The Role AI Assisted Reading Tools in Improving Comprehension in North-West Senior Secondary Schools, in Nigeria
DOI:
https://doi.org/10.65118/comprehensive.v2i3.30Keywords:
Academic Texts, Artificial Intelligence, Comprehension, Digital Literacy, Reading ToolsAbstract
This study examined the role of AI-assisted reading tools in improving reading comprehension among senior secondary school students in North-West Nigeria. The study was guided by three objectives, research questions, and hypotheses focusing on the effect of AI tools on reading comprehension, the extent of improvement in understanding academic texts, and the challenges associated with their use. A descriptive survey research design was adopted, involving a sample of 384 students selected through a multistage sampling technique. Data were collected using a structured questionnaire titled “AI-Assisted Reading Tools and Students’ Reading Comprehension Questionnaire,” validated by experts and tested for reliability using Cronbach Alpha, which yielded a coefficient of 0.89, indicating high internal consistency. Data analysis was conducted using descriptive statistics (mean and standard deviation) and inferential statistics (independent samples t-test and Pearson correlation) at a 0.05 level of significance. Findings revealed that AI-assisted reading tools significantly improve students’ reading comprehension, with students who use such tools performing better than those who do not. The study also established a strong positive relationship between the use of AI-assisted reading tools and students’ understanding of academic texts. Furthermore, it was found that challenges such as poor internet connectivity, high cost of data, limited digital literacy, inadequate access to devices, and distractions significantly hinder effective use of AI tools in learning environments. The study concludes that while AI-assisted reading tools enhance students’ comprehension and academic engagement, their effectiveness is influenced by infrastructural and skill-based limitations. It recommends improved digital infrastructure, teacher training, and affordable access to technology to maximize the benefits of AI in education.
References
Abbas, Z., Hussain, A., Abbas, S. Z., & Ahmad, N. (2025). Integrating artificial intelligence into reading comprehension instruction: Opportunities and pedagogical strategies. Research Journal for Social Affairs, 3(6), 97–109.
Adeyemi, T. O., & Salisu, M. (2025). Artificial intelligence and reading comprehension among secondary school students in Nigeria. African Journal of Educational Technology, 10(2), 45–58.
Alazemi, A. F. T. (2024). Formative assessment in AI-integrated instruction and reading comprehension outcomes. Language Testing in Asia, 14(44), 1–15.
Alanazi, N. B. H. (2025). Reading Arabic texts using AI: Challenges and innovations. International Journal of Research in Education and Psychology, 11(1), 22–35.
Almeida, F., & Simoes, J. (2025). The role of artificial intelligence in improving student learning outcomes. Interactive Learning Environments, 33(4), 567–580.
Alsalami, A. I., & Mohammed, A. (2025). Enhancing reading fluency using AI-supported tools. Arab World English Journal (CALL Special Issue), 45–60.
Chen, Y., & Lin, H. (2025). Effects of AI-assisted reading tools on students’ comprehension skills. Computers & Education, 198, 104789.
Clark, R. C., Nguyen, F., & Sweller, J. (2023). Efficiency in learning: Evidence-based guidelines to manage cognitive load. Wiley.
de Jong, T. (2023). Cognitive load theory and instructional design: Reflections and applications. Instructional Science, 51(2), 165–179.
Fiorella, L. (2023). Generative learning and technology-supported instruction. Journal of Educational Psychology, 115(3), 345–360.
Fiorella, L., & Mayer, R. E. (2023). Learning as a generative activity: Eight learning strategies that promote understanding. Cambridge University Press.
Fu, Y., Wester, J., Van Berkel, N., & Hiniker, A. (2026). Self-regulated reading with AI support: An eight-week study with students. arXiv preprint arXiv:2602.09907.
García-Martínez, I., López, M., & Ruiz, J. (2026). Enhancing classroom reading through artificial intelligence technologies. Educational Technology Research and Development, 74(1), 123–140.
