The Future of Learning in K-12 Education: Reconstructing the School-Learner-Home Relationship through Artificial Intelligence — Oak Academic Publishing
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The Future of Learning in K-12 Education: Reconstructing the School-Learner-Home Relationship through Artificial Intelligence
Department of Information Technology, University of the Potomac, Vienna, VA, USA
,
Department of Mechanical, Environmental, and Civil Engineering, Tarleton State University, Stephenville, TX, USA
1 Department of Information Technology, University of the Potomac, Vienna, VA, USA
2 Department of Mechanical, Environmental, and Civil Engineering, Tarleton State University, Stephenville, TX, USA
The evolving demands of modern K-12 education necessitate a fundamental reconceptualization of instructional architecture from fragmented, institution-centric delivery models toward integrated, data-driven learning ecosystems in which schools, learners, and homes operate as a coherent unit. This paper examines the triadic relationship between the school, the learner, and the home as a structurally foundational, yet historically underutilized, configuration for sustained educational effectiveness. Drawing on Bronfenbrenner’s ecological systems theory, Vygotsky’s sociocultural framework, and constructivist principles of knowledge building, the paper argues that the persistent disconnect among these three entities constitutes a systemic design failure—one that manifests as diminished student engagement, delayed identification of learning difficulties, and suboptimal academic outcomes across K-12 contexts. To address this failure, the paper introduces and develops the concept of Artificial Intelligence as a Coordination Layer (AI-CL), a novel theoretical and architectural construct through which AI-powered systems mediate real-time information flows, adaptive feedback mechanisms, and collaborative decision-making among schools, learners, and caregivers. The AI-CL framework is distinguished from prior AI-in-education models by its explicit focus on triadic integration rather than isolated learner-system interaction. Through conceptual modeling and design-based reasoning, the paper delineates the functional components of AI-CL, examines its implications for K-12 pedagogical practice and school leadership, and critically addresses the ethical dimensions of AI deployment with minors, including data privacy under FERPA and COPPA, algorithmic bias, the digital divide, and the imperative of equitable design. The paper concludes by situating AI-CL within the broader trajectory of educational technology research and proposing a validation agenda for future empirical study.
Epstein, J.L. and Sheldon, S.B. (2002) Present and Accounted for: Improving Student Attendance through Family and Community Involvement. The Journal of Educat ional Research , 95, 308-318. https://doi.org/10.1080/00220670209596604
Henderson, A.T. and Berla, N. (1994) A New Wave of Evidence: The Impact of School, Family, and Community Connections on Student Achievement. National Center for Family and Community Connections with Schools.
National Assessment Governing Board (2024) 5 Takeaways from 12th Grade NAEP Math and Reading Results. https://www.nagb.gov/powered-by-naep/the-2024-nations-report-card/top-5-takeaways-from-12-grade-naep-math-reading-results.html
National Center for Education Statistics. PISA 2022 U.S. Results. https://nces.ed.gov/surveys/pisa/pisa2022/
National Center for Education Statistics. Mathematics Scores of U.S. Fourth-and Eighth-Graders Decline on International Mathematics and Science Assessment. https://ies.ed.gov/learn/press-release/mathematics-scores-u-s-fourth-and-eighth-graders-decline-international-mathematics-and-science
Hattie, J. (2008) Visible Learning: A Synthesis of over 800 Meta-Analyses Relating to Achievement. Routledge.
Musset, P., Pont, B., Benavides, F. and Pons, A. (2012) Equity and Quality in Education: Supporting Disadvantaged Students and Schools. OECD Publishing.
Pane, J., Steiner, E., Baird, M. and Hamilton, L. (2015) Continued Progress: Promising Evidence on Personalized Learning. RAND Corporation.
Luckin, R. and Holmes, W. (2016) Intelligence Unleashed: An argument for AI in Education. Pearson. https://discovery.ucl.ac.uk/id/eprint/1475756
UNESCO (2022) Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. UNESCO Publishing.
Hattie, J. (2012) Visible Learning for Teachers: Maximizing Impact on Learning. Routledge.
Fiore, D.J. (2001) School, Family, and Community Partnerships: Preparing Educators and Improving Schools. NASSP Bulletin , 85, 85-87. https://doi.org/10.1177/019263650108562710
Kraft, M.A. and Rogers, T. (2015) The Underutilized Potential of Teacher-to-Parent Communication: Evidence from a Field Experiment. Economics of Education R eview , 47, 49-63. https://doi.org/10.1016/j.econedurev.2015.04.001
OECD (2023) Education at a Glance 2023. OECD Publishing. https://www.oecd.org/en/publications/education-at-a-glance-2023_e13bef63-en.html
Lareau, A. (2011) Unequal Childhoods: Class, Race, and Family Life. 2nd Edition, University of California Press. https://doi.org/10.1525/9780520949904
Pane, J., Steiner, E., Baird, M., Hamilton, L. and Pane, J. (2017) Informing Progress: Insights on Personalized Learning Implementation and Effects. RAND Corporation.
