Tutor feedback, simulation-based learning, and AI-aware practice in MSc electrical engineering module: a case study - Emad Al-Mahdawi; Nkaepe Olaniyi (Institute of Advanced Engineering and Science, 2026)
Engineering programs routinely collect tutor evaluation data for quality assurance, yet the evidence is often not translated into a transparent, reproducible module enhancement plan that can be audited, monitored, and reported as scholarly work. This paper proposes a tutor feedback-to-enhancement (TFE) framework that transforms a standard tutor feedback worksheet into: i) a coded evidence base; ii) a descriptive closed-item profile; and iii) an evidence-to-action matrix (EAM) that links observed strengths and gaps to targeted interventions and measurable indicators. The framework is demonstrated through a single-module case study (an introductory MSc electrical power engineering systems (EPES) module) using one completed tutor feedback sheet containing closed ratings and open comments. The closed items show uniformly positive evaluations (7/12 items rated excellent and 5/12 rated good; no satisfactory/unsatisfactory responses; one item not applicable). The open-text evidence highlights simulation as a core learning scaffold, the importance of equitable access to laptops and e-learning resources, and a specific curriculum enhancement need for a dedicated lecture on photovoltaic (PV) design and battery energy storage systems (BESS). The main contribution is a practical, low-cost method that operationalizes routine tutor feedback into an auditable enhancement pathway, including an explicit artificial intelligence-aware (AI-aware) practice component that emphasizes verification and engineering judgment.