Artificial Intelligence In Education: Redefining Curriculum Design And Optimizing Learning Outcomes Through Data-Driven Personalization

Abstract

Purpose: This scholarly work explores how AI can be used to promote the curriculum development process and result in improved learning outcomes of the learners in a personalized learning approach in learning institutions. It looks at how education AI is applied to set individually tailored courses and how effective such a personalized solution is. This paper also explores other variables impacting the utilization and success of AI in learning, familiarity with AI, position in learning, and years of experience.

Objective: Thus, the purpose of this study is to evaluate the effectiveness of the use of AI in the context of the curriculum and to examine the current deployment of AI in learning and teaching context based on teachers, learners, and educational managers’ perception. In analyzing these relations, the study seeks to establish the practical proof of AI applications in enhancing learning processes and challenges that hinder their practice.

Methodology: The research approach used was quantitative and cross-sectional survey data was obtained from 200 respondents, teachers, school leaders, and students. The information was collected from an on-line questionnaire, including 70 items calibrated into closed type questions. This paper sought to establish the level of awareness of participants on AI, how they employ AI tools and their attitudes towards the effect of AI in the area of curriculum and learning. These variables were analyzed for relationships and inter-relationships between them using Chi-Square tests and other tests such as Spearman correlation coefficients and Kruskal-Wallis tests as well as simple logistic regression analysis. Different kinds of graphs were used that included bar graphs, scatter diagrams and box plots to supplement the statistical analysis results while explaining the results and the data analyzed.

Results: Regarding the Role in Education and Use of AI-driven Tools there was no relationship between these variables (Chi-Square statistic = 2.178, p-value = 0.703, degrees of freedom = 4) which means that there is no distinction if a person occupy a high or low position in the education sector, the AI tools are being used. Also, no correlation was found between the extent of AI enhancing the curriculum and the degree to which AI-based personalization enhances the outcomes (Chi-Square statistic = 13.113, p-value = 0.361, degrees of freedom = 12). The Spearman correlation between Familiarity with AI and Belief in AI Improving Learning Outcomes was weak positive and non-significant correlation, with a correlation coefficient of 0.033 and p-value of 0.638. The Kruskal-Wallis test comparing the Extent AI Enhances Curriculum between the different roles in education also provided no discriminating values (Kruskal-Wallis statistic = 2.767, p-value = 0.598). Converging with the findings in the Logistic Regression analysis, Years of Experience was found as a predictor in the study as educators with less Years of Experience have significantly lower beliefs about AI effectiveness (coefficient = -1.186, p-value = 0.031).

Practical Implications: The study reveals that while AI-driven tools are being widely adopted across educational roles, their perceived impact on learning outcomes is not as straightforward as expected.  However, for AI to be integrated rightly into curriculum design, more training and resource support should be provided such educationists especially those with less experience. Also, it is suggested that the educators or administrators who assume that the AI can significantly enhance their learning outcomes should consider shifting their attention to the particular types of AI that are more consistent with effective learning and instructional practices.

Novelty: This study contributes to existing literature on AI in education, highlighting research on the application of AI tools in curriculum development and perceived practical impacts of these tools on learning outcomes. Hence, instead of complicating the possibilities of utilizing AI with pedagogy, this work presents the experiences of individuals, educators and students, and therefore provides a useful outlook on AI use in learning.

Conclusion: As a result, the study finds that although the use of AI for curriculum improvement and improved learning achievement is promising, its effectiveness is a factor of how it is deployed and the environment in which it is applied. The results imply to promote professional development for the schools especially less-experienced teachers and underlined the evidence of integrating AI tools with effective instructional methods to enhance the learning procedures. Moreover, the present research discusses the ethical issues and potentials issues of AI use in education, which outline the directions for further research; such as, the potential effects of AI on academic achievement and the AI equality.