AI-enabled school management dashboards for the early identification of student mental-health risks in secondary schools in the federal capital territory, Abuja

Authors

DOI:

https://doi.org/10.32674/fdm79p37

Keywords:

Artificial Intelligence in Education, Nigeria, school management dashboards, secondary schools, student mental health, early identification

Abstract

Adolescent mental health is a growing concern in secondary schools worldwide. In Nigerian schools, systematic mechanisms for the early detection of psychological risk remain limited, with most institutions continuing to rely on informal observation and crisis-driven referrals. This study examined the influence of artificial intelligence (AI)-enabled school management dashboards on the early identification of student mental health risks in selected private secondary schools in the Federal Capital Territory (FCT), Abuja, Nigeria. A descriptive survey research design was adopted. A researcher-developed AISMMH questionnaire, validated by subject experts, was used to generate responses from 472 respondents. Multiple linear regressions revealed that AI-driven data indicators significantly predicted early mental health risk identification.

Author Biographies

  • Victoria Chinyere Wilson-Woko, Veritas University, Abuja

    Victoria Wilson-Woko is an inclusive education practitioner and emerging researcher with interests in educational technology, school leadership, and student well-being. Her work explores the use of data-informed and digital tools to support decision-making in secondary school settings. 

    This publication is based on a course-based research project completed in partial fulfillment of academic requirements and reflects her broader interest in the ethical and practical application of technology in education.

  • Benjamin Nasara, Veritas University, Abuja

    Benjamin Nasara is an education practitioner and emerging researcher with a postgraduate degree in Guidance and Counselling. He is a co-author on this study, which is based on a course-based research project completed in partial fulfilment of academic requirements. His interests include student mental health, psychosocial support, and counselling-informed approaches to promoting student well-being in secondary school settings.

  • Mary Chinyere Chukwu, Veritas University, Abuja

    Mary Chinyere Chukwu is a postgraduate student of educational management, and an emerging researcher with interests in educational leadership, school management systems, and student well-being. She is a co-author on this study, which is based on a course-based research project completed in partial fulfilment of academic requirements. He research interest focus on the use of Ai-enabled school management dashboards and and data-driven indicators such as attendance, academic, behavioural data, to support early identification of mental-health risks among secondary school students.

  • Dr. Joy Abosede Peter, Veritas University, Abuja

    Joy Abosede Peter (Ph.D) is a lecturer at Veritas University, Abuja. She is a highly motivated researcher with expertise in educational technology, curriculum design, and learning outcomes. She has a proven track record of publishing impactful  studies and collaborating with educators to drive innovation. She is passionate about harnessing technology to enhance teaching and learning experiences.

  • Dr. Felix Emeka Iyala, Veritas University, Abuja

    Felix Emeka Iyala (Ph.D.) is a lecturer in the department of Educational Foundations, Faculty of Education, Veritas University, Abuja, Nigeria. He holds a Ph.D in Educational Administration and Planning and has research interests in school management, educational finance, and management information systems. He has published widely and is a fellow of the Association of Educational Management and Policy Practitioners (A'EMAP).

References

Ahmad, S. F., Alam, M. M., Rahmat, M. K., Mubarik, M. S., & Hyder, S. I. (2022). Academic and administrative role of artificial intelligence in education. Sustainability, 14(3), 1101. https://doi.org/10.3390/su14031101 DOI: https://doi.org/10.3390/su14031101

Akinade, E. A. (2015). Principles and practice of guidance and counselling. Brightway Publishers.

American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.). American Psychological Association. https://doi.org/10.1037/0000165-000 DOI: https://doi.org/10.1037/0000165-000

Babbie, E. (2021). The practice of social research (15th ed.). Cengage Learning.

Babatunde, A., Oshodi, O., Ebunlomo, E. O., Akinhanmi, A., & Nwachukwu, I. (2021). Barriers and facilitators to child and adolescent mental health services in low- and middle-income countries: A scoping review. Global Mental Health, 8, e34. https://doi.org/10.1017/gmh.2021.28 DOI: https://doi.org/10.1017/gmh.2021.28

Bronfenbrenner, U. (1979). The ecology of human development: Experiments by nature and design. Harvard University Press. DOI: https://doi.org/10.4159/9780674028845

Bronfenbrenner, U., & Morris, P. A. (2006). The bioecological model of human development. In R. M. Lerner & W. Damon (Eds.), Handbook of child psychology, Vol. 1: Theoretical models of human development (6th ed., pp. 793–828). Wiley.

