Design and Evaluation of a Hybrid RPA-AI Framework for Intelligent Transcript Processing in Higher Education
DOI:
https://doi.org/10.22399/ijcesen.3866Keywords:
Robotic Process Automation (RPA), Artificial Intelligence (AI), Optical Character Recognition (OCR), Natural Language Processing (NLP), Higher Education AdministrationAbstract
The administrative processes in higher education, especially those related to the generation and verification of academic transcripts, are typically manual, labour-intensive and subject to error. This paper proposes and tests a hybrid Robotic Process Automation (RPA) and Artificial Intelligence (AI) solution that would be used to automate and streamline the processing of transcripts. The framework will utilise RPA, which has been implemented in UiPath, for structured automation procedures like ingesting data, generating transcript templates, formatting, and automated dispatch. It embeds AI functionality, Optical Character Recognition (OCR) to extract text and Natural Language Processing (NLP) to validate semantic information, so that unstructured and semi-structured records are intelligently processed. The model was tested in a controlled case study using the Kaggle Student Performance Dataset as a proxy measure of transcript records. The results yielded a 70% decrease in processing time, an error rate of below 1% as compared to 6% in manual processes, and a consistent performance at scale, on transcript batches ranging in the tens of thousands. In addition, the framework also incorporates security standards, such as FERPA and GDPR compliance, encryption, role-based access control and audit logging, making it efficient and data-secure. The proposed system will contribute to the growing sphere of intelligent automation in education, as it is scalable, compliant, and adaptable. It offers a practical roadmap of digital transformation to universities, increasing efficiency of operations and preserving the institution's trust and student privacy
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