Filtered by themeUsability evaluation3 papersclear ✕

Predicting the usability of mobile applications using AI tools: the rise of large user interface models, opportunities, and challengesIndustry (EDI40) · 2024This article proposes the so-called large user interface models (LUIMs) to enable the generation of user interfaces and prediction of usability using artificial intelligence in the context of mobile applications. To this end, we synergized an integrated framework for the effective testing of the usability of mobile applications following a selective review of the most influential models of mobile usability testing. Next, we identified and analysed 13 recent AI tools that generate user interfaces for mobile apps, and systematically tested these tools to identify their AI capabilities. Our striking findings demonstrate that current generative UI tools fail to address mobile usability attributes, such as efficiency, learnability, effectiveness, satisfaction, and memorability. Our large UI models’ architecture proposes to leverage the capabilities of large language models, large vision models, and large code models to overcome the challenges of AI-driven UI/UX design and front-end implementations. This fascinating UI eco-system must be augmented with sufficient UI data and multi-sensory input regarding user behaviour to train the models. We anticipate LUIMs to create ample opportunities, like expedited frontend software development, enhanced personalised user experience, and wider accessibility of smart technologies. However, the research challenges hindering the UI generation and usability prediction of mobile apps include the seamless integration of complex generative AI models, semantic understanding of non-uniform visual designs, scarcity of UX datasets, and modelling of realistic user interactions. 672 Abdallah Namoun et al. / Procedia Computer Science 238 (2024) 671–682

UX of AI & PersonalizationResearch Papers

User experience evaluation methods: Current state and development needsArnold P.O.S. Vermeeren, Effie Lai-Chong Law, Virpi Roto, Marianna Obrist, Jettie Hoonhout, Kaisa Väänänen-Vainio-Mattila · 2010The recent shift of emphasis to user experience (UX) has rendered it a central focus of product design and evaluation. A multitude of methods for UX design and evaluation exist, but a clear overview of the current state of the available UX evaluation methods is missing. This is partly due to a lack of agreement on the essential characteristics of UX. In this paper, we present the results of our multi-year effort of collecting UX evaluation methods from academia and industry with different approaches such as literature review, workshops, Special Interest Groups sessions and an online survey. We have collected 96 methods and analyzed them, among other criteria, based on the product development phase and the studied period of experience. Our analysis reveals development needs for UX evaluation methods, such as early-stage methods, methods for social and collaborative UX evaluation, establishing practicability and scientific quality, and a deeper understanding of UX. Author Keywords User Experience, Evaluation method, Methodological development needs ACM Classification Keywords H5.2. User Interfaces (D.2.2, H.1.2, I.3.6): Evaluation/methodology

UX of AI & PersonalizationResearch Papers

Computer Science ReviewMaaruf Ali, Peter S. Excell · 2021A review of research on universal usability, plasticity of user interface design and facilitation of interface development with universal usability is presented. The survey was based on 165 research papers spanning over fifty-five years. The foundations of adaptive or intelligent user interfaces (AUI or IUI) are presented, three core domains being focused upon: Artificial Intelligence (AI), User Modelling (UM) and Human–Computer Interaction (HCI). For comparison of the various AUIs, a proposed taxonomy is given. One conclusion is that an efficient training vector for fast optimal convergence of the machine-learning algorithm is a necessity, but key to this is the bounding of the dataset, the goal being to achieve an accurate user preference model, which has to be built from a limited number of datasets obtained from the human interaction. More research also needs to be conducted to ascertain the usefulness and effectiveness of IUIs compared against AUIs. With the global mobility of users, interface design must take account of the abilities and cultures of users, derived from actual user behaviour and not on their feedback. A key question is whether the interface should be adaptive under system control or be made adaptable under user control. A need is identified for an ‘‘afferential component’’ that stores a priori information about the end user, an ‘‘inferential component’’ that determines to what extent the user interface actually needs to be adapted, and the ‘‘efferential component’’ that actually determines how the adaptivity is applied seamlessly to the system. Application to e-learning is a priority: the use of machine intelligence to achieve appropriate learnability, ideally enhanced by ‘‘Playful interaction’’, was found to be desirable. Universal application of adaptation lies in the future, but AUI properties cannot be ascertained while disregarding the other parameters of the system in which it will be used. A more complete understanding of the human mental model is necessary, requiring a highly multidisciplinary approach and cooperation between diverse researchers. Finally, a performance evaluation of plasticity of user interface was conducted: it is concluded that the use of dynamic techniques can enhance the user experience to a much greater extent than more basic approaches, although optimisation of usability parameter trade-offs needs further attention. It is noted that most of the work reviewed originated from a limited range of cultural pe

HCI & Adaptive InterfacesResearch Papers