<p>User stories play a crucial role in agile software development because of their structured format and ease of implementation. However, development teams face the challenging task of managing the variety of information required from multiple sources to craft user stories manually. Furthermore, poor-quality user stories can hinder communication among team members, potentially causing delays or leading to errors in the development process. We created a text prediction model to assist in drafting user stories, aiming to reduce writing errors and accelerate the specification process. We conducted a controlled experiment with sixteen participants split into experimental and control groups. Every group was invited to write user stories using a sample of software requirements as a reference. The first group utilized our text prediction model to auto-complete sentences, while the second group created user stories independently. To assess the quality of the user stories, we employed the AQUSA tool, which evaluates both syntactic and pragmatic aspects. Our analysis revealed that 75.6% of the defects identified were from the control group, compared to only 24.4% from the experimental group. Additionally, we conducted a post-experiment survey to gather participant feedback. The results confirmed that software practitioners are interested in adopting text generation for user stories. The experimental group agreed that our model sped up the writing process and partially agreed that it improved the quality of their user stories.</p>