Enhancing Student Employability through AI/ML Infused STEM Education: Interdisciplinary Case Studies from an Urban HBCU
DOI:
https://doi.org/10.32674/dbx78y37Keywords:
Artificial Intelligence, Machine Learning, STEM Education, Employability Theory, Interdisciplinary Learning, Health DisparitiesAbstract
This paper explores three case studies at Morgan State University, an urban HBCU, showcasing the integration of AI and ML in STEM education to boost student employability. Aimed at students from underrepresented backgrounds, these initiatives focus on health disparities. The first case study involves graduate public health students using AI to create emergency preparedness solutions for marginalized communities. The second features undergraduate engineering students employing ML to analyze data and develop predictive models for public health interventions. The third highlights undergraduate health education students crafting AI-driven strategies to enhance health outcomes in Baltimore City schools. Together, these cases demonstrate how AI/ML-enriched curricula promote critical thinking, interdisciplinary collaboration, and readiness for complex societal challenges.
References
REFERENCES
Adamu, M., & Abdu Shakur, M.B.M. (2023). Graduate employability and skill gap: relationships of vocational training and machinery/equipment in an effective new business start-up. International Journal of Human Resources Development and Management 23(2), 115-126. DOI: 10.1504/ijhrdm.2023.10056969
Aminzadeh, A., Sattarpanah Karganroudi, S., Majidi, S., Dabompre, C., Azaiez, K., Mitride, C., & Sénéchal, E. (2025). A Machine Learning Implementation to Predictive Maintenance and Monitoring of Industrial Compressors. Sensors, 25(4), 1006. https://doi.org/10.3390/s25041006
Asiedu, E., Malcalm, E., Boakye, A. N., & Amoah, C.K. (2024). Graduate employability skills of business students: The moderating role of reflective practices. Higher Education, Skills and Work-Based Learning 14(2), 352-371. https://doi.org/10.1108/HESWBL-12-2022-0264
Barbosa, L. (2011). Casual Conversations in Communicating the Value and Worth of Historically Black Colleges and Universities. The Vermont Connection, 32(1). https://scholarworks.uvm.edu/tvc/vol32/iss1/
Bennett, D., Knight, E., Dockery, A. M., & Bawa, S. (2020). Pedagogies for employability: understanding the needs of STEM students through a new approach to employability development. Higher Education Pedagogies, 5(1), 340–359. https://doi.org/10.1080/23752696.2020.1847162
Berger, M. L., Sox, H., Willke, R. J., Brixner, D. L., Eichler, H. G., Goettsch, W. (2017). Good practices for real-world data studies of treatment and/or comparative effectiveness: Recommendations from the joint ISPOR-ISPE Special Task Force on real-world evidence in health care decision making. Pharmacoepidemiology and Drug Safety. 26(9), 1033-1039. doi: 10.1002/pds.4297
BLS (2025). Bureau of Labor Statistics, U.S. Department of Labor, Occupational Outlook Handbook, Emergency Management Directors. https://www.bls.gov/ooh/management/emergency-management-directors.htm
Bryant, K. (2023, October 30). How inclusive new AI model Latimer can fight bias. Forbes. https://www.forbes.com/sites/kalinabryant/2023/10/30/how-new-ai-model-counters-bias-providing-an-equitable-future-for-all/
Cantú-Ortiz, F. J., Galeano Sánchez, N., Garrido, L., Terashima-Marin, H. Brena, R.F. (2020). An artificial intelligence educational strategy for the digital transformation. International Journal on Interactive Design and Manufacturing, 14, 1195-1209. 10.1007/s12008-020-00702-8
Chen, Y., & Nukulkij, D. (2023). Impact of college education supply on college students’ employability. Asia Pacific Journal of Religions and Cultures 7(1), 199-213. https://so06.tci-thaijo.org/index.php/ajrc/article/view/262954
Cheng, M., Adekola, O., Albia,J., Cai, S. (2021). Employability in higher education: a review of key stakeholders' perspectives. Higher Education Evaluation and Development, 16(1), 16–31. https://doi.org/10.1108/HEED-03-2021-0025
Crewe, S. E. (2017). Education with Intent—The HBCU Experience. Journal of Human Behavior in the Social Environment 27, 360–366. 10.1080/10911359.2017.1318622
Dolatabadi, E., Moyano, D., Bales, M., Spasojevic, S., Bhambhoria, R., Bhatti, J., Debnath, S., Hoell, N., Li, X., & Leng, C. (2023). Using social media to help understand patient-reported health outcomes of post-COVID-19 condition: natural language processing approach. Journal of Medical Internet Research, 25. doi: 10.2196/45767
Donald, W.E., Baruch, Y., & Ashleigh, M.J. (2024). Construction and operationalization of an Employability Capital Growth Model (ECGM) via a systematic literature review (2016–2022). Studies in Higher Education 49(1), 1-15. https://doi.org/10.1080/03075079.2023.2219270
Downer, G. (2020, August 27). HBCUs should lead the way in disaster preparedness in their communities. The Journal of Blacks in Higher Education. https://jbhe.com/2020/08/hbcus-should-lead-the-way-in-disaster-preparedness-in-their-communities/
Eimer, A., & Bohndick, C. (2023). Employability models for higher education: A systematic literature review and analysis. Social Sciences & Humanities Open, 8(1). https://doi.org/10.1016/j.ssaho.2023.100588.
