###### Abstract
Firstly, they include splitting requirements (usually in the form of user stories) into tasks; secondly, requirement estimate is calculated indirectly after estimating and summarising all generated particular tasks [12]. The research presented in this paper employs the same approach. The changed nature of effort estimation in agile development, having several positive aspects, even resulted from the [PERSON] advice (e.g., breaking down the task, frequent re-estimation, an iterative approach, systematically added buffers and others), also comes with drawbacks. One of the most obvious is an important slice of the development time devoted for estimations. When teams perform the estimations themselves, planning-Mondays are quite a labour consuming activity, which, in a two-week long iteration, can easily mean 5-10% of the developers' time that could otherwise be used for a development. Several adjustments of by-the-book agile development methods are employed frequently to improve the development team's effectiveness. One of them is also estimation that is not carried out by the development team. Expert judgements, combined with analogies, are a replacement that is employed frequently [8; 9]. As we demonstrate in Section 3, several artificial intelligence (AI)-based techniques have already been used for decades for either relieving developers of effort estimation and/or improving estimation accuracy. However, the rapid AI development in recent years, including generative AI and large language models (LLM), resulted in automated tasks that were unimaginable for automation a decade ago. Automated effort estimation in combination with LLM tools is a tempting idea. However, using a novel and nonverified approach in such an important activity with confidence and trust can be risky. On the other hand, even the large group of LLM accepts [19] have positioned an effort estimation as a task with low risks, when LLM-basedchatbot-automated (see Figure 1). Estimates are not hard to verify. However, it is important to have them be accurate, which classifies effort estimation to tasks that can be automated, according to the authors in [19]. At the end of the day, several software development platforms have already included the generative-AI- and LLM-based functionalities that enable task generation and their assessment [20]. Based on the presented background, we decided to put a selected generative-AI- and LLM-based (AI-based) effort estimation assistant (see Section 3) on trial by employing a controlled experiment during an iteration-long development. The development teams were asked to split the requirements into tasks and estimate and implement them. We analysed the empirical data that we gathered during the experiment to compare conventional (using years-proven techniques and tools) and generative-AI-based (LLM-based chatbots) effort estimation while answering the following research questions:  
* **RQ1**: Is there a difference between tasks created using conventional and generative-AI-supported tools? **- RQ1.1**: Is a set of tasks, created by generative-AI-supported tools, comparable to a set of tasks created using conventional methods? **- RQ1.2**: Are effort estimations created by generative-AI-supported tools comparable to effort estimations created using conventional methods? * **RQ2**: How accurate is the effort estimation generated by a generative-AI-supported tool compared to conventional estimation methods? * **RQ3**: Which aspects of effort estimation can be backed by generative-AI-supported tools? The rest of the paper is organised as follows. In the next Section, we outline the field of employing AI-related techniques in effort estimation. Next, other authors' research related to ours is highlighted. We are employing generative AI as a tool for creating tasks, their estimation and possible verifications of such an approach. In Section 4, we describe our research method in detail, followed by stating the raw empirical data generated by the controlled experiment.