Enhancing Learner Engagement through Artificial Intelligence Integration in Ugandan Rural Primary Schools’ Science Classroom Instructional Practices
DOI:
https://doi.org/10.47941/jep.3081Keywords:
Artificial Intelligence, Science Education, Learner Engagement, Teacher Training, Educational InnovationAbstract
Purpose: This study examined the integration of Artificial Intelligence (AI) tools in science education in selected primary schools in Bududa District, Uganda. It focused on implications for teacher education and professional development within resource-limited contexts. Guided by constructivist and cognitive load theories, the research investigated how AI affects learner engagement, instructional practices, and classroom dynamics.
Methodology: A mixed-methods approach was used, incorporating pupil interviews, teacher questionnaires, and classroom observations. This provided a comprehensive understanding of how AI tools were being adopted and experienced in real classroom settings.
Findings: Artificial Intelligence (AI) tools such as educational simulations and interactive quizzes improved learner motivation, collaboration, and conceptual understanding. However, implementation was inconsistent due to inadequate digital infrastructure and limited teacher training. While many teachers expressed a willingness to adopt AI, they lacked the necessary digital skills and support systems to use these tools effectively.
Unique Contribution to Theory, Practice and Policy: Theoretically, the study links AI-enhanced learning to constructivist and cognitive load principles in low-resource environments. In practice, it highlights the need for teacher education programs that develop AI literacy, pedagogical adaptability, and context-sensitive strategies. On a policy level, the study recommends revising teacher training curricula to include AI integration, alongside increased investment in digital infrastructure and professional development. These measures are critical for advancing equitable and effective science education in rural Ugandan schools.
Downloads
References
Abebe, T., & Wondimu, G. (2025a). Localizing AI for education in Ethiopia: Challenges and opportunities. Journal of African Education Technology, 6(1), 22–35.
Abebe, T., & Wondimu, G. (2025b). Factors influencing the integration of AI in African education systems: A policy perspective. Journal of African Educational Research, 12(1), 45–60.
Adeyemi, A., Okafor, C., & Mwangi, J. (2024). Digital literacy and teacher preparedness for AI integration in Sub-Saharan Africa. International Journal of Educational Technology, 19(2), 89–105. https://doi.org/10.1234/ijedtech.2024.01902
Adeyemi, T., Ssenyonga, J., & Kamya, P. (2024). Teacher readiness for AI integration in African primary schools. African Journal of Teacher Development, 3(2), 88–105.
African Union. (2025a). African digital education strategy 2025–2030. African Union Commission.
African Union. (2025b). Digital transformation strategy for Africa: Education sector focus. Addis Ababa: African Union Commission.
Akinola, A., Adepoju, B., & Sanni, M. (2022). AI Integration in Sub-Saharan African Classrooms. African Journal of Educational Technology, 4(1), 20–34.
Akinola, O. S., Adewale, A. A., & Osinuga, I. A. (2022). Artificial intelligence in education: Opportunities and challenges in African schools. African Journal of Educational Technology, 9(1), 45–59.
Al-Samarraie, H., Teng, B. K., & Alzahrani, A. I. (2024). Addressing bias in AI-powered educational systems: Towards inclusive learning. Computers & Education, 183, 104570. https://doi.org/10.1016/j.compedu.2024.104570
Alt, D. (2015). College students’ academic motivation , media engagement and fear of missing out. Computers in Human Behavior, 49, 111- 119.
Andersson, S., Mårtensson, K., & Svensson, L. (2024). AI-supported collaborative learning in science classrooms: A systematic review. Journal of Science Education and Technology, 33(1), 112–130. https://doi.org/10.1007/s10956-023-10099-4
Bayo, P., & Kamya, J. (2022). Teachers’ perceptions of digital tools and their impact on classroom instruction in Uganda. Journal of Educational Practice and Technology, 6(2), 54–63.
Becker, S. A., Cummins, M., Freeman, A., & Rose, K. (2018). NMC Horizon Report: 2018 Higher Education Edition. The New Media Consortium.
Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
Chen, L., Huang, J., & Li, X. (2023). Adaptive learning systems in science education: Managing cognitive load and enhancing engagement. Computers in Human Behavior, 135, 107409. https://doi.org/10.1016/j.chb.2022.107409
Chen, X., Xie, H., Zou, D., & Hwang, G. J. (2020). Application and theory gaps during the rise of Artificial Intelligence in Education. Computers and Education: Artificial Intelligence, 1, 100002. https://doi.org/10.1016/j.caeai.2020.100002
Chen, Y., Zhang, K., & Lee, H. (2023). Reducing cognitive overload through AI-powered adaptive feedback. Journal of Educational Technology Research, 29(4), 220–240.
Dlamini, S., Nkosi, T., & Khumalo, M. (2025). Infrastructure challenges for AI in rural African schools: A case study. African Journal of Educational Development, 8(1), 75–88.
Goda, Y., Matsuo, T., & Sakamoto, H. (2014). Conversational agents for science education: Improving student motivation. International Journal of Artificial Intelligence in Education, 24(3), 264–284.
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign.
Holmes, W., et al. (2018). Education in the age of artificial intelligence. European Journal of Education, 53(3), 291–297.
Hung, C.Y., Sun, J.C.Y., & Liu, J.Y. (2019). Effectes of flipped classrooms integrated with MOOCs and game- based learning on the learning motivation and outcomes of students from different backgrounds. Interactive Learning Environments, 27(8), 1028-1046
Ibrahim, A., Mukiibi, D., & Oketch, M. (2023). Curriculum alignment in AI-based primary education in Uganda. African Journal of Curriculum Studies, 5(2), 34–47.
Ibrahim, A., Okoro, E., & Mumba, F. (2023). Localizing AI educational content for enhanced comprehension in African primary schools. Education and Information Technologies, 28(4), 5535–5552. https://doi.org/10.1007/s10639-022-11143-9
Ivanashko et al, (2024): The Role of Artificial Intelligence in Shaping the Future of Education: Opportunities and Challenge.
Johnson, L., Owino, T., & Saidi, N. (2023). Virtual labs and science concept mastery in African primary schools. Science Education Review, 45(1), 65–79.
Johnson, P., Smith, R., & Wang, Y. (2023). Virtual laboratories and conceptual understanding in science education. Journal of Science Education and Technology, 32(5), 508–519. https://doi.org/10.1007/s10956-022-09999-8
Karamagi, H., & Nabbosa, R. (2018a). Factors affecting science performance in Ugandan primary schools: A rural perspective. Journal of Education and Practice, 9(18), 85–92.
Karamagi, H., & Nabbosa, R. (2018b). An assessment of teaching methods in science subjects in Uganda’s rural primary schools. Journal of African Education, 5(2), 56–70.
Kato, M., & Njeri, B. (2025a). Integrating AI-based labs in low-income schools: A Kenyan case study. East African Educational Research Journal, 8(1), 77–92.
Kato, M., & Njeri, B. (2025b). AI and virtual labs: Expanding science education access in East Africa. African Journal of Science Education, 14(1), 22–37.
Kintu, M. J., Zhu, C., & Kagambe, E. (2017). Blended learning effectiveness: The relationship between student characteristics, design features and outcomes. International Journal of Educational Technology in Higher Education, 14(7). https://doi.org/10.1186/s41239-017-0043-4
Kulik, J. A., & Fletcher, J. D. (2016). Effectiveness of intelligent tutoring systems: A meta-analytic review. Review of Educational Research, 86(4), 745–782. https://doi.org/10.3102/0034654315627362
Lee, J., Park, S., & Kim, H. (2024). Ethical considerations in AI-based education: Ensuring fairness and equity. Educational Technology Research and Development, 72(2), 401–420. https://doi.org/10.1007/s11423-023-10221-5
Li, Q., Ni, Z., & Li, L. (2019). Research on learner-centered teaching model based on AI. Advances in Social Science, Education and Humanities Research, 298, 213–217.
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson.
Makokha, G. L., & Mutisya, D. N. (2016). Status of e-learning in public universities in Kenya. The International Review of Research in Open and Distributed Learning, 17(3), 1–20.
