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Design and Evaluation of an Inclusive AI-Driven Learning System for Personalized Technical Skill Acquisition in Resource-Constrained Environments

Year: 2026

Type: article

Abstract: Artificial Intelligence (AI) presents transformative opportunities for educational personalization, yet traditional adaptive learning platforms remain constrained by high bandwidth requirements, complex cloud dependencies, and standardized linear instruction. In resource-constrained technical and vocational education and training (TVET) settings, these dependencies exacerbate inequality. This study presents the design, architectural prototype, and preliminary in-silico evaluation of a lightweight AI-driven learning system tailored for dynamic technical skill acquisition. Following Design Science Research (DSR) methodology, the system utilizes a Random Forest classifier to profile learners into four competency tiers (Beginner, Intermediate, Advanced, and Expert) based on multidimensional engagement and performance indicators. Benchmarking on a synthetic dataset (N = 14,003 learner records) demonstrates that the pre-examination diagnostic model—relying strictly on continuous behavioural engagement metrics (e.g., assignment completion, attendance, study hours) without exam scores—achieves 92.4% classification accuracy (F1-score = 0.924). Incorporating post-hoc summative examination scores yields a 100.0% classification result, confirming deterministic mathematical reconstruction of established grading rules. Feature importance analysis shows low predictive weights for Gender (3.35%), Internet Access (0.78%), and EduTech Access (2.97%), though Age accounts for 11.89% of predictive weight in pre-examination profiling. Quantitative benchmark measurements confirm a lightweight model footprint (< 15MB) and minimal inference latency (< 12ms), supporting potential offline edge deployment. These findings demonstrate the architectural feasibility of early pedagogical intervention in low-bandwidth settings, while highlighting the necessity of live pilot trials for operational validation.

Source: International Journal of Research and Innovation in Applied Science

DOI: 10.51584/ijrias.2026.11080117

Authors: Onyenweuwa Basil O., Ogu Philip E, Utu-Baku, M.A, Nwokoma Chukwuma U.

Institutions: Imo State University, Alvan Ikoku Federal University of Education, Owerri

Language: English

Topic: Online Learning and Analytics

Subfield: Computer Science Applications

Field: Computer Science

Domain: Physical Sciences

SDGs: Quality education, Reduced inequalities, Industry, innovation and infrastructure

Open Access status: bronze

OpenAlex ID: W7213294615