AfriML: A Culturally Relevant AI Tool for Teaching Machine Learning Concepts
DOI:
https://doi.org/10.46328/ijtes.5628Keywords:
Machine learning education, Adaptive learning, Higher education, Multiculturalism, Educational technologyAbstract
This paper introduces AfriML, a culturally relevant artificial intelligence tool designed to enhance machine learning (ML) education through African cultural contexts. While tools like Teachable Machine and LearningML simplify ML concepts, they often lack cultural integration. AfriML addresses this gap by using artifacts from major Nigerian ethnic groups (Hausa, Igbo, Yoruba, Ibibio, and Efik) to teach ML classification problems. The tool was implemented with students at a tertiary institution and evaluated using a survey to assess learners’ confidence, satisfaction, and perceptions of cultural relevance. Results showed a significant increase in confidence scores from 2.43 to 3.28. Participants also reported improved understanding of ML classification (mean = 3.09) and rated the tool's effectiveness at 3.36. Cultural relevance was rated positively (mean = 3.45). These findings suggest that AfriML supports both conceptual understanding and cultural engagement in ML education. Future work will expand AfriML’s cultural scope and its impact across broader educational contexts.
References
Afifah, W., & Zuchdi, D. (2018). Developing Culture-Based English Instructional Materials for Grade VII of Junior High School Students. English Language Teaching Educational Journal, 1(2), 76–96.
Algahtani, A. (2024). A Comparative Study of AI-Based Educational Tools: Evaluating User Interface Experience and Educational Impact. Journal of Theoretical and Applied Information Technology, 102(5).
Aliabadi, R., Singh, A., & Wilson, E. (2023). Transdisciplinary AI education: The confluence of curricular and community needs in the instruction of artificial intelligence. International Conference on Artificial Intelligence in Education Technology, 137–151.
Alturayeif, N., Alturaief, N., & Alhathloul, Z. (2020). DeepScratch: Scratch programming language extension for deep learning education. International Journal of Advanced Computer Science and Applications, 11(7).
Au, K. H. (2007). Culturally responsive instruction: Application to multiethnic classrooms. Pedagogies: An International Journal, 2(1), 1–18.
Barua, P. D., Vicnesh, J., Gururajan, R., Oh, S. L., Palmer, E., Azizan, M. M., Kadri, N. A., & Acharya, U. R. (2022). Artificial intelligence enabled personalised assistive tools to enhance education of children with neurodevelopmental disorders—A review. International Journal of Environmental Research and Public Health, 19(3), 1192.
Berti, M. (2021). The unexplored potential of virtual reality for cultural learning. The EuroCALL Review, 29(1), 60–67.
Boateng, G. (2024). Leveraging AI to Advance Science and Computing Education across Africa: Challenges, Progress and Opportunities. arXiv Preprint arXiv:2402.07397.
Brion, C. (2022). The use culturally proficient professional development to enhance learning transfer. Journal of School Leadership, 32(4), 319–338.
Brown, B. A., Boda, P., Lemmi, C., & Monroe, X. (2019). Moving culturally relevant pedagogy from theory to practice: Exploring teachers’ application of culturally relevant education in science and mathematics. Urban Education, 54(6), 775–803.
Brown, B., Pérez, G., Ribay, K., Boda, P. A., & Wilsey, M. (2021). Teaching culturally relevant science in virtual reality:“When a problem comes, you can solve it with science.” Journal of Science Teacher Education, 32(1), 7–38.
Carney, M., Webster, B., Alvarado, I., Phillips, K., Howell, N., Griffith, J., Jongejan, J., Pitaru, A., & Chen, A. (2020). Teachable machine: Approachable Web-based tool for exploring machine learning classification. Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, 1–8.
Casillo, M., Colace, F., Gupta, B. B., Lorusso, A., Santaniello, D., & Valentino, C. (2024). The Role of AI in Improving Interaction with Cultural Heritage: An Overview. Handbook of Research on AI and ML for Intelligent Machines and Systems, 107–136.
Cen, C., Luo, G., Li, L., Liang, Y., Li, K., Jiang, T., & Xiong, Q. (2023). User-Centered Software Design: User Interface Redesign for Blockly–Electron, Artificial Intelligence Educational Software for Primary and Secondary Schools. Sustainability, 15(6), 5232.
Chu, J. H., & Mazalek, A. (2019). Embodied engagement with narrative: A design framework for presenting cultural heritage artifacts. Multimodal Technologies and Interaction, 3(1), 1.
de Oliveira, F. P., von Wangenheim, C. G., & Hauck, J. C. (2022). TMIC: App Inventor Extension for the Deployment of Image Classification Models Exported from Teachable Machine. arXiv Preprint arXiv:2208.12637.
Druga, S., Otero, N., & Ko, A. J. (2022). The landscape of teaching resources for ai education. Proceedings of the 27th ACM Conference on on Innovation and Technology in Computer Science Education Vol. 1, 96–102.
Edwards-Fapohunda, M. O., & Adediji, M. A. (2024). Sustainable development of distance learning in continuing adult education: The impact of artificial intelligence. IRE Journals, 8(1), 113–114.
García, J. D. R., Moreno-León, J., Román-González, M., & Robles, G. (2020). LearningML: a tool to foster computational thinking skills through practical artificial intelligence projects. Revista de Educación a Distancia (RED), 20(63).
