Automated Intelligence, Machine Learning, and Big Data in Education: A Practical Framework, Synthetic Demonstration, and Deployment Guidance

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 17 | 01 | Page :
    By

    Ravindra P. Purohit,

  1. Assistant Professor, Department of Computer Science, Shree GK&CK Bosamia College, Jetpur – 360370, Gujarat, India

Abstract

Artificial intelligence (AI), machine learning (ML), and big-data methods are increasingly used to improve educational decision making through personalization, early-warning systems, scalable feedback, and operational analytics. This manuscript proposes a practical end-to-end framework for educational AI/ML projects, covering problem definition, data engineering, modeling, evaluation, intervention design, and responsible governance. To provide a complete and reproducible template without exposing sensitive student data, we present a synthetic demonstration study that mirrors typical institutional sources such as academic history, learning management system (LMS) engagement logs, and formative assessment signals. We report decision-focused metrics under a realistic constraint (flagging only the top 10% of learners by risk, reflecting limited support capacity), and we include operational artifacts commonly required for conference submissions: performance tables, calibration and fairness views, feature- importance summaries, confusion matrices, and dashboard-style heatmaps. We also provide an intervention mapping table and a compact governance checklist addressing privacy, transparency, bias monitoring, and human oversight. The paper concludes with deployment recommendations for pilot-first adoption, continuous monitoring for concept drift, and communication strategies that reduce stigma and support learner autonomy.

Keywords: educational data mining; learning analytics; machine learning; big-data pipelines; responsible AI

How to cite this article:
Ravindra P. Purohit. Automated Intelligence, Machine Learning, and Big Data in Education: A Practical Framework, Synthetic Demonstration, and Deployment Guidance. Journal of Computer Technology & Applications. 2026; 17(01):-.
How to cite this URL:
Ravindra P. Purohit. Automated Intelligence, Machine Learning, and Big Data in Education: A Practical Framework, Synthetic Demonstration, and Deployment Guidance. Journal of Computer Technology & Applications. 2026; 17(01):-. Available from: https://journals.stmjournals.com/jocta/article=2026/view=237235


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Ahead of Print Subscription Review Article
Volume 17
01
Received 27/12/2025
Accepted 12/01/2026
Published 20/02/2026
Publication Time 55 Days
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