Hidayat, M. T. (2024). Effectiveness of AI-based personalised reading platforms in enhancing reading comprehension. Journal of Learning for Development, 11(1), 115–125.
Huang, X., Zhang, Y., & Li, Q. (2026). Artificial intelligence and vocabulary development in reading comprehension. Journal of Educational Computing Research, 64(2), 210–228.
Kalyuga, S. (2023). Managing cognitive load in adaptive learning systems. Educational Technology Research and Development, 71(3), 987–1005.
Khalil, M., & Er, E. (2026). Students’ perceptions of AI tools in academic reading. Education and Information Technologies, 31(2), 1453–1470.
Kirschner, P. A., & Hendrick, C. (2024). How learning happens (2nd ed.). Routledge.
Kohnke, L., & Moorhouse, B. L. (2025). Adopting AI tools for reading instruction: Effects on student learning outcomes. TESOL Quarterly, 59(1), 89–110.
Mayer, R. E. (2001). Multimedia learning. Cambridge University Press.
Mayer, R. E. (2024). Multimedia learning (3rd ed.). Cambridge University Press.
Moreno, R., & Mayer, R. E. (2023). Interactive multimodal learning environments and cognitive theory: Implications for instructional design. Educational Psychology Review, 35(2), 1–20.
Muazu, A. M. (2026). Assessing the impact of artificial intelligence tools on reading proficiency among undergraduate students in Nigeria. Faculty of Education Journal, 10(5), 11-21.
Okafor, C. N., & Ibrahim, S. (2026). Teacher readiness and challenges in the integration of AI tools in Nigerian secondary schools. Nigerian Journal of Educational Research, 21(1), 67–82.
Ojo, A. (2024). Reading strategies in the AI age: Enhancing comprehension and engagement with advanced technologies. iSTEAMS Conference Proceedings.
Oktavianti, I. N., Budiwati, T. R., Prayudha, P., Prayogi, I., & Hidayah, I. N. (2026). Students’ attitudes, challenges, and needs in English reading comprehension: Foundations for AI-integrated pedagogy. Discover Education, 5(34), 1–12.
Paas, F., & van Merriënboer, J. J. G. (2023). Cognitive load theory and instructional design: Recent developments. Instructional Science, 51(1), 1–10.
Rahman, M. M., Hossain, M. A., & Karim, R. (2025). Digital literacy and the use of AI in education: Implications for developing countries. International Journal of Educational Development, 102, 102847.
Schnotz, W. (2023). Integrated model of text and picture comprehension in digital environments. Educational Psychology Review, 35(1), 45–68.
Shanta, S. (2026). Artificial intelligence integration in reading pedagogy: A systematic review (2019–2024). International Journal of Education and Literacy Studies, 14(1), 48–60.
Silor, A. C., & Silor, F. S. C. (2025). Boosting reading comprehension through AI-based learning tools. International Journal of Learning, Teaching and Educational Research, 24(9), 61–79.
Sweller, J., Ayres, P., & Kalyuga, S. (2024). Cognitive load theory (2nd ed.). Springer.
Tessensohn, T. C., Yunus, M. M., & Ismail, H. H. (2025). Using AI-powered tools in enhancing reading skills in the ESL classroom: A systematic review (2020–2024). International Journal of Academic Research in Progressive Education and Development, 14(2), 1–15.
Wang, L., & Liu, J. (2025). AI-based text simplification and its impact on reading comprehension. Journal of Computer Assisted Learning, 41(3), 455–469.
Yusuf, A., & Bello, H. (2026). Infrastructure challenges in adopting artificial intelligence in Nigerian schools. Journal of Education and Development in Africa, 8(1), 33–49.
Zhang, T., & Chen, S. (2025). The impact of AI-generated summaries on students’ critical reading skills. Educational Psychology Review, 37(2), 289–305.
This work is licensed under a 