Smarter Grants, Fairer Futures: AI in Higher Education Scholarship Distribution

Article Sidebar

PDF
Published: 2025-12-06
Keywords:
Artificial Intelligence, Machine Learning, Scholarship Allocation, Higher Education, Educational Equity, Algorithmic Fairness, Data Ethics

Main Article Content

Alvin T. Remolado
College Of Science and Management Bohol Island State University – Clarin Campus Https://Orcid.Org/0000-0002-2605-474X
Jesszon B. Cano
College Of Business and Management Bohol Island State University – Candijay Campus Https://Orcid.Org/0000-0001-7071-033X

Abstract

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.

Article Details

Issue

Vol. 1 No. 4 (2025): Volume 01, Issue 04, 2025

Section

Articles

References

1) O. Espinoza, B. Corradi, L. González, L. Sandoval, N. Mcginn, And T. Vera, “Segmentation In Higher Education In Chile: Massification Without Equality,” High. Educ. Q., Vol. 78, No. 3, Pp. 536–550, Jul. 2024, Doi: 10.1111/Hequ.12465.

2) S. Hun, “Scholarship Schemes In Cambodian Higher Education: Unpacking Why Lower-Income Students Are Lagging Behind,” High. Educ. Policy, Jun. 2024, Doi: 10.1057/S41307-024-00369-W.

3) K. Rana, S. J. Aitken, And R. Chimoriya, “Interdisciplinary Approaches In Doctoral And Higher Research Education: An Integrative Scoping Review,” Educ. Sci., Vol. 15, No. 1, P. 72, Jan. 2025, Doi: 10.3390/Educsci15010072.

4) A. Zickafoose Et Al., “Barriers And Challenges Affecting Quality Education (Sustainable Development Goal #4) In Sub-Saharan Africa By 2030,” Sustainability, Vol. 16, No. 7, P. 2657, Mar. 2024, Doi: 10.3390/Su16072657.

5) S. Barikzai, V. Bharathi S, And A. Perdana, “Challenges And Strategies In E-Learning Adoption In Emerging Economies: A Scoping Review,” Cogent Educ., Vol. 11, No. 1, Dec. 2024, Doi: 10.1080/2331186x.2024.2400415.

6) M. Saaida And I. Saaidah, “Understanding The Dynamics Of Failure Development In Marginalized Areas: A Comprehensive Analysis,” Dec. 06, 2023, MDPI AG. Doi: 10.20944/Preprints202312.0395.V1.

7) A. D. Patil And Dr. Neelima Prashant Warke, “Enhancing Educational Access And Equality: The Significance Of Scholarship Schemes For Students From Marginalized Communities In Higher Education,” INTERANTIONAL J. Sci. Res. Eng. Manag., Vol. 08, No. 008, Pp. 1–16, Aug. 2024, Doi: 10.55041/Ijsrem37194.

8) A. N. Ardissone, S. Galindo, A. F. Wysocki, E. W. Triplett, And J. C. Drew, “The Need For Equitable Scholarship Criteria For Part-Time Students,” Innov. High. Educ., Vol. 46, No. 4, Pp. 461–479, Aug. 2021, Doi: 10.1007/S10755-021-09549-7.

9) Y. Orgianus, F. Lapalanti, H. Tarmizi, And H. Oemar, “Optimizing Scholarship Distribution: A Management Information System Approach,” Acta Logist., Vol. 11, No. 02, Pp. 197–209, Jun. 2024, Doi: 10.22306/Al.V11i2.497.

10) R. Kirk, S. J. Harris, And C. Chauhan, “Equitable Representation In Awards And Recognition In Scholarly Publishing: Current Challenges And The Path Ahead,” Learn. Publ., Vol. 36, No. 1, Pp. 37–41, Jan. 2023, Doi: 10.1002/Leap.1518.

11) C. Bourg, S. Kriegsman, N. Lindsay, H. Sardis, E. Stalberg, And M. Altman, “Generative AI For Trustworthy, Open, And Equitable Scholarship,” MIT Explor. Gener. AI, Mar. 2024, Doi: 10.21428/E4baedd9.567bfd15.

