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This paper uses machine learning to investigate the problems faced by foreign students in Indian universities from 2021 to 2023, therefore identifying systematic flaws in ICT-driven governance systems. Using mixed-method analysis of 1,680 respondents, the study found notable policy flaws in digital integration including disconnected visa systems, poor language assistance tools, and ineffective grievance redressal procedures. By means of predictive analytics, blockchain-secured platforms, and AI-enabled cultural adaption tools, the proposed machine learning model defines a four-tier ICT governance framework in line with India's e-government operations and addresses UN SDG 4 (Quality Education).

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Machine Learning-Driven E-Governance Framework for Mitigating Challenges of International Students in Indian Institutions: A Policy-Centric Approach

  • 会议论文

  • 发表日期：

    2026年

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  • Himani Binjola,
  • Kaipee Luther Newray,
  • Vivek,
  • Vidushi Negi,
  • Shweta Bajaj,
  • Smriti Tandon Gupta,
  • Rekha Verma,
  • Shikha Tyagi

摘要

This paper uses machine learning to investigate the problems faced by foreign students in Indian universities from 2021 to 2023, therefore identifying systematic flaws in ICT-driven governance systems. Using mixed-method analysis of 1,680 respondents, the study found notable policy flaws in digital integration including disconnected visa systems, poor language assistance tools, and ineffective grievance redressal procedures. By means of predictive analytics, blockchain-secured platforms, and AI-enabled cultural adaption tools, the proposed machine learning model defines a four-tier ICT governance framework in line with India's e-government operations and addresses UN SDG 4 (Quality Education).