The purpose of this study is to investigate how artificial intelligent tools can be deployed to support teaching and learning. As developing countries grapple with the massive migration of educators to developed countries. There is an urgent need to provide solutions to bridge the impact of teacher shortages. In this study, the design science (DS) approach was used to build and evaluated an adaptive artificial intelligence (AI) system that can be used in the education domain to support teaching and learning. A student-cantered approach was used that encompasses the experiential learning technique.
This study investigates how the integration of adaptive AI systems, within the teaching and learning domain in developing countries could support teaching and learning. Using University of Technology in Jamaica as a case study. The study emphasizes AI’s revolutionary potential in tackling some of the educational challenges, particularly in the context of limited resources and the use of traditional teaching techniques. Demonstrating how an AI tutoring systems can improve educational outcomes, support teaching and learning, support students learning outcomes in the midst of the chronic teacher shortages in developing countries. mixed-method was used in conducting this study. Mixed-methods research involves using quantitative and qualitative techniques to comprehensively understand research (Doyle et al., 2009; Adeoye and Wentling, 2007; Wang, 2020). The DS approach in information systems is a methodological framework that guides developing and evaluating technical artifacts to solve specific, practical problems. According to Wieringa (2014), DS is a qualitative approach that involves iterating over designing an artifact that improves something for stakeholders and empirically investigating its performance in context. The DS approach focuses on the creation and performance evaluation of artifacts like methods, techniques and algorithms.
As the authors analysed the students experiences, it is evident that TeachLearnBot’s core functionalities were highly regarded, with a significant number of the students finding it effective in meeting their learning needs. It is a testament to TeachLearnBot’s foundational design, affirming that the system serves its educational purpose exceptionally well. The analysis presented in figure 14 indicated that students believed the system could support them significantly on their academic journey. After interacting with the prototype just over 82% of the student say they would strongly agree or agree to recommend the TeachLearnBot to other students. The overall feedback from lecturers was also positive.
The unavailability of facilitators to participate in the testing of the artifact. Research was limited to one department in the faculty.
Practically, the results of this research can provide valuable insight to governments and education policymakers on how to use existing resources to develop a personalized learning system that addresses the needs of students. In addition, this study can validate AI’s potential to support the narrowing of the existing educational gaps and promote equity in education.
This study can extend to other domains outside of higher educational institutions to support teaching and learning. Helping to bridge the learning gap. In addition, this model can be adopted within developing countries to support educators and students.
This paper adds value and extends the paucity of knowledge that exist relating to the adoption of AI systems or intelligent tutoring systems in the developing countries context.