Fakunle, O., & Higson, H. (2021). Interrogating theoretical and empirical approaches to employability in different global regions. Higher Education Quarterly. https://doi.org/10.1111/hequ.12345
Fayer, S., Lacey, A., & Watson, A. (2017, January). U.S. Bureau of Labor Statistics. STEM occupations: past, present, and future. https://www.bls.gov/emp/tables/stem-employment.htm
Garcia-Morales, V. J., Moreno, A. G., Martin-Rojas, R. (2021). The transformation of higher education after the COVID disruption: emerging challenges in an online learning scenario. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.616059
Ghazzawi D. & Pattison D. L., Horn C. L. (2022). Investigating the interplay between participation in a STEM-focused student success program and workforce participation on STEM undergraduate degree completion. Frontiers in Sociology, 7. doi: 10.3389/fsoc.2022.818032.
Grosemans, I., De Cuyper, N., Forrier, A., & Vansteenkiste, S. (2023). Graduation is not the end, it is just the beginning: change in perceived employability in the transition associated with graduation. Journal of Vocational Behavior 145. https://doi.org/10.1016/j.jvb.2023.103915
Hahn, R. A.(2021). What is a social determinant of health? back to basics. Journal of Public Health Research 10(4). doi: 10.4081/jphr.2021.2324.
Hassan, M.U., Alaliyat, S., Sarwar, R., Nawaz, R., & Hameed, I.A. (2023). Leveraging deep learning and big data to enhance computing curriculum for industry-relevant skills: a Norwegian case study. Heliyon 9 (4). doi: 10.1016/j.heliyon.2023.e15407
Hitchcock, C., Vance-Chalcraft, H. D., & Aristeidou, M. (2021). Citizen Science in Higher Education. Citizen Science: Theory and Practice, 6(1), 22. http://doi.org/10.5334/cstp.467
Hood, S., Campbell, B. & Baker, K. (2023). Culturally informed community engagement: implications for inclusive science and health equity. National Library of Medicine. https://www.ncbi.nlm.nih.gov/books/NBK592587/
Isopahkala-Bouret, U., & Tholen, A. (2023). Relative employability: Applying the insights of positional competition and conflict theories within the current higher education landscape. Rethinking Graduate Employability in Context: Discourse, Policy and Practice, 51-72. doi:10.1007/978-3-031-20653-5_3
Javaid, M., Haleem, A., Singh, R.P., Suman, R., Rob, S. (2022). Significance of machine learning in healthcare: features, pillars, and applications. International Journal of Intelligent Networks, 3, 58-73. https://doi.org/10.1016/j.ijin.2022.05.002
Kandlhofer, M., Steinbauer, G., Hirschmugl-Gaisch, S., Huber, P. (2016). Artificial intelligence and computer science in education: From kindergarten to university. IEEE Frontiers in Education Conference (FIE). 1-9, 10.1109/FIE.2016.7757570
Kapucu N. (2011). Disaster and emergency management systems in urban areas. Cities, S41-S49. https://doi.org/10.1016/j.cities.2011.11.009
Kumar, V. (2021, November). Distance/online Engineering Education during and after COVID-19: graduate teaching assistant’s perspective. In ASME International Mechanical Engineering Congress and Exposition, 85659, p. V009T09A017. American Society of Mechanical Engineers. https://doi.org/10.1115/IMECE2021-72341
Kumar, V., Sznajder, K. K., & Kumara, S. (2022). Machine learning based suicide prediction and development of suicide vulnerability index for US counties. NPJ Mental Health Research, 1(1), https://doi.org/10.1038/s44184-022-00002-x
Kumar, V. (2025). Using ChatGPT for Assessment Development in Design and Manufacturing Engineering Education: Opportunities and Challenges. International Journal of Mechanical Engineering Education. https://doi.org/10.1177/03064190251374463
Masozera, M., Bailey, M., & Kerchner, C. (2007). Distribution of impacts of natural disasters across income groups: A case study of New Orleans. Ecological Economics, 63(2–3), 299–306. doi: 10.1016/j.ecolecon.2006.06.013
Mhasawade, V., Zhao, Y. & Chunara, R. (2021). Machine learning and algorithmic fairness in public and population health. Nature Machine Intelligence 3, 659–666. https://doi.org/10.1038/s42256-021-00373-4
Mooney, S. J. & Pejaver, V. (2024). Big data in public health: Terminology, machine learning, and privacy. Annual Review of Public Health, 39(1), 95–112. doi: 10.1146/annurev-publhealth-040617-014208.
