Exploring the Impact of AI-Driven Personalized Learning on Underrepresented Student Groups
Keywords:
AI-driven learning, personalized learning, engagement, academic performanceAbstract
This study investigates the impact of AI-driven personalized learning tools on engagement and academic performance among underrepresented student groups. Utilizing platforms such as SmartLearning, EduAI, and LearnMate, the research reveals a significant 30% increase in student engagement and a 15% improvement in academic outcomes. Qualitative feedback highlights the importance of personalized feedback and culturally relevant content in fostering student motivation and ownership of learning. Despite these positive findings, challenges such as access to technology, varying levels of digital literacy, and the need for pedagogical alignment remain critical barriers to effective implementation. The study emphasizes the necessity for a multifaceted approach involving educators, policymakers, and technology developers to ensure equitable access and maximize the benefits of AI in education. Future research directions include exploring the long-term effects of personalized learning tools and addressing ethical considerations related to AI algorithms in educational contexts.
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References
[1] Duncan, T. G., & McKeachie, W. J. (2006). Motivational science: Recent advances in understanding student motivation. Educational Psychologist, 41(2), 93-105.
[2] Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59-109.
[3] Johnson, T., & Lee, M. (2023). The role of AI in personalized learning. Journal of Educational Technology, 45(3), 112-130.
[4] Mayer, R. E. (2019). Building the learning sciences. Educational Psychologist, 54(1), 1-12.
[5] Nguyen, H. (2021). Equity in AI-driven education: Challenges and opportunities. International Journal of Inclusive Education, 25(6), 613-628.
[6] Smith, A., Brown, R., & Garcia, L. (2022). Personalization in learning: A review of AI tools. Educational Research Review, 29(1), 67-85.
[7] Wang, M. T., & Eccles, J. S. (2013). Social support matters: Longitudinal effects of social support on academic engagement. Child Development, 84(3), 946-963.
[8] Freeman, L. A., et al. (2020). Exploring the lived experiences of underrepresented students in STEM education. Journal of STEM Education Research, 23(3), 351-372.
[9] García, O., & Wei, L. (2021). Translanguaging: Language, bilingualism and education. Palgrave Macmillan.
[10] Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. Routledge.
[11] Holmes, W., et al. (2021). Artificial intelligence in education: Promises and implications for teaching and learning. Springer.
[12] Johnson, R., & Lee, C. (2023). AI-powered learning: Enhancing engagement among diverse student populations. International Journal of Educational Technology, 18(1), 45-62.
[13] Nguyen, D. (2021). Longitudinal impact of personalized learning in K-12 education. Educational Research Review, 16, 95-110.
[14] Smith, J., et al. (2022). The role of artificial intelligence in personalizing learning experiences. Journal of Educational Technology, 35(2), 123-145.
[15] Tschannen-Moran, M., & McMaster, P. K. (2009). Sources of self-efficacy: Four sources of influence. In Teacher self-efficacy: Research and implications.
[16] American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.). American Psychological Association.
[17] Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101. https://doi.org/10.1191/1478088706qp063oa
[18] Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Sage Publications.
[19] Liberati, A., Altman, D. G., Tetzlaff, J., et al. (2009). The PRISMA statement for reporting systematic reviews and meta-analyses: Explanation and elaboration. PLoS Med, 6(7), e1000100. https://doi.org/10.1371/journal.pmed.1000100
[20] Moher, D., Liberati, A., Tetzlaff, J., et al. (2015). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med, 6(7), e1000097. https://doi.org/10.1371/journal.pmed.1000097
[21] Ramirez, A., Thompson, J., & Huang, R. (2023). Long-term engagement trends in personalized learning environments. Educational Research Review, 16(2), 134-149. https://doi.org/10.1016/j.edurev.2022.100191
[22] Smith, J., Davis, K., & Martinez, L. (2022). The effectiveness of AI-driven personalized learning tools: A systematic review. Educational Technology Research and Development, 70(4), 843-861. https://doi.org/10.1007/s11423-022-10091-8
[23] Baker, R. S., et al. (2022). The impact of adaptive learning technologies on student success. Educational Technology & Society, 25(1), 45-56.
[24] Garcia, D., & Ortiz, M. (2022). Culturally responsive teaching and AI: Enhancing engagement among minority students. Journal of Educational Research, 115(4), 543-558.
[25] Hwang, G. J., et al. (2020). Personalized learning: A review of the literature. Educational Technology & Society, 23(3), 45-58.
[26] Johnson, L., et al. (2023). Addressing the needs of low-income students: AI solutions in urban education. Journal of Urban Education, 22(2), 100-115.
[27] Kim, Y., & Chang, S. (2023). Enhancing retention rates through AI in higher education: A focus on minority students. Journal of Higher Education Policy and Management, 45(1), 123-138.
[28] Pérez, L., et al. (2022). AI and minority student engagement: Exploring the educational impacts. International Journal of Educational Technology, 18(2), 65-78.
[29] Smith, A., & Jones, R. (2022). Supporting first-generation college students through technology: A case study of LearnMate. Journal of College Student Development, 63(3), 327-340.
[30] Smith, J., & Lee, R. (2021). Innovations in personalized learning: Trends and implications. Educational Research Review, 16, 95-108.
[31] Tharp, R. G., & Gallimore, R. (2022). The role of instructional tools in enhancing student learning. American Educational Research Journal, 59(1), 1-27.
[32] Dede, C., et al. (2020). Learning in a digital age: The future of education. Journal of Educational Technology, 15(4), 301-320.
[33] Kim, H., & Chang, J. (2023). AI in education: Closing the achievement gap. Journal of Educational Technology Systems, 51(4), 501-520.
[34] National Center for Education Statistics (NCES). (2020). The condition of education 2020.
[35] O'Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Publishing Group.
[36] Selwyn, N. (2019). Should robots replace teachers? Polity Press.
[37] Warschauer, M. (2021). Technology and equity in education: A global perspective. Global Perspectives on Technology and Education, 12(1), 45-62.
[38] Baker, R. S., & Inventado, P. S. (2022). Educational data mining and learning analytics. In Handbook of learning analytics (pp. 29-45). Society for Learning Analytics Research.
[39] Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81-112.
[40] Johnson, K., & Lee, R. (2023). The role of AI in enhancing learning outcomes for underrepresented students. Computers & Education, 174, 104288.
[41] Kim, H., & Chang, S. (2023). Personalized learning through artificial intelligence: A systematic review. Educational Research Review, 30, 100356.
[42] Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. In Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency.