Gresse von Wangenheim, C., Hauck, J. C., Pacheco, F. S., & Bertonceli Bueno, M. F. (2021). Visual tools for teaching machine learning in K-12: A ten-year systematic mapping. Education and Information Technologies, 26(5), 5733–5778.
Kaswan, K. S., Dhatterwal, J. S., & Ojha, R. P. (2024). AI in personalized learning. In Advances in Technological Innovations in Higher Education (pp. 103–117). CRC Press.
Kelly, A., Sullivan, M., & Strampel, K. (2023). Generative artificial intelligence: University student awareness, experience, and confidence in use across disciplines. Journal of University Teaching and Learning Practice, 20(6), 1–16.
Kieran, L., & Anderson, C. (2019). Connecting universal design for learning with culturally responsive teaching. Education and Urban Society, 51(9), 1202–1216.
Kurz, T. L., Jayasuriya, S., Swisher, K., Mativo, J., Pidaparti, R., & Robinson, D. T. (2024). The Impact of Teachable Machine on Middle School Teachers’ Perceptions of Science Lessons after Professional Development. Education Sciences, 14(4), 417.
Likourezos, V., Kalyuga, S., & Sweller, J. (2019). The variability effect: When instructional variability is advantageous. Educational Psychology Review, 31, 479–497.
Mahipal, V., Ghosh, S., Sanusi, I. T., Ma, R., Gonzales, J. E., & Martin, F. G. (2023). DoodleIt: A novel tool and approach for teaching how CNNs perform image recognition. Proceedings of the 25th Australasian Computing Education Conference, 31–38.
Martin, F., Mahipal, V., Jain, G., Ghosh, S., & Sanusi, I. T. (2024). ChemAIstry: A Novel Software Tool for Teaching Model Training in K-8 Education. Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1, 792–798.
Miller, O. (2014). It’s deeper than rap, toward culturally responsive CS. XRDS: Crossroads, The ACM Magazine for Students, 20(4), 28–30.
Mogavi, R. H., Deng, C., Kim, J. J., Zhou, P., Kwon, Y. D., Metwally, A. H. S., Tlili, A., Bassanelli, S., Bucchiarone, A., Gujar, S., & others. (2023). Exploring user perspectives on chatgpt: Applications, perceptions, and implications for ai-integrated education. arXiv Preprint arXiv:2305.13114.
Moremoholo, T. P. (2023). The role of culture in shaping the curriculum of higher education in South Africa. Journal of Curriculum Studies Research, 5(2), 37–55.
Ober, T. M., Lehman, B. A., Gooch, R., Oluwalana, O., Solyst, J., Phelps, G., & Hamilton, L. S. (2023). Culturally responsive personalized learning: Recommendations for a working definition and framework. ETS Research Report Series, 2023(1), 1–14.
Oyelere, S. S., Sanusi, I. T., Agbo, F. J., Oyelere, A. S., Omidiora, J. O., Adewumi, A. E., & Ogbebor, C. (2022). Artificial intelligence in African schools: Towards a contextualized approach. 2022 IEEE Global Engineering Education Conference (EDUCON), 1577–1582.
Peffers, K., Tuunanen, T., Gengler, C. E., Rossi, M., Hui, W., Virtanen, V., & Bragge, J. (2020). Design science research process: A model for producing and presenting information systems research. arXiv Preprint arXiv:2006.02763.
Prabhakaran, V., Qadri, R., & Hutchinson, B. (2022). Cultural incongruencies in artificial intelligence. arXiv Preprint arXiv:2211.13069.
Rao, A., Bihani, A., & Nair, M. (2018). Milo: A visual programming environment for data science education. 2018 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), 211–215.
Samuel, Y., Brennan-Tonetta, M., Samuel, J., Kashyap, R., Kumar, V., Krishna Kaashyap, S., Chidipothu, N., Anand, I., & Jain, P. (2023). Cultivation of human centered artificial intelligence: Culturally adaptive thinking in education (CATE) for AI. Frontiers in Artificial Intelligence, 6, 1198180.
Sanusi, I. T., & Oyelere, S. S. (2020). Pedagogies of machine learning in K-12 context. 2020 IEEE Frontiers in Education Conference (FIE), 1–8.
Shao, Y., Zhang, C., Zhou, J., Gu, T., & Yuan, Y. (2019). How does culture shape creativity? A mini-review. Frontiers in Psychology, 10, 1219.
Vadivel, V., Song, I., & Bhati, A. S. (2020). Culturally themed educational tools for enhancing learning in Southeast Asian secondary schools. Proceedings of the Future Technologies Conference (FTC) 2020, Volume 1, 950–968.
Smilkov, D., Thorat, N., Assogba, Y., Nicholson, C., Kreeger, N., Yu, P., Cai, S., Nielsen, E., Soegel, D., Bileschi, S., & others. (2019). Tensorflow. Js: Machine learning for the web and beyond. Proceedings of Machine Learning and Systems, 1, 309–321.
Vlachogianni, P., & Tselios, N. (2022). Perceived usability evaluation of educational technology using the System Usability Scale (SUS): A systematic review. Journal of Research on Technology in Education, 54(3), 392–409.
Vom Brocke, J., Hevner, A. R., & Maedche, A. (2020). Design science research: Cases. Springer.
Yu, J., & Denham, A. (2019). Analyzing the effects of a culturally relevant augmented reality math board game on Lakota students’ arithmetic performances: A case study. E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, 606–610.
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), 1–27.
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