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Harnessing AI to Foster Equity in Education: Opportunities, Challenges, and Emerging Strategies

DOI: 10.4236/jilsa.2023.154009, PP. 123-143

Keywords: Artificial Intelligence, Education, Equity, And Inclusivity

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In contemporary educational landscapes, Artificial Intelligence (AI) has emerged as a pivotal tool to promote equity and inclusivity. One of the most significant contributions of AI is its ability to facilitate personalized learning. Through the analysis of a student’s learning patterns, strengths, and weaknesses, AI-driven platforms can customize educational content, ensuring that each student receives instruction tailored to their individual needs. This personalization ensures that all students, regardless of their starting point, have an equal opportunity to progress and excel. This paper explores the utilization of AI in facilitating an equitable educational environment by analyzing the opportunities, challenges, and strategies pertinent to AI implementation. Through a comprehensive review of the current literature and case studies, this paper identifies promising avenues for leveraging AI to bridge educational gaps while also highlighting potential pitfalls and barriers to equity. This paper proposes actionable strategies and recommendations for stakeholders to cultivate an educational ecosystem that champions equity through the prudent integration of AI technology.


[1]  Kerr, A., Kalendra, D., Marchand, J., Wijeratne, A. and Wegner, D. (2017) From [Virtual] Classroom to Boardroom: Coaching Students to Use a Research Approach to Address Contemporary Issues in Their Workplace. Proceedings of 35th International Conference of Innovation, Practice and Research in the Use of Educational Technologies in Tertiary Education, Geelong, 25-28 November 2018, 440-445.
[2]  Pedro, F., Subosa, M., Rivas, A. and Valverde, P. (2019) Artificial Intelligence in Education: Challenges and Opportunities for Sustainable Development.
[3]  Chen, L., Chen, P. and Lin, Z. (2020) Artificial Intelligence in Education: A Review. IEEE Access, 8, 75264-75278.
[4]  Seo, K., Tang, J., Roll, I., et al. (2021) The Impact of Artificial Intelligence on Learner—Instructor Interaction in Online Learning. International Journal of Educational Technology in Higher Education, 18, Article No. 54.
[5]  Schiff, D. (2021) Out of the Laboratory and into the Classroom: The Future of Artificial Intelligence in Education. AI & Society, 36, 331-348.
[6]  EDPS: European Data Protection Supervisor (2022) Artificial Intelligence, Human Rights, Democracy and the Rule of Law.
[7]  American Psychological Association (2023) Socioeconomic Status. APA Dictionary of Psychology.
[8]  Freire, P. (2020) Pedagogy of the Oppressed. Routledge, London, 374-386.
[9]  Jayakumar, U.M. and Page, S.E. (2021) Cultural Capital and Opportunities for Exceptionalism: Bias in University Admissions. The Journal of Higher Education, 92, 1109-1139.
[10]  Gruber, M. and Benedikter, R. (2021) The Role of Women in Contemporary Technology and the Feminization of Artificial Intelligence and Its Devices. Springer, Cham, 17-38.
[11]  Toupin, S. (2023) Shaping Feminist Artificial Intelligence. New Media & Society.
[12]  Kontokosta, C.E. and Hong, B. (2021) Bias in Smart City Governance: How Socio-Spatial Disparities in 311 Complaint Behavior Impact the Fairness of Data-Driven Decisions. Sustainable Cities and Society, 64, Article ID: 102503.
[13]  Yemini, M., Engel, L. and Ben Simon, A. (2023) Place-Based Education—A Systematic Review of Literature. Educational Review.
[14]  Roiha, A. and Sommier, M. (2021) Exploring Teachers’ Perceptions and Practices of Intercultural Education in an International School. Intercultural Education, 32, 446-463.
[15]  Yang, W. (2022) Artificial Intelligence Education for Young Children: Why, What, and How in Curriculum Design and Implementation. Computers and Education: Artificial Intelligence, 3, Article ID: 100061.
[16]  Nguyen, A., Ngo, H.N., Hong, Y., Dang, B. and Nguyen, B.P.T. (2023) Ethical Principles for Artificial Intelligence in Education. Education and Information Technologies, 28, 4221-4241.
[17]  Taye, M.M. (2023) Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions. Computers, 12, Article 91.
[18]  Srinivasa, K.G., Kurni, M. and Saritha, K. (2022) Adaptive Teaching/Learning. In: Srinivasa, K.G., Kurni, M. and Saritha, K., Eds., Learning, Teaching, and Assessment Methods for Contemporary Learners, Springer, Singapore, 201-240.
