Harnessing Artificial Intelligence in Teaching Ghanaian Colleges of Education in the 21st Century: Enhancing Quality Teaching, Student Research, and Learning Abilities

Authors

DOI:

https://doi.org/10.54536/ajmri.v4i3.4457

Keywords:

AI Applications, Artificial Intelligence, Education Technology, Ghana, Teacher Education

Abstract

This study investigates the potential of Artificial Intelligence (AI) to transform teaching and learning practices within Ghanaian Colleges of Education in the 21st century. It explores the opportunities AI offers to enhance the quality of teaching, foster student research capabilities, and improve overall learning outcomes. The research examines specific AI applications relevant to the Ghanaian context, focusing on tools such as ChatGPT, Scite-ci, Ellicit, Mendeley, Zotero, Google Scholar Litmaps, Quillbot, and PaperPal alongside more general applications like personalized learning platforms, intelligent tutoring systems, AI-powered assessment tools, and AI-driven research assistants. The study investigates how these technologies can address challenges such as teacher workload, access to resources, and diverse student needs. Methodologically, the study employed a mixed-methods approach, combining a review of relevant literature with both qualitative and quantitative data collection. A sample of 400 teacher trainees from four colleges of education in Ghana participated and were selected using a stratified sampling technique. Data collection utilized surveys, interviews, classroom observations, and questionnaires to comprehensively understand the current state and perceived impact of AI. Data analysis focused on identifying perceived benefits, challenges, and best practices for integrating AI and triangulating findings across multiple data sources. The findings highlight the need for strategic investments in infrastructure, teacher training, and policy frameworks to facilitate the successful adoption of AI in teacher education. The study concludes by offering recommendations for policymakers, college administrators, and educators to effectively harness the power of AI to prepare future teachers for the demands of the 21st-century classroom and equip them to foster lifelong learning in their students.

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Author Biography

  • Luke Boryang Liekum, Gbewaa College of Education, Pusiga-Bawku, Ghana

    Luke Boryang LIEKUM, MPhil,

    Tutor, Department of French Gbewaa College of Education P.O.Box 157, Pusiga Bawku, Ghana
    Contact: +233209407911/+233249435300 liekum@gmail.com lbliekum@st.ug.edu.gh  

References

Adu-Gyamfi, K., Owusu-Ansah, A., & Agyei, D. D. (2020). Exploring the use of technology in teacher education: A case study of colleges of education in Ghana. International Journal of Educational Technology in Higher Education, 17(1), 24-45. https://doi.org/10.1186/s41239-020-00193-7

Ampadu, E., & Osei, R. (2020). Technology in education: A study of the use of information and communication technology in teacher education in Ghana. International Journal of Teaching and Education, 8(1), 1-20. http://doi.org/10.20472/TE.2020.8.1.001

Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. In R. K. Sawyer (Ed.), Cambridge Handbook of the Learning Sciences (2nd ed., pp. 255-276). Cambridge University Press. https://doi.org/10.1017/CBO9781139519526.014

Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.

Carnegie Learning. (2021). Personalized learning. https://www.carnegielearning.com/

Demartini, G., O’Neill, E., & Iodice, R. (2018). AI in the classroom: How artificial intelligence can improve the research process. International Journal of Educational Technology in Higher Education, 15(1), 10-25. https://doi.org/10.1186/s41239-018-0103-8

Forsyth, D. A., & Ponce, J. (2003). Computer vision: A modern approach. Prentice Hall.

Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 7(1), 59-109.

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Technology in Learning. https://www.brookings.edu/research/artificial-intelligence-in-education-promises-and-implications-for-teaching-and-learning/

Jurafsky, D., & Martin, J. H. (2009). Speech and language processing. Pearson.

Kalantzis, M., & Cope, S. (2012). Learning by design: A cultural history of learning and technology. Cambridge University Press.

Knewton. (2021a). Adaptive learning platform. https://www.knewton.com/

Knewton. (2021b). Knewton Alta: The adaptive learning platform for higher education.

Lai, M., Hwang, G., & Chen, C. (2020). The effect of learning analytics and AI-enhanced learning environments on student engagement: A systematic review. Computers & Education, 157, 103911.

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-44.

Lipton, Z. C., & Stein, A. (2018). The mythos of model interpretability. Proceedings of the ACM Conference on Fairness, Accountability, and Transparency, 2018, 1-12.

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson. https://www.pearson.com/content/dam/one-dot-com/one-dot-com/global/Files/news/2016/luckinreport_web.pdf

Mitchell, T. M. (1997). Machine learning. McGraw-Hill.

O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Publishing Group.

Opoku-Ameyaw, K., Kwarteng, J., & Asare, R. (2017). Teacher education in Ghana: Issues, challenges and prospects. Journal of Education and Practice, 8(11), 101–110. https://www.iiste.org/Journals/index.php/JEP/article/view/36170

Osei-Tutu, E., Nkansah, M. A., & Aggor, R. (2021). Bridging the digital divide in teacher education: Challenges and prospects for teacher training in Ghana. Journal of Education and Learning, 10(1), 23–34. https://doi.org/10.5539/jel.v10n1p23

Pashler, H., Bain, P. M., & Bottge, B. A. (2009). A synthesis of principles for learning. In Learning and Instruction (pp. 39-60). Routledge.

Piaget, J. (1973). The child and reality: Problems of genetic psychology. Basic Books.

Rumelhart, D. E., McClelland, J. L., & the PDP Research Group. (1986). Parallel distributed processing: Explorations in the microstructure of cognition (Vol. 2). MIT Press.

Russell, S. J., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.

Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1–19. https://doi.org/10.1177/0002764213491767

Smart Sparrow. (2021). Adaptive eLearning platform. https://smartsparrow.com

Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT Press.

Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.

Walkington, C. (2013). Personalized learning: A guide for engaging students with technology. The Education Digest, 79(6), 28–33.

Wang, Y., Goh, K. M., & Tan, J. G. (2019). Implementing ethical AI in education: Balancing innovation and over-expectation. In S. Isotani, E. Millán, A. Ogan, P. Hastings, B. McLaren, & R. Luckin (Eds.), Artificial intelligence in education: 20th International Conference, AIED 2019 (pp. 354–368). Springer. https://doi.org/10.1007/978-3-030-29736-7_29

Waterman, D. A. (1986). A guide to expert systems. McGraw-Hill.

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Published

2025-05-22

How to Cite

Liekum, L. B. (2025). Harnessing Artificial Intelligence in Teaching Ghanaian Colleges of Education in the 21st Century: Enhancing Quality Teaching, Student Research, and Learning Abilities. American Journal of Multidisciplinary Research and Innovation , 4(3), 191-204. https://doi.org/10.54536/ajmri.v4i3.4457

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