Abstract
As Industry 5.0 (I.D. 5.0) reshapes the engineering education landscape, Higher Education Institutes (HEIs) have evolved to integrate Generative Artificial Intelligence (GenAI) via strategic curriculum revamps to meet Education 5.0 (E.D. 5.0) competencies. EN.540.202 (Introduction to Chemical & Biological Process Analysis) is the first core course at Johns Hopkins University and was revamped in Fall 2025 to create more rigorous course content and the conscious creation of new weekly graded problem sets, which did not rely on prior course content/textbook-based solved examples. Problem sets were fed as Effective Prompt Engineering (EPE) inspired prompts to ChatGPT, and AI-elicited responses were compared. AI was able to perform fundamental calculations, offer detailed explanations, unit conversions/checks, proactive information (outside the problem scope), and graphical information. Key challenges and pitfalls observed were terminology misinterpretation, lack of visual representation, data acquisition issues, and ambiguity when tackling open-ended problem prompts, which pose a potential threat of incorrect learning if students exclusively rely on ChatGPT. Quantitative benchmark comparisons of AI performance spanning across various prompt categories indicate a significant 73% performance gap. While AI demonstrates 88% accuracy on standard problems, the success rate plunges to 15% on complex mass/energy balances involving non-ideal recycle streams and constraints. Introduction of EPE and Chain-of-Thought (CoT) strategies mitigated these risks, restoring AI accuracy to 91% in instructor-led trials. To ensure academic integrity, a dual-layer Quality Assurance (QA) audit was implemented using Turnitin's AI-writing detection; analysis shows a mean AI-probability of <12% in derivation sections, suggesting students successfully used AI for structural brainstorming, while maintaining technical ownership of engineering execution. Anonymous student-filled instructor teaching evaluations capture a strongly positive perception of preparedness, better student engagement, and recognition of potential risks of over-reliance on AI-led Large Language Models (LLMs), potentially leading to AI-led misdirected learning (unless students can distinguish between correct/incorrect AI responses). Despite the intellectual challenge of the course being rated as high, overall course quality and instructor teaching effectiveness metrics were ranked high, signalling a successful case of AI-led curriculum revamps, capturing a thoughtful integration of EPE into the undergraduate curricula at HEIs. This work provides a scalable framework for HEIs to modernize engineering pedagogy, positioning AI as a collaborative tool that augments and empowers, rather than replaces, critical human-centric engineering judgment.