The PearlAI Framework: Leveraging Artificial Intelligence to Transform Education in Africa
DOI:
https://doi.org/10.32628/CSEIT25113581Keywords:
PearlAI, artificial intelligence, Africa, education, formative assessment, mother-tongue instruction, retrieval practice, equityAbstract
Africa’s education systems face chronic challenges of access, equity, quality, and relevance, exacerbated by underfunding, insecurity, governance failures, and structural inequalities. Traditional interventions have yielded incremental progress, yet millions of children remain out of school, and most learners fail to achieve foundational literacy and numeracy. In this context, PearlAI is proposed as a non-profit, open-source framework designed to leverage artificial intelligence (AI) to systematically address the perennial barriers facing education in Africa. The PearlAI Framework integrates six solution domains: Access & Equity, Teacher Workforce, Literacy & Numeracy, Curriculum & Relevance, Assessment & Evaluation, and Student Engagement & Motivation. Grounded in academic literature on mother-tongue instruction, formative assessment, retrieval practice, culturally responsive pedagogy, and AI-driven feedback systems, PearlAI provides both a comprehensive toolkit for system-wide adoption and modular tools for context-specific deployment. This paper synthesizes evidence from African and global education research to articulate the design principles, operational model, and impact pathways of PearlAI. The framework aims to democratize access to AI, improve learning outcomes, and foster innovation across African schools while contributing globally to open educational technologies.
Downloads
References
Aker, J. C., Ksoll, C., & Lybbert, T. J. (2020). Can mobile phones improve learning? Evidence from a field experiment in Niger. American Economic Journal: Applied Economics, 12(1), 1–40.
Azevedo, R., Mudrick, N. V., Taub, M., Wortha, F., & Millar, G. C. (2019). Self-regulation in computer-assisted learning systems. Educational Psychologist, 54(3), 257–277.
Banerjee, A. V., Cole, S., Duflo, E., & Linden, L. (2007). Remedying education: Evidence from two randomized experiments in India. Quarterly Journal of Economics, 122(3), 1235–1264. DOI: https://doi.org/10.1162/qjec.122.3.1235
Black, P., & Wiliam, D. (1998). Assessment and classroom learning. Assessment in Education: Principles, Policy & Practice, 5(1), 7–74. DOI: https://doi.org/10.1080/0969595980050102
Bloom, B. S. (1984). The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational Researcher, 13(6), 4–16. DOI: https://doi.org/10.3102/0013189X013006004
Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. DOI: https://doi.org/10.1109/ACCESS.2020.2988510
Cristia, J., Czerwonko, A., & Garofalo, P. (2017). Does technology in schools affect repetition, dropout and enrollment? Evidence from Peru’s One Laptop per Child program. Journal of Development Economics, 130, 271–286.
Darling-Hammond, L., Hyler, M. E., & Gardner, M. (2017). Effective teacher professional development. Palo Alto, CA: Learning Policy Institute. DOI: https://doi.org/10.54300/122.311
Dettmers, T., Lewis, M., Belkada, Y., & Zettlemoyer, L. (2023). QLoRA: Efficient finetuning of quantized LLMs. Proceedings of the 40th International Conference on Machine Learning (ICML).
D’Mello, S. K., & Graesser, A. C. (2015). Feeling, thinking, and computing with affect-aware learning technologies. International Journal of Artificial Intelligence in Education, 25(2), 205–210.
Ertmer, P. A., & Ottenbreit-Leftwich, A. (2010). Teacher technology change: How knowledge, confidence, beliefs, and culture intersect. Journal of Research on Technology in Education, 42(3), 255–284. DOI: https://doi.org/10.1080/15391523.2010.10782551
Gao, Y., Lin, Y., Li, C., & Zhang, J. (2023). AI-driven formative assessment in education: A systematic review. Computers & Education, 193, 104672.
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
Holmes, W., Porayska-Pomsta, K., & Holstein, K. (2021). Ethics in AI in education: Towards a community-wide framework. British Journal of Educational Technology, 52(4), 1631–1648.
Holstein, K., McLaren, B. M., & Aleven, V. (2019). Co-designing a real-time classroom orchestration tool to support teacher–AI complementarity. Journal of Learning Analytics, 6(2), 27–52. DOI: https://doi.org/10.18608/jla.2019.62.3
Kioko, A., Ndung’u, R., Njoroge, M., & Orwenjo, D. (2014). Language and education in Africa: Lessons from the African Union’s Language Plan of Action. Journal of Language, Identity & Education, 13(1), 1–15.
Kulik, C. L., & Kulik, J. A. (1990). Effectiveness of computer-based instruction: An updated analysis. Computers in Human Behavior, 7(1-2), 75–94. DOI: https://doi.org/10.1016/0747-5632(91)90030-5
Madise, Ü., & Martens, T. (2006). E-voting in Estonia 2005: The first practice of country-wide binding Internet voting in the world. Electronic Voting 2006, 15–26.
Mandyata, J. (2024). Language policy and reading outcomes in Sub-Saharan Africa: Challenges and opportunities. International Review of Education, 70(1), 45–64.
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & The PRISMA Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine, 6(7), e1000097. DOI: https://doi.org/10.1371/journal.pmed.1000097
Mohohlwane, N., Taylor, S., & Cilliers, J. (2023). Early grade reading in South Africa: Evidence from three randomized evaluations. Journal of Development Economics, 160, 102–118.
Mtebe, J. S., & Raisamo, R. (2014). Challenges and instructors’ intention to adopt and use open educational resources in higher education in Tanzania. International Review of Research in Open and Distributed Learning, 15(1), 249–271. DOI: https://doi.org/10.19173/irrodl.v15i1.1687
Muralidharan, K., Singh, A., & Ganimian, A. J. (2019). Disrupting education? Experimental evidence on technology-aided instruction in India. American Economic Review, 109(4), 1426–1460. DOI: https://doi.org/10.1257/aer.20171112
Pastötter, B., & Bäuml, K. H. T. (2014). Retrieval practice enhances new learning: The forward effect of testing. Frontiers in Psychology, 5, 286. DOI: https://doi.org/10.3389/fpsyg.2014.00286
Prompiengchai, N., Li, X., & Wu, T. (2025). Generative AI in formative assessment: Opportunities and challenges. Computers & Education, 215, 104808.
Roschelle, J., Feng, M., Murphy, R. F., & Mason, C. A. (2016). Online mathematics homework increases student achievement. AERA Open, 2(4), 1–12. DOI: https://doi.org/10.1177/2332858416673968
Selwyn, N. (2021). Should robots replace teachers? AI and the future of education. Polity Press.
Severin, E., & Capota, C. (2011). The experience of Plan Ceibal in Uruguay. CEPAL Review, 104, 47–63.
Touvron, H., Lavril, T., Izacard, G., et al. (2023). LLaMA: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971.
Trucano, M. (2016). Evaluating the impact of ICTs in education. World Bank.
Unwin, T., Abrahams, L., Kleessen, B., & Weber, M. (2020). Digital learning in Africa: Challenges, opportunities and perspectives. International Journal of Educational Development, 74, 102159.
van Pinxteren, M. (2022). The impact of language of instruction in sub-Saharan Africa: A systematic review. International Journal of Educational Research, 114, 101996.
Wasonga, T. A., Ndichu, D., & Mboroki, G. (2020). Kenya’s Digital Literacy Programme: Achievements and challenges. International Journal of Education and Development using ICT, 16(2), 50–64.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 International Journal of Scientific Research in Computer Science, Engineering and Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