12) “Artificial Intelligence And Machine Learning In Higher Education,” In Advances In Educational Technologies And Instructional Design, IGI Global, 2021, Pp. 1–17. Doi: 10.4018/978-1-7998-5009-0.Ch001.

13) D. Кazimova, G. Tazhigulova, G. Shraimanova, A. Zatyneyko, And A. Sharzadin, “Transforming University Education With AI: A Systematic Review Of Technologies, Applications, And Implications,” Int. J. Eng. Pedagogy IJEP, Vol. 15, No. 1, Pp. 4–24, Jan. 2025, Doi: 10.3991/Ijep.V15i1.50773.

14) M. Nuong Deri, A. Singh, P. Zaazie, And D. Anandene, “Leveraging Artificial Intelligence In Higher Educational Institutions: A Comprehensive Overview,” Rev. Educ. Derecho, No. 30, Oct. 2024, Doi: 10.1344/Reyd2024.30.45777.

15) M. A. Sulaiman, “ABU Easy Go! Development And Optimization Of A Machine Learning-Powered Chatbot For FAQ Assistance At Ahmadu Bello University, Zaria.,” Jan. 01, 2025, Springer Science And Business Media LLC. Doi: 10.21203/Rs.3.Rs-5740271/V1.

16) “The Application Of Artificial Intelligence In Educational Administration,” In Advances In Educational Technologies And Instructional Design, IGI Global, 2025, Pp. 209–230. Doi: 10.4018/979-8-3693-7949-3.Ch008.

17) A. T. Tran Et Al., “Improving The Robustness Of Deep Learning Models In Predicting Hematoma Expansion From Admission Head CT,” Am. J. Neuroradiol., Jan. 2025, Doi: 10.3174/Ajnr.A8650.

18) “AI In Higher Education: Revolutionizing Curriculum And Administration,” In Advances In Educational Technologies And Instructional Design, IGI Global, 2025, Pp. 31–62. Doi: 10.4018/979-8-3693-7949-3.Ch002.

19) B. Karim-Abdallah, M. Ayitey Junior, P. Appiahene, E. Harris, And D. K. Binful, “Application Of Machine Learning Algorithms In Predicting Academic Performance Of Students In Higher Education Institutes (Heis): A Systematic Review And Bibliographic Analysis,” Afr. J. Appl. Res., Vol. 11, No. 1, Pp. 536–559, Jan. 2025, Doi: 10.26437/Ajar.V11i1.869.

20) A. Maiti, A. Abarda, M. Hanini, And A. Zaaloul, “A New Approach For Predicting Academic Performance,” Procedia Comput. Sci., Vol. 257, Pp. 1257–1262, 2025, Doi: 10.1016/J.Procs.2025.03.169.

21) K. Koçar, “AI-Based Decision Making Process In The Finance Sector,” Gener. J. Young Res., Vol. 8, No. 1, P. 143, Dec. 2024, Doi: 10.62802/Rz706w81.

22) U. Goswami, “THE ROLE OF ARTIFICIAL INTELLIGENCE IN FINANCIAL DECISION-MAKING,” INTERANTIONAL J. Sci. Res. Eng. Manag., Vol. 08, No. 04, Pp. 1–5, May 2024, Doi: 10.55041/Ijsrem32690.

23) Aparna Krishna Bhat, “Application And Impact Of Artificial Intelligence In Financial Decision Making,” Int. J. Sci. Res. Sci. Eng. Technol., Vol. 11, No. 5, Pp. 57–63, Sep. 2024, Doi: 10.32628/Ijsrset2411417.

24) A. K. M. K. Khan, “AI In Finance Disruptive Technologies And Emerging Opportunities,” J. Artif. Intell. Gen. Sci. JAIGS ISSN3006-4023, Vol. 3, No. 1, Pp. 155–170, Mar. 2024, Doi: 10.60087/Jaigs.V3i1.76.

25) I. G. Prahmana, Adek Maulidya, Kristina A. Sitepu, And Razaq Habibi, “Application Of The K-Nearest Neighbor (KNN) Algorithm In Machine Learning To Predict The Selection Of Undergraduate Study Programs Based On New KIP Lecture Students,” J. Artif. Intell. Eng. Appl. JAIEA, Vol. 4, No. 2, Pp. 1522–1526, Feb. 2025, Doi: 10.59934/Jaiea.V4i2.941.

