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

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.

Artificial Intelligence in Education (AIED), Personalized Learning, Technical Skill Acquisition, Random Forest Classifier, Resource-Constrained Environments, Inclusive Education, Synthetic Benchmark

1. Almasri, F. (2024). Exploring the impact of artificial intelligence in teaching and learning of science: A systematic review of empirical research. Research in Science Education, 54(4), 977–997. https://doi.org/10.1007/s11165-024-10176-3 [Google Scholar] [Crossref]

2. Bayly-Castaneda, M., Wang, Y., & Rosyadi, A. (2024). Technological dependencies and equity in adaptive learning environments: A scoping review. Educational Technology Research and Development, 72(2), 511–534. https://doi.org/10.1007/s11423-024-10350-x [Google Scholar] [Crossref]

3. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324 [Google Scholar] [Crossref]

4. du Plooy, C., Wang, Y., & Bayly-Castaneda, M. (2024). Personalised adaptive learning in resource-constrained contexts: A systematic scoping review. Computers & Education, 210, 104950. https://doi.org/10.1016/j.compedu.2023.104950 [Google Scholar] [Crossref]

5. European Commission. (2022). Ethical guidelines on the use of artificial intelligence (AI) and data in teaching and learning for educators. Publications Office of the European Union. https://doi.org/10.2766/153756 [Google Scholar] [Crossref]

6. European Commission. (2026). Guidelines on the ethical use of artificial intelligence and data in teaching and learning. European Education Area. https://education.ec.europa.eu/guidelines- ai-data [Google Scholar] [Crossref]

7. Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75–105. https://doi.org/10.2307/25148625 [Google Scholar] [Crossref]

8. Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign. [Google Scholar] [Crossref]

9. Kulik, J. A., & Fletcher, J. D. (2016). Effectiveness of intelligent tutoring systems: A meta-analytic review. Review of Educational Research, 86(1), 42–78. https://doi.org/10.3102/0034654315581420 [Google Scholar] [Crossref]

10. Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson. [Google Scholar] [Crossref]

11. Miao, F., & Holmes, W. (2023). Guidance for generative AI in education and research. UNESCO Publishing. https://doi.org/10.54675/EVDF9635 [Google Scholar] [Crossref]

12. Miao, F., Shiohira, K., & Lao, N. (2024). AI competency framework for students. UNESCO Publishing. https://doi.org/10.54675/QXOE4512 [Google Scholar] [Crossref]

13. OECD. (2023). OECD digital education outlook 2023: Towards an effective digital education ecosystem. OECD Publishing. https://doi.org/10.1787/c74f03de-en [Google Scholar] [Crossref]

14. Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2007). A design science research methodology for information systems research. Journal of Management Information Systems, 24(3), 45–77. https://doi.org/10.2753/MIS0742-1222240302 [Google Scholar] [Crossref]

15. Rosyadi, A., Wang, Y., & du Plooy, C. (2023). Artificial intelligence applications in technical and vocational education: A systematic mapping study. Journal of Vocational Education & Training, 75(4), 621–643. https://doi.org/10.1080/13636820.2023.2189012 [Google Scholar] [Crossref]

16. Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10(1), Article 15. https://doi.org/10.1186/s40561-023-00237-x [Google Scholar] [Crossref]

17. UNESCO. (2023a). Global education monitoring report 2023: Technology in education: A tool on whose terms? UNESCO Publishing. [Google Scholar] [Crossref]

18. UNESCO. (2023b). Guidance for generative AI in education and research. UNESCO Publishing. [Google Scholar] [Crossref]

19. UNESCO. (2024a). AI competency framework for students. UNESCO Publishing. [Google Scholar] [Crossref]

20. UNESCO. (2025). Digital learning in low-resource contexts: Key considerations for equity and inclusion. UNESCO Publishing. [Google Scholar] [Crossref]

21. UNESCO Institute for Information Technologies in Education [UNESCO IITE]. (2025). Digital technologies for inclusive education: Guidelines and policy recommendations. UNESCO IITE. [Google Scholar] [Crossref]

22. Wang, Y., Bayly-Castaneda, M., & du Plooy, C. (2024). Artificial intelligence in education (AIED): A systematic review and meta-analysis of adaptive learning platforms. Interactive Learning Environments, 32(3), 1102–1124. https://doi.org/10.1080/10494820.2023.2214502 [Google Scholar] [Crossref]

23. Wang, Y., Bayly-Castaneda, M., & du Plooy, C. (2025). Infrastructure barriers and equity in AI-enabled personalized learning environments. Computers & Education: Artificial Intelligence, 7, 100312. https://doi.org/10.1016/j.caeai.2024.100312 [Google Scholar] [Crossref]

24. Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology, 45(3), 223–235. https://doi.org/10.1080/17439884.2020.1798995 [Google Scholar] [Crossref]

25. Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education—Where are the educators? International Journal of Educational Technology in Higher Education, 16(1), Article 39. https://doi.org/10.1186/s41239-019-0171-0 [Google Scholar] [Crossref]

26. Zhang, L., & Aslan, B. (2024). A systematic review of learning task design for K-12 AI education: Trends, challenges, and opportunities. Computers and Education: Artificial Intelligence, 6, 100217. https://doi.org/10.1016/j.caeai.2024.100217 [Google Scholar] [Crossref]

• Green Synthesis of Cobalt Oxide/Gold (Coo/Au) Bimetallic Nanoparticles Using Sinapinic Acid: A Comprehensive Study
• Advances in Solar Cell Technologies: A Comprehensive Review of Material Synthesis, Structural Properties, Efficiency and Diverse Applications
• Thermal Decomposition of Co-Fe-Cr-Citrate Complex Via Structural and Spectral Study
• Surface Activity and Thermodynamic Assessment of Surfactants Derived from Oreochromis Niloticus Oil (Nile Tilapia Fish)
• Green Synthesis of Robust Metal-Organic Frameworks: A Sustainable Approach for Advanced Applications