AI-Assisted Analytics for Practical Education Appraisal: Evaluating Agriculture–Forestry Management Training with Multi-Indicator Models
Main article
Abstract
Agriculture–forestry economic management is a strategically important undergraduate major in China, but its practical education component is difficult to appraise because the discipline mixes economic, ecological, organisational and field-skill content. This study designs an AI-assisted multi-indicator appraisal framework that combines factor analysis, the analytic hierarchy process (AHP), entropy weighting, fuzzy comprehensive evaluation and gradient-boosted scoring, and applies it to data drawn from five Chinese agricultural universities. A four-dimension, eighteen-indicator instrument was completed by 412 senior undergraduate students, 38 faculty members, 24 administrators and 64 peer teachers, and was triangulated against 2,154 practice-base log records. Factor analysis extracted four latent factors whose structure matched the conceptual dimensions of the instrument (Cronbach α between 0.81 and 0.93). The AI-assisted scoring engine outperformed the three classical methods on reliability and discriminant validity, while AHP retained the strongest stakeholder interpretability. Empirical results show that instructor competence carries the largest aggregate weight (0.291–0.328 across methods) and that practical operation ability, teaching methods and practicality are the three highest individual indicators. A three-stage optimisation pathway is proposed (0–6, 6–18 and 18–36 months) and projected outcomes include a 23–31% rise in employer-rated graduate competence and an 18% increase in postgraduate placement. The framework gives Chinese agricultural universities a reproducible, analytics-grounded route to align practical education with rural revitalisation policy and the training needs of the modern agriculture–forestry sector.
