10 Broadening Applications of Artificial Intelligence in Education to Include Indigenous Ways of Knowing

Applications for artificial intelligence in education (AIEd) have great potential for improving learning and instruction for learners in STEM education (Xu & Ouyang, 2022; Yang, 2022). Such improvements have the potential to lead to increased participation of learners from historically underrepresented groups in STEM fields. Nevertheless, current applications of AI can often be designed in ways that create barriers for learners rather than opportunities. For example, AI technologies have been known to encourage algorithmic biases (Drozdowski et al., 2020, Obermeyer et al., 2019). Additionally, scholars caution that AIEd may compound existing inequities that many learners from historically underrepresented groups already face (e.g., Roschelle et al., 2020). For instance, Druga and colleagues (2020) found that while students from lower socioeconomic status (SES) backgrounds were more likely to engage in collaboration, they were less likely to advance their technological skills as compared to students from higher SES backgrounds (which was partly due to students from lower SES backgrounds having decreased previous exposure to coding and AI technologies). As such, scholars must broaden our current conversations of applications for AIEd to account for ways in which we can promote digital equity for all. One of the ways in which we can acknowledge the possibilities of AIEd is to recognize the limitations of purely studying westernized instantiations of AIEd. One such limitation is the current gap in our understanding of what AIEd applications look like in Indigenous contexts.

While American Indians/Alaskan Natives (AI/ANs) comprise two percent of the U.S. population, they make up only 0.2 percent of those employed in science and engineering occupations, meaning AI/ANs are significantly underrepresented in scientific and technological fields (National Center for Science and Engineering Statistics, 2017). Many AI/ANs feel disconnected from their cultural backgrounds and struggle to see themselves belonging in school environments where they are a small minority. Beyond that, many science learning activities focus on westernized perspectives which disregards other ways of knowing and does not account for science learning as a cultural activity (Barajas-López & Bang, 2018). Moreover, students are often encouraged to utilize one way of knowing over the other. Instead of having students choose between western and Indigenous methods, we should instead envision more collaborative education experiences that not only acknowledge and encourage students to utilize multiple scientific knowledge systems in a complementary way (Brayboy & Maughan, 2009), but we must also provide students with tools for navigating across diverse knowledge systems (Bang & Medin, 2010).

The landscape of AIEd continues to evolve as a result of digital tools and technologies becoming more accessible to the general public. However, this particular notion of technological accessibility solely driving the AIEd movement in new directions provides a somewhat narrow view of what AIED is and could be. Just as AI technology accessibility is said to have led to an “educational renaissance,” a second re-awakening could be realized if we were to further examine technological accessibility in terms of creating equitable and barrier-free learning opportunities for learners as they engage with AI technologies. Given the potential impacts of AIED to engage students in STEM fields, a logical next step for the future of AIEd might be to embrace Indigenous perspectives, histories, and futures of various technologies including AI. But first, we must ask ourselves: Can scholars and practitioners work toward a common goal of bridging Indigenous ways of knowing and being with more western forms of AIEd? And if so, what would that look like, and how could we navigate this process in ways that mutually benefit one another?

References

Bang, M., & Medin, D. (2010). Cultural processes in science education: Supporting the navigation of multiple epistemologies. Science Education, 94(6), 1008-1026.

Barajas-López, F., & Bang, M. (2018). Indigenous making and sharing: Claywork in an Indigenous STEAM program. Equity and Excellence in Education, 51(1), 7-20. https://doi.org/10.1080/10665684.2018.1437847

Brayboy, B. M., & Maughan, E. (2009). Indigenous knowledges and the story of the bean. Harvard Educational Review, 79(1), 1-21.

Drozdowski, P., Rathgeb, C., Dantcheva, A., Damer, N., & Busch, C. (2020). Demographic bias in biometrics: A survey on an emerging challenge. IEEE Transactions on Technology and Society, 1(2), 89–103. https://doi.org/10.1109/TTS.2020.2992344

Druga, S., Vu, S. T., Likhith, E., & Qiu, T. (2019). Inclusive AI literacy for kids around the world. In Proceedings of FabLearn 2019 (pp. 104-111).

National Center for Science and Engineering Statistics (NCSES). 2023. Diversity and STEM: Women, Minorities, and Persons with Disabilities 2023. Special Report NSF 23-315. Alexandria, VA: National Science Foundation. Available at https://ncses.nsf.gov/wmpd.

Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453. https://doi.org/10.1126/science.aax2342

Roschelle, J., Lester, J., & Fusco, J. (2020). AI and the Future of Learning: Expert Panel Report. Digital Promise.

Roscoe, R. D., Salehi, S., Nixon, N., Worsley, M., Piech, C., & Luckin, R. (2022). Inclusion and equity as a paradigm shift for artificial intelligence in education. In F. Ouyang, P. Jiao, B. M. McLaren, A. H. Alavi (Eds.), Artificial intelligence in STEM education: The paradigmatic shifts in research, education, and technology (pp. 359-373). CRC Press.

Xu, W., & Ouyang, F. (2022). The application of AI technologies in STEM education: A systematic review from 2011 to 2021. International Journal of STEM Education, 9(1), 1-20.

Yang, W. (2022). Artificial intelligence education for young children: Why, what, and how in curriculum design and implementation. Computers and Education: Artificial Intelligence, 3, 100061.