[19]  Ochoa, X., Echeverria, V., Carrillo, G., Heredia, V. and Chiluiza, K. (2023) Supporting Online Collaborative Work at Scale: A Mixed-Methods Study of a Learning Analytics Tool. Proceedings of the Tenth ACM Conference on Learning, Copenhagen, 20-22 July 2023, 237-247.
[20]  Ahmad, K., Qadir, J., Al-Fuqaha, A., Iqbal, W., El-Hassan, A., Benhaddou, D. and Ayyash, M. (2020, June 19). Data-Driven Artificial Intelligence in Education: A Comprehensive Review.
[21]  Kakish, K., Robertson, C. and Pollacia, L. (2022) Advancing Adaptive Learning via Artificial Intelligence. In: Arai, K., Ed., IntelliSys 2021: Intelligent Systems and Applications, Springer, Cham, 691-718.
[22]  Badal, Y.T, and Sungkur, R.K. (2023) Predictive Modelling and Analytics of Students’ Grades Using Machine Learning Algorithms. Education and Information Technologies, 28, 3027-3057.
[23]  Kearney, C.A., Dupont, R., Fensken, M. and Gonzálvez, C. (2023) School Attendance Problems and Absenteeism as Early Warning Signals: Review and Implications for Health-Based Protocols and School-Based Practices. Frontiers in Education, 8, Article ID: 1253595.
[24]  Bradley, V.M. (2021) Learning Management System (LMS) Use with Online Instruction. International Journal of Technology in Education, 4, 68-92.
[25]  Ma, Y., Wang, L., Zhang, J., Liu, F. and Jiang, Q. (2023) A Personalized Learning Path Recommendation Method Incorporating Multi-Algorithm. Applied Sciences, 13, Article No. 5946.
[26]  Minn, S. (2022) AI-Assisted Knowledge Assessment Techniques for Adaptive Learning Environments. Computers and Education: Artificial Intelligence, 3, Article ID: 100050.
[27]  U.S. Department of Education and Office of Educational Technology (2023) Artificial Intelligence and Future of Teaching and Learning: Insights and Recommendations. Washington DC.
[28]  Bauer, E., Greisel, M., Kuznetsov, I., Berndt, M., Kollar, I., Dresel, M. and Fischer, F. (2023) Using Natural Language Processing to Support Peer-Feedback in the Age of Artificial Intelligence: A Cross-Disciplinary Framework and a Research Agenda. British Journal of Educational Technology, 54, 1222-1245.
[29]  Suresh, V., Agasthiya, R., Ajay, J., Gold, A.A. and Chandru, D. (2023) AI Based Automated Essay Grading System Using NLP. 2023 7th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, 17-19 May 2023, 547-552.
[30]  Conijn, R., Kahr, P. and Snijders, C. (2023) The Effects of Explanations in Automated Essay Scoring Systems on Student Trust and Motivation. Journal of Learning Analytics, 10, 37-53.
[31]  Shermis, M.D. (2022) Anchoring Validity Evidence for Automated Essay Scoring. Journal of Educational Measurement, 59, 314-337.
[32]  Morris, W., Crossley, S., Holmes, L. and Trumbore, A. (2023) Using Transformer Language Models to Validate Peer-Assigned Essay Scores in Massive Open Online Courses (MOOCs). LAK23: 13th International Learning Analytics and Knowledge Conference, Arlington, 13-17 March 2023, 315-323.
[33]  Perelman, L. (2020) The BABEL Generator and e-Rater: 21st Century Writing Constructs and Automated Essay Scoring (AES). Journal of Writing Assessment, 13, 1-8.
[34]  Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K. and Galstyan, A. (2021) A Survey on Bias and Fairness in Machine Learning. ACM Computing Surveys, 54, 1-35.
[35]  Cho, B., Geng, E., Arvind, V., Valliani, A.A., Tang, J.E., Schwartz, J. and Kim, J.S. (2022) Understanding Artificial Intelligence and Predictive Analytics: A Clinically Focused Review of Machine Learning Techniques. JBJS Reviews, 10, e21.00142.
[36]  Chen, Y., Zheng, Q., Ji, S., Tian, F., Zhu, H. and Liu, M. (2020) Identifying at-Risk Students Based on the Phased Prediction Model. Knowledge and Information Systems, 62, 987-1003.
[37]  Foster, E. and Siddle, R. (2020) The Effectiveness of Learning Analytics for Identifying at-Risk Students in Higher Education. Assessment & Evaluation in Higher Education, 45, 842-854.
[38]  Bañeres, D., Rodríguez, M.E., Guerrero-Roldán, A.E. and Karadeniz, A. (2020) An Early Warning System to Detect at-Risk Students in Online Higher Education. Applied Sciences, 10, Article No. 4427.
[39]  Shemshack, A. and Spector, J.M. (2020) A Systematic Literature Review of Personalized Learning Terms. Smart Learning Environments, 7, Article No. 33.
[40]  Jones, T.J. (2022) Relationships between Undergraduate Student Performance, Engagement, and Attendance in an Online Environment. Frontiers in Education, 7, Article ID: 906601.