26) “Chapter 11 Spotlight On Six Marginalized Populations In American Higher Education,” In Innovations In Higher Education Teaching And Learning, Emerald Publishing Limited, 2018, Pp. 155–168. Doi: 10.1108/S2055-364120180000012012.

27) P. K. Mangat, “INCLUSIVITY IN HIGHER EDUCATION: ANALYSING THE IMPLEMENTATION OF NEP 2020 FOR MARGINALIZED COMMUNITIES IN INDIA,” Shodhkosh J. Vis. Perform. Arts, Vol. 5, No. 1, Jun. 2024, Doi: 10.29121/Shodhkosh.V5.I1.2024.1990.

28) R. Folostina, Faculty Of Psychology And Educational Sciences, University Of Bucharest, Bucharest, Romania, L.-T. Saghin, Faculty Of Psychology And Educational Sciences, University Of Bucharest, Bucharest, Romania, E.-M. Iancu, And Faculty Of Psychology And Educational Sciences, University Of Bucharest, Bucharest, Romania, “Enhancing Access And Success For Disadvantaged Students Through Student Ambassadors,” Educ. 21, No. 28, Pp. 306–313, Nov. 2024, Doi: 10.24193/Ed21.2024.28.33.

29) A. Mayr And S. Oppl, “Higher Education At The Margins – Success Criteria For Blended Learning Systems For Marginalized Communities,” Educ. Inf. Technol., Vol. 28, No. 3, Pp. 2579–2617, Mar. 2023, Doi: 10.1007/S10639-022-11282-3.

30) T. Austin, B. S. Rawal, A. Diehl, And J. Cosme, “AI For Equity: Unpacking Potential Human Bias In Decision Making In Higher Education,” AI Comput. Sci. Robot. Technol., Vol. 2, May 2023, Doi: 10.5772/Acrt.20.

31) Y. Ding Et Al., “Voices Of Her: Analyzing Gender Differences In The AI Publication World,” 2023, Arxiv. Doi: 10.48550/ARXIV.2305.14597.

32) D. Sarraf, V. Vasiliu, B. Imberman, And B. Lindeman, “Use Of Artificial Intelligence For Gender Bias Analysis In Letters Of Recommendation For General Surgery Residency Candidates,” Am. J. Surg., Vol. 222, No. 6, Pp. 1051–1059, Dec. 2021, Doi: 10.1016/J.Amjsurg.2021.09.034.

33) F. V. Espiritu, M. C. B. Natividad, And R. A. Velasco, “Data-Driven Decision Making In Scholarship Programs: Leveraging Decision Trees And Clustering Algorithms,” Int. J. Inf. Technol. Gov. Educ. Bus., Vol. 6, No. 1, Pp. 55–67, May 2024, Doi: 10.32664/Ijitgeb.V6i1.134.

34) M. Mutevere Et Al., “Empowering Underprivileged Students Beyond Financial Aid: Insights From A Scholarship Program’s Monitoring And Evaluation,” Eval. Program Plann., Vol. 105, P. 102448, Aug. 2024, Doi: 10.1016/J.Evalprogplan.2024.102448.

35) A. Yusuf Novianto, “Optimizing Scholarship Selection: A Decision Support System Approach Using The Analytical Hierarchy Process,” TIERS Inf. Technol. J., Vol. 4, No. 2, Pp. 144–149, Dec. 2023, Doi: 10.38043/Tiers.V4i2.5519.

36) R. Hegde, A. G. V, S. Madival, S. H. S, And S. U, “A Review On Data Mining And Machine Learning Methods For Student Scholarship Prediction,” In 2021 5th International Conference On Computing Methodologies And Communication (ICCMC), Erode, India: IEEE, Apr. 2021, Pp. 923–927. Doi: 10.1109/Iccmc51019.2021.9418376.

37) A. H. Hoti, X. Zenuni, M. Hamiti, And J. Ajdari, “Student Performance Prediction Using AI And ML: State Of The Art,” In 2023 12th Mediterranean Conference On Embedded Computing (MECO), Budva, Montenegro: IEEE, Jun. 2023, Pp. 1–6. Doi: 10.1109/Meco58584.2023.10154933.