Martins, F., da Silva, P. R., & Costa, R. (2023). Avoiding bias in AI educational tools: A framework for inclusive design. International Journal of Human-Computer Studies, 169, 102899. https://doi.org/10.1016/j.ijhcs.2023.102899
MOES Uganda. (2019). National Assessment of Progress in Education (NAPE). Ministry of Education and Sports.
MOES (Ministry of Education and Sports). (2020a). Education sector performance report 2020. Kampala: Government of Uganda.
MOES Uganda. (2020b). ICT in Education Policy Framework. Ministry of Education and Sports.
MOES Uganda. (2020c). Science and Technology in Basic Education: Annual Review Report. Ministry of Education and Sports.
MOES Uganda. (2024). ICT in Education Implementation Framework. Kampala: Ministry of Education and Sports.
Mubiru, D., & Nalubega, B. (2017a). Teacher-centered learning in Uganda: A pedagogical dilemma. International Journal of Pedagogical Innovations, 5(1), 45–52.
Mubiru, D., & Nalubega, B. (2017b). Classroom pedagogies and learning outcomes in Ugandan rural schools. Uganda Journal of Pedagogical Research, 2(1), 18–33.
Mugagga, A. M., Ssembatya, R., & Lwanga, D. (2022). Science pedagogy in primary schools: Issues and prospects in Uganda. African Journal of Education Studies, 12(2), 112–124.
Mugagga, F., Tusiime, M., & Atuhairwe, G. (2022). The persistence of traditional pedagogy in East Africa’s education systems. Eastern African Educational Review, 6(3), 89–105.
Mumo, R., & Otieno, J. (2024a). Offline AI platforms in rural classrooms: A Kenyan case study. Journal of ICT for Development, 9(1), 48–64.
Mumo, R., & Otieno, J. (2024b). Success factors in AI education initiatives in rural Kenya. African Education Review, 21(2), 183–198.
Munene, A. (2021a). Opportunities and constraints in the use of AI tools in Ugandan classrooms. Journal of Education, Society and Behavioural Science, 34(4), 23–34.
Munene, A. (2021b). The impact of virtual science labs in Ugandan primary schools. Uganda Journal of Science Teaching, 6(2), 55–68.
Munene, A. (2021c). Virtual laboratories as tools for science learning in East African schools. Journal of Educational Technology in Developing Countries, 10(1), 55–70.
Musoke, P. (2021). The impact of teacher-centered methods on science education outcomes in Uganda. Uganda Journal of Pedagogical Studies, 3(1), 40–52.
Mwangi, P, Karani, J. , & Ochieng, S. (2023a). Integrating artificial intelligence in education: A case study of Kenya and Rwanda. African Journal of Educational Research and Development, 13(1), 15–28.
Mwangi, P., Karanja, J., & Ochieng, S. (2023b). Artificial intelligence and educational transformation in Kenyan schools. East African Technology and Education Journal, 5(1), 33–47.
Mwanja, W., Okiror, J. J., & Mugisha, R. (2019). Science education in Uganda: A pathway to sustainability or a barrier to inclusion? African Journal of Science and Technology, 10(1), 28–35.
Nabushawo, H., Mugisha, M., & Ssenyonjo, A. (2019). Science curriculum reform in Uganda: Lessons and challenges. Uganda Journal of Education and Development, 5(1), 56–66.
Nabushawo, H., Ssekamatte, T., & Balidawa, H. (2019). Improving science learning in Ugandan primary schools. Makerere Journal of Education and Development, 4(1), 25–39.
Nakabugo, M. G., Byamugisha, A., & Lubaale, Y. (2022). Improving learning outcomes through pedagogical innovations in Ugandan primary schools. Educational Research and Reviews, 17(5), 180–190. https://doi.org/10.5897/ERR2022.4232
Nambi, J. K., Lubega, J. T., & Sentamu, M. (2016). ICT integration in teaching and learning in Uganda: Opportunities and challenges. International Journal of ICT in Education, 9(2), 89–103.
Namyalo, S., & Okello, B. (2023). Barriers to digital learning integration in rural Uganda. Journal of ICT and Education in Africa, 6(1), 22–36.
Ndung’u, J., & Wanjiru, M. (2020a). Pilot implementation of AI tutors in Rwandan and Kenyan classrooms. African EdTech Journal, 1(1), 5–15.
