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An Ethical Governance Framework for Early-Warning Systems in Indian Higher Education: Moving from Student Risk Prediction to Student Support
Subject area: Science,Engineering and Technology · Area of research: Educational Technology and Learning Analytics
Abstract
Background: Student dropout in higher education is shaped by academic performance, engagement, family circumstances, financial pressure and the quality of institutional support, often acting together rather than in isolation. Universities increasingly use learning analytics and early-warning systems to flag students who may need help, but these systems can also reduce students to a risk score, gather sensitive data without a clear purpose, and drive high-stakes decisions without adequate human review. Purpose: This conceptual paper proposes an ethical governance framework for early-warning systems in Indian higher education, arguing that such systems should be designed as support architecture rather than surveillance infrastructure. Methods: Following an established conceptual-research approach, the paper synthesises literature on student retention, learning analytics, educational data mining and student-data privacy to build a structured model that separates an indicator, a risk signal and an institutional decision, with human judgement placed between the second and the third. Results: The framework rests on six governance principles — data minimisation and purpose limitation, transparency and student agency, mandatory human oversight, restrictions on high-stakes automated decisions, bias monitoring and accountability, and mechanisms for correction and student recourse. These are operationalised through a seven-stage support architecture and a practical institutional pilot in which academic and engagement indicators trigger human-led outreach rather than automatic academic or disciplinary consequences. Conclusions: Early-warning systems in Indian higher education should be judged not by how many students they classify as "at risk," but by whether they help institutions notice difficulties sooner, respond with more care, and protect student dignity and agency throughout.
Keywords
AI governance; early-warning systems; human-in-the-loop systems; learning analytics; responsible AI systems design
References
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How to cite this paper
@article{1723165,
author = {Aditya Sagar},
title = {An Ethical Governance Framework for Early-Warning Systems in Indian Higher Education: Moving from Student Risk Prediction to Student Support},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {3},
pages = {2606-2615},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1723165.pdf},
abstract = {Background: Student dropout in higher education is shaped by academic performance, engagement, family circumstances, financial pressure and the quality of institutional support, often acting together rather than in isolation. Universities increasingly use learning analytics and early-warning systems to flag students who may need help, but these systems can also reduce students to a risk score, gather sensitive data without a clear purpose, and drive high-stakes decisions without adequate human review.
Purpose: This conceptual paper proposes an ethical governance framework for early-warning systems in Indian higher education, arguing that such systems should be designed as support architecture rather than surveillance infrastructure.
Methods: Following an established conceptual-research approach, the paper synthesises literature on student retention, learning analytics, educational data mining and student-data privacy to build a structured model that separates an indicator, a risk signal and an institutional decision, with human judgement placed between the second and the third.
Results: The framework rests on six governance principles — data minimisation and purpose limitation, transparency and student agency, mandatory human oversight, restrictions on high-stakes automated decisions, bias monitoring and accountability, and mechanisms for correction and student recourse. These are operationalised through a seven-stage support architecture and a practical institutional pilot in which academic and engagement indicators trigger human-led outreach rather than automatic academic or disciplinary consequences.
Conclusions: Early-warning systems in Indian higher education should be judged not by how many students they classify as "at risk," but by whether they help institutions notice difficulties sooner, respond with more care, and protect student dignity and agency throughout.},
keywords = {AI governance; early-warning systems; human-in-the-loop systems; learning analytics; responsible AI systems design},
month = {September},
}