Exploring the Capabilities of GPT Models in Drafting Course Assessments Based on Bloom’s Taxonomy - Muhamad, Gilang Aulia; Alsulami, Bassma Saleh; Thabit, Khalid Omar (JOIV : International Journal on Informatics Visualization, 2025)
The application of Generative Pre-trained Transformer (GPT) models is significantly essential in automating drafting course assessment based on Bloom’s Taxonomy, specifically GPT-3.5-turbo, GPT-4, and GPT-4o. Therefore, this study aimed to explore the interaction between Artificial Intelligence (AI) models and educational content using refined prompt engineering methods to enhance the accuracy and relevance of the generated questions. For the investigation, the processing 146 Course Learning Outcomes (CLOs) method was applied through each model using OpenAI Application Programming Interface (API). Metrics such as 'Accuracy', 'Precision', 'Recall', and 'F1 Score' were used to assess the performance of each model. The results showed that GPT-4 was suitable for complex course assessments, showing superior performance in delivering detailed and precise responses. A cost-effective solution was obtained using GPT-3.5-turbo for generating simpler course assessment, while GPT-4o provided a middle ground, balancing cost, and performance. The results showed the potential of AI to reduce the administrative burden on instructors by streamlining the creation and refinement of course assessments. The enhancement of course assessments was also facilitated by automation, thereby supporting more adaptive questions. The potential for broader AI integration into educational practices promised a transformative impact on traditional course assessment drafting methods, enabling more dynamic and educational experiences. Moreover, further studies were recommended to explore the ethical dimensions of AI in education, the ability to handle diverse tasks, as well as assess the long-term impacts on learning outcomes and educational equity.