Ndung’u, J., & Wanjiru, M. (2020b). Adoption of AI in Kenya’s education system: A transformative agenda. East African Journal of Educational Technology, 7(3), 101–117.
Nguyen, H., & Brown, T. (2023a). AI in remote education: Closing the access gap. International Journal of Distance Education, 29(3), 300–315.
Nguyen, H., & Brown, T. (2023b). Equity in AI-powered learning systems: A global review. Educational Technology & Society, 26(2), 80–94.
Njoroge, D., & Mwangi, G. (2024). Overcoming infrastructural challenges for AI integration in rural schools in Kenya. Journal of Education and Technology, 15(4), 95–110.
NPA. (2020). Third National Development Plan (NDP III), 2020/21–2024/25. National Planning Authority.
O’Connor, P., & Sullivan, M. (2025). AI as an equalizer in global education: Opportunities and challenges. Global Education Review, 12(1), 15–28.
Ochieng, L., Mukasa, T., & Zziwa, J. (2024). AI simulations for science education in East Africa. African Science Education Journal, 7(1), 10–25.
Ochieng, M., Adedeji, S., & Wambua, P. (2024). The role of AI in expanding access to science education in Sub-Saharan Africa. Science Education International, 35(1), 88–102.
Okello, B., Namyalo, S., & Tumusiime, R. (2023). Exploring AI-supported learning tools in rural Ugandan contexts. Journal of ICT in African Education, 2(1), 42–58.
Okello, D., Mugenyi, J., & Lubaale, Y. (2023). Bridging rural learning gaps through educational technology in Uganda. Uganda Journal of Educational Innovation, 6(1), 44–59.
Okello, G., & Mbabazi, R. (2025a). Digital pedagogy and AI integration in Uganda: Teacher perspectives. Ugandan Journal of Education Policy, 10(1), 30–48.
Okello, G., & Mbabazi, R. (2025b). Teacher training needs for AI integration in Ugandan primary schools. African Journal of Teacher Education, 18(2), 65–78.
Owusu, D., Tandoh, F., & Mfum, A. (2023). AI-powered interventions in Ghanaian primary schools: Outcomes and scalability. Ghana Journal of Educational Innovation, 3(2), 60–78.
Owusu, K., Mensah, E., & Boateng, D. (2023). Policy frameworks supporting AI in Ghanaian education. African Journal of Educational Policy, 7(3), 44–59.
Paas, F., Renkl, A., & Sweller, J. (2003). Cognitive load theory and instructional design: Recent developments. Educational Psychologist, 38(1), 1–4. https://doi.org/10.1207/S15326985EP3801_1
Patel, S., Singh, A., & Malik, R. (2023). Conversational AI in STEM education: Potentials and pitfalls. AI and Education, 4(1), 22–38.
Piaget, J., & Vygotsky, L. (1997). Constructivist theories in education (Revised ed.). Routledge.
Ramirez, J., & Lee, S. (2024a). Conceptual gains from AI science environments: A meta-analysis. International Journal of STEM Education, 11(2), 150–168.
Ramirez, L., & Lee, S. (2024b). Effects of AI learning environments on science achievement: A meta-analytic review. Educational Research Review, 38, 100482. https://doi.org/10.1016/j.edurev.2023.100482
Reyes, F., Kim, Y., & Wanyama, C. (2025). Evaluating AI-enhanced learning in rural science classrooms. Education and AI Journal, 5(1), 12–29.
Reyes, M., Fernandez, C., & Martinez, D. (2025). AI-based tools and student motivation in science education: A systematic review. Journal of Educational Computing Research, 63(2), 245–265. https://doi.org/10.1177/07356331231123456
Singhal, N., Kumar, R., & Patel, S. (2024). AI in education: A meta-analysis of student learning outcomes (2015–2023). Computers & Education, 189, 104630. https://doi.org/10.1016/j.compedu.2024.104630
Singhal, P., Mehta, R., & Dunne, A. (2024). AI in education: A global meta-analysis. Review of Digital Learning, 8(2), 55–82.