Pane, J.F., Griffin, B.A., McCaffrey, D.F. and Karam, R. (2014) Effectiveness of Cognitive Tutor Algebra I at Scale. Educational Evaluation and Policy Analysis , 36, 127-144. https://doi.org/10.3102/0162373713507480
Bloom, B.S. (1984) The 2 Sigma Problem: The Search for Methods of Group Instruction as Effective as One-to-One Tutoring. Educational Researcher , 13, 4-16. https://doi.org/10.3102/0013189x013006004
Aleven, V., McLaren, B.M., Sewall, J. and Koedinger, K.R. (2009) A New Paradigm for Intelligent Tutoring Systems: Example-Tracing Tutors. International Journal of Art ificial Intelligence in Education , 19, 105-154.
Corbett, A.T. and Anderson, J.R. (1995) Knowledge Tracing: Modeling the Acquisition of Procedural Knowledge. User Modelling and User - Adapted Interaction , 4, 253-278. https://doi.org/10.1007/bf01099821
Long, P. and Siemens, G. (2011) Penetrating the Fog: Analytics in Learning and Education. Educational Review , 46, 30-32.
Arnold, K.E. and Pistilli, M.D. (2012) Course Signals at Purdue: Using Learning Analytics to Increase Student Success. Proceedings of the 2 nd International Conference on Learning Analytics and Knowledge , Vancouver, 29 April-2 May 2012, 267-270. https://doi.org/10.1145/2330601.2330666
Mollick, E.R. and Mollick, L. (2023) Assigning AI: Seven Approaches for Students, with Prompts. SSRN Electronic Journal . https://doi.org/10.2139/ssrn.4475995
UNESCO (2022) K-12 AI Curricula: A Mapping of Government-Endorsed AI Curricula. https://unesdoc.unesco.org/ark:/48223/pf0000380602
Fullan, M. (2016) The New Meaning of Educational Change. 5th Edition, Teachers College Press.
Garbarino, J. (1980) The Ecology of Human Development: Experiments by Nature and Design. Children and Youth Services Review , 2, 433-438. https://doi.org/10.1016/0190-7409(80)90036-5
Swe Dberg, R. (1980) Mind in Society: The Development of Higher Psychological Processes. Science & Society : A Journal of Marxist Thought and Analysis , 44, 126-126. https://doi.org/10.1177/003682378004400121
Jonassen, H.D. (1999) Designing Constructivist Learning Environments. In: Reigeluth, C.M., Ed., Instructional - Design Theories and Models : A New Paradigm of Ins tructional Theory , Vol. 2, Lawrence Erlbaum Associates, 215-239.
Hohpe, G. and Woolf, B. (2003) Enterprise Integration Patterns: Designing, Building, and Deploying Messaging Solutions. Addison-Wesley.
Brown, A.L. (1992) Design Experiments: Theoretical and Methodological Challenges in Creating Complex Interventions in Classroom Settings. Journal of the Learning Sciences , 2, 141-178. https://doi.org/10.1207/s15327809jls0202_2
Barab, S. and Squire, K. (2004) Design-Based Research: Putting a Stake in the Ground. Journal of the Learning Sciences , 13, 1-14. https://doi.org/10.1207/s15327809jls1301_1
Darling-Hammond, L., et al . (2021) Using the Science of Learning and Development to Transform Educational Practice. In: Cantor, P. and Osher, D., Eds., The Science of Learnin g and Development : Enhancing the Lives of All Young People , Routledge, 123-144.
U.S. Department of Education, Family Educational Rights and Privacy Act (FERPA). https://www2.ed.gov/policy/gen/guid/fpco/ferpa/index.html
Pew Research Center (2025) Internet/Broadband Fact Sheet. https://www.pewresearch.org/internet/fact-sheet/internet-broadband/
Sharma, S. (2020) Algorithms of Oppression: How Search Engines Reinforce Racism. Ethnic and Racial Studies , 43, 592-594. https://doi.org/10.1080/01419870.2019.1635260
Eubanks, V. (2018) Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin’s Press. https://orcid.org/0000-0001-5017-0831
Escueta, M., Quan, V., Nickow, A.J. and Oreopoulos, P. (2017) Education Technology: An Evidence-Based Review. NBER Working Paper No. 23744. https://www.nber.org/papers/w23744
Garrison, D.R., Anderson, T. and Archer, W. (1999) Critical Inquiry in a Text-Based Environment: Computer Conferencing in Higher Education. The Internet and Higher Education , 2, 87-105. https://doi.org/10.1016/s1096-7516(00)00016-6
CAST (2018) Universal Design for Learning Guidelines Version 2.2. CAST. http://udlguidelines.cast.org