38) “Predicting Student Success In A Scholarship Program: A Comparative Study Of Classification Learning Models,” In Lecture Notes In Networks And Systems, Cham: Springer International Publishing, 2022, Pp. 333–341. Doi: 10.1007/978-3-031-02447-4_35.

39) H. P. Josyula, S. R. Landge, S. R. Gunturu, K. Gupta, And T. Kiruthiga, “Leveraging AI And ML To Automate Financial Predictions And Recommendations,” In 2024 2nd International Conference On Disruptive Technologies (ICDT), Greater Noida, India: IEEE, Mar. 2024, Pp. 452–457. Doi: 10.1109/Icdt61202.2024.10489718.

40) M. H. Ismail, T. R. Razak, N. M. Noor, And A. A. Aziz, “Evaluating Machine Learning Algorithms For Predicting Financial Aid Eligibility: A Comparative Study Of Random Forest, Gradient Boosting And Neural Network,” In 2024 18th International Conference On Ubiquitous Information Management And Communication (IMCOM), Kuala Lumpur, Malaysia: IEEE, Jan. 2024, Pp. 1–6. Doi: 10.1109/Imcom60618.2024.10418450.

41) T.-A. Nguyen-Hoang Et Al., “Advancing Scholarship Management: A Blockchain-Enhanced Platform With Privacy-Secure Identities And AI-Driven Recommendations,” IEEE Access, Vol. 12, Pp. 168060–168090, 2024, Doi: 10.1109/Access.2024.3486078.

42) M. Shoaib, N. Sayed, J. Singh, J. Shafi, S. Khan, And F. Ali, “AI Student Success Predictor: Enhancing Personalized Learning In Campus Management Systems,” Comput. Hum. Behav., Vol. 158, P. 108301, Sep. 2024, Doi: 10.1016/J.Chb.2024.108301.

43) J. C. Calfoforo, R. C. Raga, And M. B. Abisado, “Transforming Scholarship Selection: A Review Of AI-Driven Matching Algorithms And Transformer Models,” In 2025 8th International Conference On Information And Computer Technologies (ICICT), Hawaii-Hilo, HI, USA: IEEE, Mar. 2025, Pp. 411–416. Doi: 10.1109/Icict64582.2025.00070.

44) J. M. Scoulas And J.-C. Kim, “Reevaluating Assessment Practices For Marginalized Groups,” J. Libr. Adm., Vol. 65, No. 2, Pp. 244–254, Feb. 2025, Doi: 10.1080/01930826.2024.2437966.

45) B. Rose Et Al., “Understanding The Role Of OLAP And OLTP In Managing And Interpreting Student Data For SEAIT Scholarship System,” Int. J. Sci. Acad. Res., Vol. 04, No. 09, Pp. 17–28, 2024, Doi: 10.54756/Ijsar.2024.18.

46) J. Nam And E. Bell, “Efficiency Or Equity? How Public Values Shape Bureaucrats’ Willingness To Use Artificial Intelligence To Reduce Administrative Burdens,” Public Perform. Manag. Rev., Pp. 1–34, Oct. 2024, Doi: 10.1080/15309576.2024.2419132.

47) T. Lazebnik And A. Rosenfeld, “How Lonely Or Influential Is The Lone Wolf? An Analysis Of Individual Scholars’ Solo-Authorship Dynamics,” Scientometrics, Vol. 130, No. 5, Pp. 3053–3069, May 2025, Doi: 10.1007/S11192-025-05332-Z.

48) L. M. Bacalhau, M. C. Pereira, And J. Neves, “A Bibliometric Analysis Of AI Bias In Marketing: Field Evolution And Future Research Agenda,” J. Mark. Anal., Vol. 13, No. 2, Pp. 308–327, Jun. 2025, Doi: 10.1057/S41270-025-00379-6.

49) C. Woods And G. P. Johnson, “(Re)Designing Privacy Literacy In The Age Of Generative AI,” Commun. Des. Q., Vol. 12, No. 2, Pp. 86–97, Jun. 2024, Doi: 10.1145/3655727.3655736.