Journal of Computer Technology & Applications

Journal of Computer Technology & Applications Review Article

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

  1. Ravindra P. Purohit Department of Computer Science, Shree GK&CK Bosamia College, Jetpur - 360370

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

  • Machine Learning
  • big-data pipelines
  • educational data mining
  • learning analytics
  • responsible AI

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