Optimizing Automated Educational Systems: Adaptive Syllabus Generation and Precise Assessment Using Artificial Intelligence
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 educ...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10758621/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846156535162470400 |
|---|---|
| author | William Villegas-Ch Jaime Govea Roque Albuja Diego Buenano-Fernandez Aracely Mera-Navarrete |
| author_facet | William Villegas-Ch Jaime Govea Roque Albuja Diego Buenano-Fernandez Aracely Mera-Navarrete |
| author_sort | William Villegas-Ch |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6f47a2a890684cce9ec53b6cceb77f30 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-6f47a2a890684cce9ec53b6cceb77f302024-11-26T00:01:36ZengIEEEIEEE Access2169-35362024-01-011217339017340910.1109/ACCESS.2024.350353210758621Optimizing Automated Educational Systems: Adaptive Syllabus Generation and Precise Assessment Using Artificial IntelligenceWilliam Villegas-Ch0https://orcid.org/0000-0002-5421-7710Jaime Govea1Roque Albuja2Diego Buenano-Fernandez3https://orcid.org/0000-0001-8123-2783Aracely Mera-Navarrete4Escuela de Ingeniería en Ciberseguridad, FICA, Universidad de Las Américas, Quito, EcuadorEscuela de Ingeniería en Ciberseguridad, FICA, Universidad de Las Américas, Quito, EcuadorEscuela de Posgrados, Maestría en Derecho Digital, Universidad de Las Américas, Quito, EcuadorEscuela de Posgrados, Maestría en Derecho Digital, Universidad de Las Américas, Quito, EcuadorDepartamento de Sistemas, Universidad Internacional del Ecuador, Quito, EcuadorIntegrating 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.https://ieeexplore.ieee.org/document/10758621/Automated educational systemssyllabus generationAI in educationevaluation precision |
| spellingShingle | William Villegas-Ch Jaime Govea Roque Albuja Diego Buenano-Fernandez Aracely Mera-Navarrete Optimizing Automated Educational Systems: Adaptive Syllabus Generation and Precise Assessment Using Artificial Intelligence IEEE Access Automated educational systems syllabus generation AI in education evaluation precision |
| title | Optimizing Automated Educational Systems: Adaptive Syllabus Generation and Precise Assessment Using Artificial Intelligence |
| title_full | Optimizing Automated Educational Systems: Adaptive Syllabus Generation and Precise Assessment Using Artificial Intelligence |
| title_fullStr | Optimizing Automated Educational Systems: Adaptive Syllabus Generation and Precise Assessment Using Artificial Intelligence |
| title_full_unstemmed | Optimizing Automated Educational Systems: Adaptive Syllabus Generation and Precise Assessment Using Artificial Intelligence |
| title_short | Optimizing Automated Educational Systems: Adaptive Syllabus Generation and Precise Assessment Using Artificial Intelligence |
| title_sort | optimizing automated educational systems adaptive syllabus generation and precise assessment using artificial intelligence |
| topic | Automated educational systems syllabus generation AI in education evaluation precision |
| url | https://ieeexplore.ieee.org/document/10758621/ |
| work_keys_str_mv | AT williamvillegasch optimizingautomatededucationalsystemsadaptivesyllabusgenerationandpreciseassessmentusingartificialintelligence AT jaimegovea optimizingautomatededucationalsystemsadaptivesyllabusgenerationandpreciseassessmentusingartificialintelligence AT roquealbuja optimizingautomatededucationalsystemsadaptivesyllabusgenerationandpreciseassessmentusingartificialintelligence AT diegobuenanofernandez optimizingautomatededucationalsystemsadaptivesyllabusgenerationandpreciseassessmentusingartificialintelligence AT aracelymeranavarrete optimizingautomatededucationalsystemsadaptivesyllabusgenerationandpreciseassessmentusingartificialintelligence |