Chen, F., Cui, Y., & Mou, Y. (2023). A phased prediction model for identifying at-risk students in online education. Computers & Education, 195, 104721. https://doi.org/10.1016/j.compedu.2022.104721 DOI: https://doi.org/10.1016/j.compedu.2022.104721

Creswell, J. W., & Creswell, J. D. (2023). Research design: Qualitative, quantitative, and mixed methods approaches (6th ed.). SAGE Publications.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008 DOI: https://doi.org/10.2307/249008

El Zaatari, W., & Maalouf, I. (2022). How the Bronfenbrenner bio-ecological system theory explains the development of students’ sense of belonging to school. SAGE Open, 12(4). https://doi.org/10.1177/21582440221134089 DOI: https://doi.org/10.1177/21582440221134089

Havik, T., Bru, E., &Ertesvåg, S. K. (2023). School-related emotional distress and school absence: The role of peer relationships and academic problems. Social Psychology of Education, 26, 1–26. https://doi.org/10.1007/s11218-022-09744-4

Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., Santos, O. C., Rodrigo, M. T., Cukurova, M., Bittencourt, I. I., & Koedinger, K. R. (2022). Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education, 32(3), 504–526. https://doi.org/10.1007/s40593-021-00239-1 DOI: https://doi.org/10.1007/s40593-021-00239-1

Hwang, G.-J., Xie, H., Wah, B. W., &Gašević, D. (2020). Vision, challenges, roles and research issues of artificial intelligence in education. Computers & Education: Artificial Intelligence, 1, 100001. https://doi.org/10.1016/j.caeai.2020.100001 DOI: https://doi.org/10.1016/j.caeai.2020.100001

Israel, G. D. (2013). Determining sample size (PEOD6). University of Florida IFAS Extension. https://edis.ifas.ufl.edu/pdffiles/PD/PD00600.pdf

Jörns-Presentati, A., Napp, A.-K., Dessauvagie, A. S., Stein, D. J., Jonker, D., Breet, E., Charles, W., Swart, R. L., Lahti, M., Suliman, S., Jansen, R., van den Heuvel, L. L., Seedat, S., & Groen, G. (2021). The prevalence of mental health problems in sub-Saharan adolescents: A systematic review. PLOS ONE, 16(5), e0251689. https://doi.org/10.1371/journal.pone.0251689 DOI: https://doi.org/10.1371/journal.pone.0251689

Joshi, A., Kale, S., Chandel, S., & Pal, D. K. (2015). Likert scale: Explored and explained. British Journal of Applied Science & Technology, 7(4), 396–403. https://doi.org/10.9734/BJAST/2015/14975 DOI: https://doi.org/10.9734/BJAST/2015/14975

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson Education.

National Population Commission. (2023). Federal Capital Territory, Abuja: Population and demographic statistics. National Population Commission of Nigeria.

Navarro, J. L., & Tudge, J. R. H. (2023). Technologizing Bronfenbrenner: Neo-ecological theory. Current Psychology, 42(22), 19338–19354. https://doi.org/10.1007/s12144-022-02738-3 DOI: https://doi.org/10.1007/s12144-022-02738-3

Oramah, L. O. (2018). Guidance and counselling in secondary schools in Nigeria: Issues and challenges. Journal of Education and Practice, 9(14), 124–130.

