Automating NCAAA Accreditation Process with GPT-4 API

In the educational world, leveraging advanced technology, particularly for accreditation tasks, presents a promising avenue for enhancing efficiency and user experience. This study implements a web application integrating the GPT-4 model via OpenAI's Application Programming Interface (API) to streamline the National Commission for Academic Accreditation & Assessment (NCAAA) accreditation for Computer Science postgraduate programs at King Abdulaziz University (KAU), Saudi Arabia. Traditionally, fulfilling these requirements entailed a substantial workload, including crafting detailed course reports and updating assessment questions to align with Course Learning Outcomes (CLOs) and Bloom's Taxonomy levels, typically consuming about 5 h per course, resulting in delayed submission. Our solution employs a GPT-4 Large Language Model (LLM) with prompt engineering and OpenAI's API to automate the drafting of course reports and the generation of assessment questions, effectively reducing the task completion time by approximately 90% and encouraging timely submissions. The system's asynchronous design allows for automated background processing, employing a modular architecture to improve development and testing in a software engineering manner. Preliminary user feedback attests to the system's capacity to significantly ease the accreditation process burden, attributed to its intuitive user interface, autocomplete functionalities, and the capability to upload draft questions for assessments. This research demonstrates the potential of Artificial Intelligence (AI), particularly LLM and prompt engineering techniques, to improve manual accreditation tasks but also supports wider adoption and further exploration of such technologies in academic settings, thereby making the accreditation process more efficient across university departments in the Kingdom.