The impact of generative pre-trained transformers on creative writing instruction: Enhancing student engagement and expressive competence

Creative writing instruction plays a crucial role in developing students’ linguistic and cognitive abilities. However, challenges such as lack of engagement, difficulty in idea generation, and limited stylistic diversity hinder students from fully expressing their thoughts. Artificial intelligence (AI) offers a promising solution to enhance writing quality and creativity. This study aims to develop an AI-assisted creative writing framework by integrating GPT-4 with Innovative Locust Swarm Optimization (ILSO) to generate more engaging, coherent, and stylistically rich text tailored to students’ writing levels. A dataset of student-written essays, novels, and poetry was collected for training. Pre-processing techniques, including text normalization and tokenization, were applied to refine the input text. Feature extraction was performed using Word2Vec embedding to enhance semantic understanding. GPT-4 generates adaptive text suggestions, while ILSO optimizes model hyperparameters to refine text coherence, creativity, and narrative flow. The optimized model adapts to individual writing styles, offering dynamic suggestions that encourage creativity while maintaining fluency. The ILSO algorithm fine-tunes the generation process by enhancing text structuring and thematic consistency. The proposed method was implemented using Python 3.10.1. Experimental results demonstrate that the optimized GPT model significantly improves coherence scores, stylistic variation, thematic consistency, writing proficiency, and engagement rates. However, concerns regarding AI dependency and originality necessitate a balanced integration of AI-assisted and traditional writing pedagogy. This study provides a foundation for future adaptive AI-driven creative writing instruction, with potential extensions including real-time feedback systems and self-learning mechanisms for personalized writing enhancement.