Ouyang, F., Zheng, L., & Jiao, P. (2022). Artificial intelligence in online higher education: A systematic review of empirical research from 2011 to 2020. Education and Information Technologies, 27(6), 7893–7925. https://doi.org/10.1007/s10639-022-10925-9 DOI: https://doi.org/10.1007/s10639-022-10925-9

Pallant, J. (2020). SPSS survival manual: A step by step guide to data analysis using IBM SPSS (7th ed.). McGraw-Hill Education. DOI: https://doi.org/10.4324/9781003117445

Plak, S., Cornelisz, I., Meeter, M., & van Klaveren, C. (2022). Early warning systems for more effective student counselling in higher education: Evidence from a Dutch field experiment. Higher Education Quarterly, 76(2), 255–274. https://doi.org/10.1111/hequ.12313 DOI: https://doi.org/10.1111/hequ.12298

Ramaswami, G., Susnjak, T., Mathrani, A., & Lim, J. (2023). Using educational data mining techniques to increase prediction accuracy: Early identification of at-risk students in higher education. Journal of Computers in Education, 10, 345–377. https://doi.org/10.1007/s40692-022-00230-6

Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. WIREs Data Mining and Knowledge Discovery, 10(3), e1355. https://doi.org/10.1002/widm.1355 DOI: https://doi.org/10.1002/widm.1355

Selwyn, N. (2022). Education and technology: Key issues and debates (3rd ed.). Bloomsbury Academic. DOI: https://doi.org/10.5040/9781350145573

Steare, T., González-Saavedra, C., O’Donnell, L., & Abel, K. M. (2023). The mental health of adolescents in England and the impact of COVID-19: Evidence from a longitudinal cohort study. Psychological Medicine, 53, 4553–4563. https://doi.org/10.1017/S003329172200076X

Susnjak, T., Ramaswami, G. S., & Mathrani, A. (2022). Learning analytics dashboard: A tool for providing actionable insights to learners. International Journal of Educational Technology in Higher Education, 19, 12. https://doi.org/10.1186/s41239-021-00313-7 DOI: https://doi.org/10.1186/s41239-021-00313-7

Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48(6), 1273–1296. https://doi.org/10.1007/s11165-016-9602-2 DOI: https://doi.org/10.1007/s11165-016-9602-2

UNESCO. (2023). Technology in education: A tool on whose terms? UNESCO Global Education Monitoring Report. UNESCO Publishing. https://www.unesco.org/gem-report/en/2023

UNICEF. (2022). The state of the world’s children 2021: On my mind — Promoting, protecting and caring for children’s mental health. UNICEF. https://www.unicef.org/reports/state-worlds-children-2021

Warren, J. M., Dobalian, A., & Silas, M. (2022). School counselors and teachers collaborating for student mental health: Barriers and facilitators to effective practice. Professional School Counseling, 26(1), 1–11. https://doi.org/10.1177/2156759X221125628

Williamson, B., &Kizilcec, R. F. (2022). Learning analytics and educational data mining at the crossroads of ethics, privacy, and surveillance. Learning Analytics & Knowledge Conference Proceedings. ACM. https://doi.org/10.1145/3506860.3506905 DOI: https://doi.org/10.1145/3506860.3506905

World Health Organization. (2021). Comprehensive mental health action plan 2013–2030. World Health Organization. https://www.who.int/publications/i/item/9789240031029

World Health Organization. (2022). World mental health report: Transforming mental health for all. World Health Organization. https://www.who.int/publications/i/item/9789240049338

Yağcı, M. (2022). Educational data mining: Prediction of students’ academic performance using machine learning algorithms. Smart Learning Environments, 9, 11. https://doi.org/10.1186/s40561-022-00192-z DOI: https://doi.org/10.1186/s40561-022-00192-z

Yamane, T. (1967). Statistics: An introductory analysis (2nd ed.). Harper and Row.

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education: Where are the educators? International Journal of Educational Technology in Higher Education, 16, 39. https://doi.org/10.1186/s41239-019-0171-0 DOI: https://doi.org/10.1186/s41239-019-0171-0

Additional Files

Published

2026-06-15

Issue

Section

Education, Technology, and Scientific Innovation

How to Cite

Wilson-Woko, V. C., Nasara, B. ., Chukwu, M. C., Peter, J. A., & Iyala, F. E. (2026). AI-enabled school management dashboards for the early identification of student mental-health risks in secondary schools in the federal capital territory, Abuja. Journal of Interdisciplinary Studies in Education, 15(4), 23-44. https://doi.org/10.32674/fdm79p37