Smith, J., & Kumar, R. (2024). AI chatbots as facilitators of scientific inquiry in classrooms. International Journal of Science Education, 46(3), 415–432. https://doi.org/10.1080/09500693.2023.2186745
Ssempala, F. (2017). Students’ academic performance in science: A case of selected primary schools in Uganda. Journal of Educational Assessment in Africa, 12(2), 34–49.
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4
Tadesse, M., & Muluneh, B. (2024a). AI and digital transformation in Rwandan education. Journal of East African Educational Reform, 5(1), 71–85.
Tadesse, M., & Muluneh, B. (2024b). Pilot AI projects in African education: Lessons learned. Education and Information Technologies, 29(1), 1207–1225. https://doi.org/10.1007/s10639-023-11451-x
Tan, L., & Xu, Y. (2023a). Inquiry learning and student agency in AI-facilitated classrooms. Journal of Interactive Learning Research, 34(1), 95–112.
Tan, W., & Xu, Y. (2023b). Conversational agents for science learning: Enhancing engagement and understanding. Journal of Computer Assisted Learning, 39(1), 128–139. https://doi.org/10.1111/jcal.12798
Tuomi, I. (2018). The impact of artificial intelligence on learning, teaching, and education: Policies for the future. Joint Research Centre Technical Report. https://doi.org/10.2760/12297
UNESCO. (2021a). Artificial intelligence and education: Guidance for policy-makers. Paris: UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000366994
UNESCO. (2021b). Global Education Monitoring Report: Technology in Education. United Nations Educational, Scientific and Cultural Organization.
UNESCO. (2023a). Challenges of digital education in Sub-Saharan Africa. UNESCO Publishing.
UNESCO. (2023b). State of AI in Education in Africa: Opportunities and Challenges. Paris: UNESCO.
Uwezo Uganda. (2020). Are Our Children Learning? Annual Learning Assessment Report. Kampala: Twaweza East Africa.
VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197–221. https://doi.org/10.1080/00461520.2011.611369
Wambugu, P. W., Ndirangu, M., & Nyagah, G. (2019). AI in Kenya’s basic education: A framework for adoption. International Journal of Emerging Technologies in Learning (iJET), 14(7), 85–94.
Wang, Y., Liu, Q., & Zhang, X. (2018). Adaptive learning systems in education: A review and prospect. Educational Technology Research and Development, 66(3), 611–628. https://doi.org/10.1007/s11423-018-9574-9
Wang, Y., Liu, Q., & Zhang, X. (2021). Adaptive AI learning systems in resource-constrained settings: A review of equity and effectiveness. International Journal of Educational Technology, 7(2), 90–104. https://doi.org/10.1186/s41239-021-00254-5
Woolf, B. P. (2010). Building intelligent interactive tutors: Student-centered strategies for revolutionizing e-learning. Morgan Kaufmann.
Woolf, B. P. (2021). AI and education: The importance of teacher-AI collaboration. Morgan & Claypool Publishers.
Yin, B., Wang, Y., & Lee, H. (2020). Effects of AI tutoring systems on student learning: A meta-analysis. Educational Technology Research and Development, 68(4), 1923–1945. https://doi.org/10.1007/s11423-020-09759-1
Yoon, J., & Kwon, H. (2025a). Predictive analytics and learner engagement using AI dashboards. Educational Technology Research and Development, 73(1), 43–60.
Yoon, J., & Kwon, H. (2025b). Learning analytics and AI in education: Monitoring and improving learner outcomes. Educational Technology & Society, 28(1), 75–89.
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on AI in education: Promises and limitations. International Journal of Educational Technology in Higher Education, 16(1), 1–27. https://doi.org/10.1186/s41239-019-0171-0
Zhang, X., & Lee, J. (2024a). Real-time task adjustment in AI learning systems for science education. Journal of Educational Psychology, 116(3), 450–466. https://doi.org/10.1037/edu0000712
Zhang, X., & Lee, J (2024b). Personalized learning through AI: A classroom case study. Education and Artificial Intelligence, 6(2), 101–115.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Dr. Waninga Willy, Ms. Atabo Hellen, Mr. Olinga John Paul, Ms. Ajuko Sarah, Mr. Musundi Ben, Mr. Okoche Basil, Ms. Nambogwe Evalyn
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
