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Before interacting with real users, developers must be proficient in human--computer interaction (HCI) so as not to exhaust user patience and availability. For that, substantial training and practice are required, but it is costly to create a variety of high-quality HCI training materials. In this context, chat generative pretrained transformer (ChatGPT) and other chatbots based on large language models (LLMs) offer an opportunity to generate training materials of acceptable quality without foregoing specific human characteristics present in real-world scenarios. Personas is a user-centered design method that encompasses fictitious but believable user archetypes to help designers understand and empathize with their target audience during product design. We conducted an exploratory study on the Personas technique, addressing the validity and believability of interviews designed by HCI trainers and answered by ChatGPT-simulated users, which can be used as training material for persona creation. Specifically, we employed ChatGPT to respond to interviews designed by user experience (UX) experts. Two groups, HCI professors and professionals, then evaluated the validity of the generated materials considering quality, usefulness, UX, and ethics. The results show that both groups rated the interviews as believable and helpful for Personas training. However, some concerns about response repetition and low response variability suggested the need for further research on improved prompt design in order to generate more diverse and well-developed responses. The findings of this study provide insight into how HCI trainers can use ChatGPT to help their students master persona creation skills before working with real users in real-world scenarios for the first time.

Background: During the last fifty years, improved information retrieval techniques have become necessary because of the huge amount of information people have available, which continues to increase rapidly due to the use of new technologies and the Internet. Stemming is one of the processes that can improve information retrieval in terms of accuracy and performance. Aim: This paper provides a detailed assessment of the current status of the stemming process framed in an information retrieval application field by tracing its historical evolution. Method: Papers presenting the first approaches for stemming were reviewed to extract their main features, benefits and drawbacks. Additionally, papers dealing with stemmers for non-English languages or with some more recent proposals were also consulted and compiled. Finally, experimental papers defining the most well-known methods and metrics aimed at evaluating and classifying stemmers were also taken into account to expose their contributions and results. Results: Even if not all researchers agree on the benefits and drawbacks of using stemming in an information retrieval process in general terms, many of them agree on its benefits in specific contexts, such as when the language is highly inflective, when documents are short or when there is limited space for storing data. Some researchers also state that the nature of the documents can influence the performance and the accuracy of the stemmer. Conclusions: Despite many researchers having investigated this field over many years, there are still some open questions, such as how to evaluate a stemmer independently of the information retrieval process, or how much a stemmer improves an information retrieval application in terms of speed. As a summary, some guidelines are also provided to help readers to determine which is the best stemmer for their needs and the tasks they have to carry out.