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As a leading risk factor for chronic illnesses, obesity highlights the critical need for prompt diagnosis and active management. This research introduces an innovative expert system developed to precisely categorise obesity severity and offer individualised treatment advice. Utilising the SEMMA data mining framework alongside the tidymodels ecosystem, the study assessed five machine learning algorithms: decision tree, random forest, multinomial logistic regression (MLR), Naive Bayes, and support vector machine (SVM). Achieving the highest predictive accuracy (97.48%), MLR was chosen as the definitive model. The platform incorporates an intuitive R Shiny interface, promoting seamless real-time user engagement. Treatment protocols are formulated via if-then rule-based logic to guarantee customised advice for every obesity classification. A comparative evaluation emphasises the system's exceptional diagnostic precision and its functional utility in recommending treatments. By improving accessibility—especially for marginalised rural communities—this system bolsters public health by facilitating preliminary diagnosis, specific interventions, and forward-looking obesity care.

The application of Artificial Intelligence (AI) tools to assist with systematic literature reviews (SLRs) is expanding rapidly, yet researchers still lack structured frameworks for assessing and incorporating these tools into their research practices. This paper introduces and implements a rubric-based framework designed to evaluate AI toolchains across essential SLR stages, such as literature searching, screening, and data synthesis. A mixed-methods study was executed with 63 postgraduate student researchers, who represent the early-stage investigators that serve as primary adopters of these digital workflows. The results demonstrate that workflows structured around the rubric enhanced the participants' perceived utility of AI utilities, provided organized criteria for comparative analysis, and promoted greater transparency during tool selection. Although formal indicators of inter-rater reliability (such as Cohen’s or Fleiss’ Kappa) were not calculated due to the fact that participants did not evaluate identical benchmark sets concurrently, the consistency of the rubric was qualitatively validated through pilot evaluations, iterative updates, and structured focus group discussions. These efforts confirm interpretative alignment among users while recognizing that future validation via independent assessors is required. This research contributes a validated assessment rubric, an operational example of an AI-augmented SLR workflow, and a discussion regarding the consequences for research training, study reproducibility, and cross-disciplinary translation.