation remains at a moderate level, constrained by uneven stakeholder engagement and insufficient localization. Addressing these gaps through targeted reforms in training, collaboration, and situational adaptation will be key to preparing engineers capable of responsibly advancing green intelligent manufacturing.