Myers, M. F., Rogers, D. J., Cox, J., Flahault, A., & Hay, S. I. (2000). Forecasting disease risk for increased epidemic preparedness in public health. Advances in Parasitology, 4, 309–330. doi: 10.1016/s0065-308x(00)47013-2
Ordu, M., Demir, E., Tofallis, C., & Gunal, M. M. (2021). A novel healthcare resource allocation decision support tool: A forecasting-simulation optimization approach. Journal of the Operational Research Society, 72(3), 485–500.doi:10.1080/01605682.2019.1700186
Panch, T., Pearson-Stuttard, J., Greaves, F., & Atun, R. (2019). Artificial intelligence: opportunities and risks for public health. Lancet Digital Health, 1(1). 13-14. doi: 10.1016/S2589-7500(19)30002-0
Shah, N., & Bharathi, S.V. (2023). Towards enhancing the students’ employability for industry 5.0 through education 4.0. AIP Conference Proceedings 2869 (1). doi: 10.1063/5.0175774
Shuler, H. D., Spencer, E. C., Davis, J. S., Damo, S., Shakespeare, T. I., Murray, S. A., Lee, D. L., Hinton, A. (2022). Learning from HBCUs: How to produce Black professionals in STEM. Cell 185(16), 2841-2845. doi: 10.1016/j.cell.2022.06.013.
Shah, S., Mulahuwaish, A., Ghafoor, K.Z. et al. (2022). Prediction of global spread of COVID-19 pandemic: a review and research challenges. Artificial Intelligence Review, 55, 1607–1628. https://doi.org/10.1007/s10462-021-09988-w
Southworth, J., Migliaccio, K., Glover, J., Glover, J. N., Reed, D., McCarty, C., ... & Thomas, A. (2023). Developing a model for AI Across the curriculum: Transforming the higher education landscape via innovation in AI literacy. Computers and Education: Artificial Intelligence, 4, http://doi.100127.
Thottoli, M.M., Islam, A., Ahamad, S., & Hassan, S. (2023). Does e-learning enhance accounting students’ employability skills? A qualitative study of university students in Oman. Business, Management and Economics Engineering 21(1), 46-58. DOI: 10.5281/zenodo.7538588
Ulbrich, T. R., Kirk, L. M. (2017). It's time to broaden the conversation about the student debt crisis beyond rising tuition costs. American Journal of Pharmaceutical Education, 81(6) doi: 10.5688/ajpe816101.
Vance-Chalcraft, H. D., & Jelks, N. T. O. (2023). Community-engaged learning to broaden the impact of applied ecology: A case study. Ecological Applications, 33(5), https://doi.org/10.1002/eap.2768
Wei, R., & Sotiriadou, P. (2023). Teaching generic skill sets to sport undergraduates to increase their employability and promote smooth college-to-work transition. Journal of Hospitality, Leisure, Sport & Tourism Education 32. https://files.eric.ed.gov/fulltext/EJ1390848.pdf
Wijesinghe, D.P.S., & Jayawardane, V.P.T. (2023). Employability skills required by entry-level engineers in Sri Lanka. Engineer, 56(01), 113-122. http://dx.doi.org/10.4038/engineer.v56i1.7565
Zhang, H., Khaskheli, A., Raza, S.A., & Masood, A. (2023). Linkage between students’ skills and employability: Moderating influence of university reputation. Corporate Reputation Review, 1-20. http://dx.doi.org/10.1057/s41299-023-00169-9
Zhang, H., Zhang, X., & Brewer, G. (2012). Context-specific elective coursework and students’ employability development: Application of social cognitive career theory in hospitality education. Journal of Hospitality, Leisure, Sport & Tourism Education 33, https://doi.org/10.1016/j.jhlste.2023.100465
Zhang, Y. L. (2022). STEM persisters, switchers, and leavers: Factors associated with 6-year degree attainment for STEM aspiring community college transfer students. Community College Journal of Research and Practice, 46(11), 796-811. https://doi.org/10.1080/10668926.2021.1906784
Call for Special Issue Proposals 






