ABSTRACT Engineering education faces a dual crisis: foundational courses undergo severe hour compression while AI tools proliferate, creating tension between technical implementation and professional judgment development. This study proposes a lightweight project framework that addresses this dilemma within 24 instructional periods (45 min each; 18 contact hours in total) by strategically leveraging professional authenticity to cultivate boundary recognition —the ability to diagnose when computational tools exceed valid operating ranges. Guided by outcome‐based education and informed by situated learning perspectives, the framework operationalizes three design principles: (1) technical minimalism (core code < 100 lines), (2) professional anchoring (algorithms embedded in safety‐critical decisions), and (3) dual‐model contrast (structured comparison between physics‐based and data‐driven approaches). A quasi‐experimental study involving 263 civil engineering undergraduates showed substantially higher post‐reform scores across all assessed dimensions using Welch's independent‐samples t ‐tests (all p < 0.001). The largest differences were observed for engineering risk awareness (mean difference = 2.66, d = 4.83), uncertainty quantification (2.38, d = 3.61), and critical AI usage (2.58, d = 4.35). Sensitivity analyses adjusting for prior GPA yielded virtually unchanged estimates. Crucially, 81% of students established explicit decision rules linking sample size to appropriate modeling strategies, consistent with the professional competence principle articulated in the ASCE Code of Ethics. Thematic analysis of eight student interviews indicated that small‐sample constraints functioned as cognitive scaffolds by limiting non‐essential implementation demands and foregrounding uncertainty interpretation as an engineering responsibility. This work contributes a theoretically grounded mechanism model. It explains how professional contexts transform abstract algorithmic properties into tangible engineering boundaries. By doing so, the study moves beyond descriptive accounts of “what works” to explain how the approach may support boundary recognition in AI‐augmented engineering education.
ABSTRACT Course‐level gamification has shown promise in engineering education, but comprehensive implementations in chemical engineering that combine blended learning with level‐based progression remain limited. This study presents the Mixed Pass‐Through Game (M‐PTG), a course‐level gamified blended learning framework for chemical engineering unit operations. The MPTG builds on the original Pass‐Through Game (PTG) and addresses three of its limitations, namely inadequate preparation, poor appointment compliance, and unsustainable instructor workload. It does so through a mandatory online pre‐pass‐through mechanism, a three‐stage cognitive scaffolding system, and a digital platform that automates grading and progress tracking. The framework was implemented over three consecutive semesters at a regional university in China in a quasi‐experimental design comparing three cohorts. Results showed consistent improvements: median pass‐through scores rose from 86.35 to 92.00, one‐time pass rates increased from 68.18% to 80.60%, and final examination performance improved substantially. The proportion of students scoring below 50 on the final examination dropped from 22.53% to 0%, while the percentage scoring 80 or above more than doubled from 16.90% to 46.37%. Instructor contact hours were reduced by 33% (from 144 to 96), and missed appointments decreased by 82%. Student surveys indicated high satisfaction, with over 91% reporting strengthened knowledge mastery. These findings suggest that course‐level gamification can be embedded within existing curricula, offering a potentially scalable model for engineering education.
ABSTRACT Fuzzy logic control is well known for its robustness across various automation engineering applications. Its structured linguistic modeling of human expertise and empirical knowledge makes it attractive to both students and control engineers. However, such expertise can only be developed through practice and trials. Existing solutions are time‐ and energy‐consuming general‐purpose software, as the literature lacks ready‐to‐use tools dedicated to this purpose. Therefore, this paper introduces P4FPID, a novel fuzzy logic toolbox for direct and advanced interaction with fuzzy controllers. Developed using Processing 4.4, P4FPID adopts innovative graphical human‐computer interaction paradigms that allow students to smoothly configure and experiment with fuzzy controllers in real time through simulation, animation, or hardware integration. Owing to its dynamic simulation model, P4FPID enables immediate observation of system feedback. The proposed software also supports fuzzy set subnormality exploration (an aspect rarely addressed in fuzzy control systems) and integrates an interactive genetic algorithm capable of animating more than 20 optimization scenarios. In addition, it supports industrial PLC standards (IEC 61131) and MATLAB file formats, making it suitable for both academic and professional use. As a case study application, P4FPID integrates a digital model of a Ball‐and‐Beam system currently in use, enabling experimentation across a wide range of benchmark scenarios. In this paper, a user experience survey showed a clear preference for P4FPID over the MATLAB Fuzzy Logic Toolbox, while benchmark experiments against MATLAB/Simulink & Python Simpful highlighted the advantages of an application‐oriented software approach over general‐purpose tools in terms of execution time.
ABSTRACT Automated feedback is increasingly used to support programming instruction, yet little is known about how such tools influence students' self‐efficacy development. This study examines how prior programming experience and initial confidence shape self‐efficacy gains among first‐year engineering students using an automated code critiquer for formative feedback. Pre‐ and post‐intervention surveys were collected from 380 students enrolled in an introductory MATLAB programming course, employing two validated instruments: the Computer Programming Self‐Efficacy Scale (CPSES) and the Longitudinal Assessment of Engineering Self‐Efficacy (LAESE). Students with prior programming experience demonstrated significantly greater gains in both CPSES and LAESE than novices, although distinctions among basic, intermediate, and advanced levels were minimal. Students with low initial confidence achieved the largest improvements, while highly confident students plateaued or slightly declined, reflecting ceiling or recalibration effects. Gains were more pronounced for CPSES than for LAESE, suggesting that domain‐specific feedback most strongly enhances proximal, skill‐focused beliefs. These findings highlight the importance of tailoring feedback to students' entry profiles and embedding programming within design‐rich, collaborative contexts to strengthen its connection to engineering identity. The study advances understanding of how adaptive feedback mechanisms support self‐efficacy development and provides actionable guidance for creating more inclusive and effective programming curricula in engineering education.
ABSTRACT As global environmental challenges intensify, engineering education increasingly requires approaches that integrate computational methods with interdisciplinary collaboration. This paper presents the design, implementation, and outcomes of a 5‐day intensive machine learning course tailored to civil and environmental engineering students and professionals with diverse backgrounds. The course integrated foundational machine learning concepts with hands‐on applications using real‐world environmental datasets from Uruguay. It was designed to support competencies relevant to interdisciplinary engineering practice, including the integration of environmental reasoning with data‐driven modeling and collaboration across diverse disciplinary backgrounds. Using Google Colab notebooks, students explored data preprocessing, supervised and unsupervised learning, and data analysis in an interactive, collaborative environment. Interdisciplinary teamwork was a core feature, enabling diverse perspectives to be brought to bear on solving environmental problems. The course outcomes were examined through three complementary perspectives: the instructors’ assessment of how the design functioned in practice, the academic results from the final projects, and the students’ perceptions gathered through a post‐course survey. Particular attention was given to how collaborative projects and practice‐oriented activities supported the integration of computational tools into environmental reasoning, while fostering interdisciplinary interaction among students with different academic backgrounds. These perspectives suggest that carefully designed, practice‐oriented sessions can help students engage with computational thinking, create conditions for meaningful cross‐disciplinary collaboration, and raise awareness of sustainability challenges. Beyond assessing a single course, the analysis identifies key strengths, such as improved collaboration and contextualized learning, as well as challenges, including disparities in prior knowledge and technical preparedness. These findings offer concrete lessons for future implementations and provide a replicable framework for embedding machine learning in engineering curricula, better preparing students for complex environmental challenges.
