Smarter Grants, Fairer Futures: AI in Higher Education Scholarship Distribution
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| Formato: | Recurso digital |
| Lenguaje: | Idioma anglosajón |
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2025
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| _version_ | 1866901834862428160 |
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| author | Alvin T. Remolado Jesszon B. Cano |
| author_facet | Alvin T. Remolado Jesszon B. Cano |
| contents | <div> <div> <p>Scholarships And Grants Are Essential Tools in Promoting Equitable Access To Higher Education, Particularly For Students From Low-Income Households Who May Otherwise Be Unable To Afford College. With Increasing Demand For Tertiary Education And Limited Financial Resources, The Challenge Of Distributing Scholarships Fairly, Efficiently, And Transparently Has Become More Complex. Traditional Scholarship Systems Often Suffer From Delays In Processing, Limited Accessibility, Subjective Evaluation Criteria, And Administrative Inefficiencies That Disadvantage Marginalized Applicants. This Commentary Explores The Transformative Potential Of Artificial Intelligence (AI) And Machine Learning (ML) In Enhancing The Scholarship Allocation Process In Colleges And Universities. These Technologies Can Analyze Large Datasets To Identify Eligible Candidates More Accurately, Streamline Application Reviews, Reduce Human Bias, And Enable Real-Time Tracking And Reporting Of Scholarship Outcomes. Moreover, AI And ML Applications Can Help Institutions Develop Predictive Models To Anticipate Students’ Financial Needs, Retention Risks, And Academic Performance, Making Aid Allocation More Targeted And Impactful. The Integration Of AI In Scholarship Systems Aligns With Several United Nations Sustainable Development Goals (Sdgs), Particularly SDG 4 (Quality Education), SDG 5 (Gender Equality), And SDG 10 (Reduced Inequalities), By Promoting Inclusive And Equitable Educational Opportunities. However, The Use Of These Technologies Also Raises Critical Concerns, Including Data Privacy, Algorithmic Transparency, And The Risk Of Reinforcing Existing Biases If Not Properly Regulated. This Paper Recommends That Higher Education Institutions Ethically And Effectively Harness AI And ML In Scholarship Management.</p> </div> </div> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17854947 |
| institution | Zenodo |
| language | ang |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Smarter Grants, Fairer Futures: AI in Higher Education Scholarship Distribution Alvin T. Remolado Jesszon B. Cano Artificial Intelligence, Machine Learning, Scholarship Allocation, Higher Education, Educational Equity, Algorithmic Fairness, Data Ethics <div> <div> <p>Scholarships And Grants Are Essential Tools in Promoting Equitable Access To Higher Education, Particularly For Students From Low-Income Households Who May Otherwise Be Unable To Afford College. With Increasing Demand For Tertiary Education And Limited Financial Resources, The Challenge Of Distributing Scholarships Fairly, Efficiently, And Transparently Has Become More Complex. Traditional Scholarship Systems Often Suffer From Delays In Processing, Limited Accessibility, Subjective Evaluation Criteria, And Administrative Inefficiencies That Disadvantage Marginalized Applicants. This Commentary Explores The Transformative Potential Of Artificial Intelligence (AI) And Machine Learning (ML) In Enhancing The Scholarship Allocation Process In Colleges And Universities. These Technologies Can Analyze Large Datasets To Identify Eligible Candidates More Accurately, Streamline Application Reviews, Reduce Human Bias, And Enable Real-Time Tracking And Reporting Of Scholarship Outcomes. Moreover, AI And ML Applications Can Help Institutions Develop Predictive Models To Anticipate Students’ Financial Needs, Retention Risks, And Academic Performance, Making Aid Allocation More Targeted And Impactful. The Integration Of AI In Scholarship Systems Aligns With Several United Nations Sustainable Development Goals (Sdgs), Particularly SDG 4 (Quality Education), SDG 5 (Gender Equality), And SDG 10 (Reduced Inequalities), By Promoting Inclusive And Equitable Educational Opportunities. However, The Use Of These Technologies Also Raises Critical Concerns, Including Data Privacy, Algorithmic Transparency, And The Risk Of Reinforcing Existing Biases If Not Properly Regulated. This Paper Recommends That Higher Education Institutions Ethically And Effectively Harness AI And ML In Scholarship Management.</p> </div> </div> |
| title | Smarter Grants, Fairer Futures: AI in Higher Education Scholarship Distribution |
| topic | Artificial Intelligence, Machine Learning, Scholarship Allocation, Higher Education, Educational Equity, Algorithmic Fairness, Data Ethics |
| url | https://doi.org/10.5281/zenodo.17854947 |