AI-Powered Tutoring Systems for Personalised Learning Feedback in Developing Secondary Education Contexts

Authors

DOI:

https://doi.org/10.62411/faith.3048-3719-290

Keywords:

AI adoption, Artificial intelligence, Educational innovation, Intelligent tutoring systems, Mixed-methods research, Personalized learning, Secondary education, Socio-technical systems

Abstract

This study investigates how Artificial Intelligence (AI)-based Tutoring Systems can be used to provide secondary school students with personalized learning feedback and to enhance their academic performance. The two main empirical objectives were to assess individualized formative assessment in classroom settings and to examine teachers’ and students’ perceptions of AI adoption. A mixed-methods quasi-experimental design was employed, integrating quantitative pre-/post-tests, interviews, and focus groups across six schools (n = 600). This approach enabled triangulation between measurable learning outcomes and contextual perception data for robust validation. Quantitative data were analyzed using Python (t-tests, ANCOVA), while thematic coding in NVivo was applied to qualitative data. Expert review, pilot testing, and Cronbach’s α (>0.80) were used to validate the instruments and ensure reliability, including pre-/post-tests and engagement scales. Findings revealed that students who received AI-based interventions achieved significantly higher academic performance (Cohen’s d = 1.05) and engagement (d = 0.72) compared with control groups. Teachers with AI exposure reported greater preparedness (mean = 3.4) and fewer perceived barriers. The study provides empirical evidence on the pedagogical viability of AI tutoring in under-resourced contexts, contributing to self-regulated and socio-technical learning theories. It also recommends enhanced systemic teacher education, ethical leadership, and structural support to foster equitable adoption of AI in education. The findings carry strong implications for policy development and educational innovation in promoting data-driven, inclusive learning within Nigeria’s secondary education system

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Author Biographies

Danladi Moses Adayilo, National Open University of Nigeria

Africa Centre of Excellence on Technology Enhanced Learning, National Open University of Nigeria, Plot 91, Abuja 900108, Nigeria

Ishaq Oyebisi Oyefolahan, National Open University of Nigeria

Africa Centre of Excellence on Technology Enhanced Learning, National Open University of Nigeria, Plot 91, Abuja 900108, Nigeria

Juliana Ngozi Ndunagu, National Open University of Nigeria

Department of Computer Science and Information Technology, Faculty of Science, National Open University of Nigeria, Plot 91, Abuja 900108, Nigeria

Chinedu Otuya, National Open University of Nigeria

Africa Centre of Excellence on Technology Enhanced Learning, National Open University of Nigeria, Plot 91, Abuja 900108, Nigeria

Ebenezer Malcalm, Ghana Communication Technology University

Management Studies, Ghana Communication Technology University, Accra, PO Box 100, Accra North, Ghana

Khanyisile Twabu, University of South Africa

Department of Information Communication Technology, University of South Africa, Pretoria, 0002, South Africa

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Published

2025-12-09

How to Cite

[1]
D. M. Adayilo, I. O. Oyefolahan, J. N. Ndunagu, C. Otuya, E. Malcalm, and K. Twabu, “AI-Powered Tutoring Systems for Personalised Learning Feedback in Developing Secondary Education Contexts”, J. Fut. Artif. Intell. Tech., vol. 2, no. 4, pp. 549–564, Dec. 2025.

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