[41]  Arnold, K.E. and Pistilli, M.D. (2012) Course Signals at Purdue: Using Learning Analytics to Increase Student Success. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, Vancouver, 29 April-2 May 2012, 267-270.
[42]  The University of Maryland (2022) Student Success Dashboard.
[43]  Muñoz, J.L.R., Ojeda, F.M., Jurado, D.L.A., Peña, P.F.P., Carranza, C.P.M., Berríos, H.Q., et al. (2022) Systematic Review of Adaptive Learning Technology for Learning in Higher Education. Eurasian Journal of Educational Research, 98, 221-233.
[44]  Bragg, D., Wetzstein, L., Meza, E.A. and Yeh, T. (2020) Transfer Partnerships: Lessons to Improve Student Success during and after COVID-19. Data Note 11. Transfer Partnerships Series. Community College Research Initiatives.
[45]  Poekert, P.E., Swaffield, S., Demir, E.K. and Wright, S.A. (2020) Leadership for Professional Learning towards Educational Equity: A Systematic Literature Review. Professional Development in Education, 46, 541-562.
[46]  Kelly, S.M. and Smith, D.W. (2011) The Impact of Assistive Technology on the Educational Performance of Students with Visual Impairments: A Synthesis of the Research. Journal of Visual Impairment & Blindness, 105, 73-83.
[47]  Shadiev, R., Hwang, W.Y., Chen, N.S. and Huang, Y.M. (2014) Review of Speech-to-Text Recognition Technology for Enhancing Learning. Journal of Educational Technology & Society, 17, 65-84.
[48]  Al-Azawei, A., Serenelli, F. and Lundqvist, K. (2016) Universal Design for Learning (UDL): A Content Analysis of Peer-Reviewed Journals from 2012 to 2015. Journal of the Scholarship of Teaching and Learning, 16, 39-56.
[49]  Woolf, B.P., Lane, H.C., Chaudhri, V.K. and Kolodner, J.L. (2013) AI Grand Challenges for Education. AI Magazine, 34, 66-84.
[50]  Ramaswami, G., Susnjak, T., Mathrani, A. and Umer, R. (2023) Use of Predictive Analytics within Learning Analytics Dashboards: A Review of Case Studies. Technology, Knowledge and Learning, 28, 959-980.
[51]  Jang, H. (2008) Supporting Students’ Motivation, Engagement, and Learning during an Uninteresting Activity. Journal of Educational Psychology, 100, 798-811.
[52]  Ibrahim, A., Thiruvady, D., Schneider, J.G. and Abdelrazek, M. (2020) The Challenges of Leveraging Threat Intelligence to Stop Data Breaches. Frontiers in Computer Science, 2, Article ID: 562053.
[53]  Reiss, M.J. (2021) The Use of Al in Education: Practicalities and Ethical Considerations. London Review of Education, 19.
[54]  Hacker, P. (2021) A Legal Framework for AI Training Data—From First Principles to the Artificial Intelligence Act. Law, Innovation and Technology, 13, 257-301.
[55]  Varsha, P.S. (2023) How Can We Manage Biases in Artificial Intelligence Systems—A Systematic Literature Review. International Journal of Information Management Data Insights, 3, Article ID: 100165.
[56]  Kitsara, I. (2022) Artificial Intelligence and the Digital Divide: From an Innovation Perspective. In: Bounfour, A., Ed., Platforms and Artificial Intelligence, Springer, Cham, 245-265.
[57]  Kim, J., Lee, H. and Cho, Y.H. (2022) Learning Design to Support Student-AI Collaboration: Perspectives of Leading Teachers for AI in Education. Education and Information Technologies, 27, 6069-6104.
[58]  Huang, L. (2023) Ethics of Artificial Intelligence in Education: Student Privacy and Data Protection. Science Insights Education Frontiers, 16, 2577-2587.
[59]  Park, T.M. (2022) Making AI Inclusive. Partnership on AI.
[60]  Nazaretsky, T., Ariely, M., Cukurova, M. and Alexandron, G. (2022) Teachers’ Trust in AI-Powered Educational Technology and a Professional Development Program to Improve It. British Journal of Educational Technology, 53, 914-931.
[61]  Stahl, B.C., Antoniou, J., Bhalla, N., Brooks, L., Jansen, P., Lindqvist, B., Wright, D., et al. (2023) A Systematic Review of Artificial Intelligence Impact Assessments. Artificial Intelligence Review, 56, 12799–12831.
[62]  Kizilcec, R.F. and Lee, H. (2022) Algorithmic Fairness in Education. Routledge, London.
[63]  Lorenzo, N. and Gallon, R. (2019) Smart Pedagogy for Smart Learning. In: Daniela, L., Ed., Didactics of Smart Pedagogy, Springer, Cham, 41-69.
[64]  Williamson, B. and Eynon, R. (2020) Historical Threads, Missing Links, and Future Directions in AI in Education. Learning, Media and Technology, 45, 223-235.
[65]  Morris, M.R. (2020) AI and Accessibility. Communications of the ACM, 63, 35-37.


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