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Integrating artificial intelligence in education can revolutionize how educational resources are generated, and assessments are conducted. However, current automated systems often struggle with precision, relevance, and usability issues, particularly in adapting to the specific needs of diverse educational contexts. This study addresses these challenges by developing and refining an automated syllabus generation and academic evaluation system, focusing on continuous user feedback and iterative adjustments. Our approach involved optimizing text processing algorithms to improve the system’s contextual understanding and incorporating customization options to align the generated content with course-specific objectives. The results were significant: the system’s evaluation precision increased from 78.5% to 89.7% over six months, and the relevance of the generated syllabi improved from 82.0% to 90.5%. Usability also saw a notable enhancement, with user satisfaction scores rising by 21.1%. These findings demonstrate that an adaptive, user-centered approach to education automation can effectively overcome current systems’ limitations, leading to more accurate, relevant, and user-friendly tools. By focusing on the iterative improvement of the system based on continuous feedback, we have developed a solution that not only meets the immediate needs of educators and students but also has the potential to scale and adapt to future educational challenges. |
