The arrival of generative artificial intelligence tools into university classrooms has coincided with a separate but equally significant shift: students are increasingly absent from those classrooms. This perspective article draws on nearly twenty-five years of teaching and learning research in chemical engineering at an Australian research university, including published work on holistic curriculum design, distance education, digital technology in remote engineering programs, and engineering dual degrees, to examine how AI tools are reshaping course offerings, assessment design, and the development of analytical thinking in chemical engineering students. The article argues that while AI offers genuine pedagogical benefits, its uncritical adoption, combined with the structural erosion of in-person contact caused by recorded lecture culture and employment pressures, poses a serious risk to the discipline. A convergence framework is proposed, three evidence-based tools are provided (an AI capability and risk matrix, a learning mode comparison, and a course-level AI use policy framework), and practical recommendations are offered for educators, curriculum designers, and institutional leaders. The analysis is situated within one institutional context and is offered as a practitioner perspective rather than a generalisable empirical claim.
With constrained laboratory facilities and expanding class sizes, many universities end up delivering process control courses in a highly abstract, math-heavy format rather than through hands-on experimentation. This work describes a set of three Arduino-based process control experiments developed and implemented for a chemical engineering process dynamics and controls course. These labs were specifically designed to deliver hands-on experience with key process control concepts within a traditional lecture hall environment, thereby alleviating the demand on teaching laboratories. Students wired components, performed calibrations, conducted system identification, and designed P and PI water level controllers. End-of-course survey data from 63 chemical engineering seniors showed high engagement, with over 80% reporting neutral or improved perceived understanding and engagement with process control concepts. This low-cost ($74.77 per group) toolkit offers a portable, highly scalable framework for delivering hands-on process systems engineering education under resource and capacity constraints.
Process safety should function as a formative axis of chemical engineering education rather than as an isolated topic reserved for a single elective or a late stage lecture block. Areal Locations of Hazardous Atmospheres (ALOHA®), part of the Computer-Aided Management of Emergency Operations (CAMEO®) Software Suite, can be integrated into the curriculum as a structured digital environment for supporting competence development in hazard identification, scenario construction, consequence interpretation, sensitivity analysis and emergency oriented decision making. A literature grounded and replicable integration framework was implemented through a propylene glycol production case. Aspen Plus outputs were translated into ALOHA inputs for four critical process sections and students compared six atmospheric scenarios defined by three times of day under dry and humid conditions. The activity supported systematic discussion of how inventory, release representation, gas behaviour and meteorological conditions affect dispersion footprints. The largest downwind distances were associated with propylene glycol releases from the reactor, whereas hydrogen scenarios were shorter and showed somewhat greater relative variation across the selected meteorological cases. The educational value of ALOHA emerged from the interpretive work around these outputs. When embedded in a curriculum wide process safety strategy, it offers a low cost bridge between process design and screening level consequence analysis.
Generative artificial intelligence (GenAI) is increasingly used in chemical engineering education, yet understanding of how individual applications connect with curriculum design and engineering competency development remains fragmented. This review examines these connections through structured literature identification and thematic synthesis. A Web of Science Core Collection search conducted in August 2026, supplemented by reference-list checking, informed an educational evidence base of 54 publications, comprising 24 core chemical-engineering or closely discipline-related publications and 30 supporting publications from related educational contexts. We synthesize the literature through three complementary roles: AI as an Intelligent Tutor, AI as a Learning Partner, and AI as Educational Infrastructure. These roles connect conversational support, collaborative problem solving, and integrated learning environments with different instructional requirements and learner competencies. We develop a human-centered framework linking these roles to foundational AI literacy, integration within disciplinary courses, and authentic engineering practice. Reported benefits include greater engagement, collaboration, and opportunities for personalized support, but their realization depends on pedagogical design, output verification, and learners’ critical engagement. The evidence remains uneven and largely short-term, while unreliable outputs, cognitive dependence, assessment validity, and institutional constraints limit broader conclusions. We outline future priorities for trustworthy educational AI, pedagogical development, and institutional support for responsible GenAI integration in chemical engineering education. The proposed framework offers educators a conceptual basis for aligning AI use with learning objectives while preserving first-principles reasoning, engineering judgment, and human responsibility.
Transport phenomena are a cornerstone of engineering education, yet students often struggle to connect the mathematical structure of governing equations with their underlying physical meaning. This work proposes an operator-centered pedagogical framework aimed at improving conceptual understanding of transport equations. Rather than presenting governing equations as the outcome of formal mathematical derivations, the approach constructs and interprets them through the physical meaning of differential operators (gradient, divergence, and Laplacian), viewed as descriptors of local inhomogeneity, flux imbalance, and diffusion within continuous fields. Within this framework, mass, momentum and energy transport are presented through a unified interpretation that highlights their common structure and underlying physical principles. The approach was implemented through instructional seminars involving 72 engineering students who had previously completed standard transport courses. Learning outcomes were evaluated through a conceptual pre-test/post-test assessment. The percentage of correct responses increased from 27% to 79%, indicating a substantial improvement in students’ ability to interpret differential operators and transport terms. These results were also supported by positive student feedback collected through a post-session questionnaire. The findings suggest that the proposed framework can effectively complement traditional instruction by bridging the gap between mathematical formalism and physical intuition.
