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All Journal International Journal of Evaluation and Research in Education (IJERE)
Emad Al-Mahdawi
MidKent College Training Services Ltd-Royal School of Military Engineering
Education

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Tutor feedback, simulation-based learning, and AI-aware practice in MSc electrical engineering module: a case study Emad Al-Mahdawi; Nkaepe Olaniyi
International Journal of Evaluation and Research in Education (IJERE) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v15i4.38929

Abstract

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.
Writing a research paper with artificial intelligence: a step-by-step guide for junior researchers Emad Al-Mahdawi; Nkaepe Olaniyi
International Journal of Evaluation and Research in Education (IJERE) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v15i4.38930

Abstract

Early-career researchers often have a sound idea yet struggle to turn it into a publishable manuscript that reviewers can trace and evaluate. This paper synthesizes practical guidance on structuring and drafting research articles using the introduction-methods-results-and-discussion (IMRaD) convention, while addressing emerging concerns about the responsible use of generative artificial intelligence (AI) in academic writing. A documentary narrative synthesis was conducted using 36 high-authority sources, including writing guides, guidance from journal editors, publisher and ethics policies, and recent empirical studies on AI-assisted writing. Recommendations were coded using an explicit IMRaD-aligned codebook and then consolidated into a step-by-step workflow from question formulation to submission checks. The synthesis indicates that treating IMRaD as a traceability checklist improves alignment between research questions, methods, results, and claims, and that iterative revision is more effective than one-pass drafting. AI support is most defensible when limited to language and process assistance, combined with disclosure, reference verification, and full human accountability for all content. The paper concludes with an actionable checklist and a visual ‘traceability map’ that can be adapted for research training and supervision.
Co-Authors Nkaepe Olaniyi Nkaepe Olaniyi