The evaluation of an adaptive user interface model

The user interface (UI) layer is considered an important component in software applications since it links the users to the software's functionality. Enterprise applications such as enterprise resource planning and customer relationship management systems have very complex UIs that are used by users with diverse needs in terms of the required features and layout preferences. The inability to cater for the variety of user needs diminishes the usability of these applications. One way to cater for those needs is through adaptive UIs. Some enterprise software providers offer mechanisms for tailoring UIs based on the variable user needs, yet those are not generic enough to be used with other applications and require maintaining multiple UI copies manually. A generic platform based on a model-driven approach could be more reusable since operating on the model level makes it technology independent. The main objective of this research is devising a generic, scalable, and extensible platform for building adaptive enterprise application UIs based on a runtime model-driven approach. This platform primarily targets UI simplification, which we defined as a mechanism for increasing usability through adaptive behavior by providing users with a minimal feature-set and an optimal layout based on the context-of-use. This paper provides an overview of the research questions and methodology, the results that were achieved so far, and the remaining work.

Mobile computing devices and their applications that encompass context aware components are becoming increasingly more prevalent. The context-awareness of these types of applications typically focuses on the services offered. In this paper we describe a framework that supports the monitoring and analysis of mobile application usage patterns with the goal of updating user models for adaptive services and user interface personalisation. This paper focuses on two aspects of the framework. The first is the modelling and storage of the usage data. The second focuses on the data mining component of the framework, outlining the five different capabilities of the adaptation in addition to the algorithms used. The proposed framework has been evaluated through specific case studies, with the results attained demonstrating the effectiveness of the data mining capabilities and in particular the adaptation of the User Interface. The accuracy and efficiency of the algorithms used are also evaluated with three users. The results of the evaluation show that the aims of the data mining component were achieved with the personalisation and adaptation of content and user interface, respectively.

User interface adaptation has been proposed in recent years as a means to achieve personalized accessibility and usability of user interfaces. Related user interface architectures, as well as a variety of related development method and tools, have also been elaborated. Admittedly, however, despite the recognized validity of the approach, which has been proved in practice in a series of prototype systems, the wider adoption and uptake of user interface adaptation approaches are still limited. One of the identified obstacles is the complexity intrinsic in designing such interfaces and the need of radically revising the current user interface design practice to account for (a) the alternative designs required for adaptation, (b) the parameters involved in driving adaptations (i.e., selecting among alternatives at a given point during interaction), and (c) the logic of adaptation at runtime. This article proposes a twofold tool-based support strategy for user interface adaptation development, based on (a) an adaptation development toolkit and related widget library, which directly embeds lexical level adaptations into common interactive widgets, and (b) embedding such a library in a common integrated development environment, thus allowing designers to define and view alternative adaptations at design time and create adaptable user interfaces through traditional prototyping. The aforementioned approach has been implemented in the domain of adaptable applications for older users, producing tools that are currently in use in the development of a large suite of interactive applications in various domains. The approach presented in this article is claimed to be the first and so far unique supporting rapid prototyping of adaptable user interfaces, thus minimizing the divergence between typical development practices and user interface adaptation development.

In the contemporary digital era, individuals spend an increasing amount of time interacting with digital devices, leading to potential issues such as cognitive overload, digital fatigue, and diminished overall well-being. Traditional user interface (UI) designs often adopt a one-size-fits-all approach, failing to adapt to the dynamic cognitive and emotional states of users. This limitation can exacerbate negative user experiences and hinder productivity. Concurrently, the proliferation of digital interactions has produced vast amounts of behavioral data, including keystroke dynamics, which offer a non-invasive and continuous source of information about user states. This paper proposes an interdisciplinary approach to design and implement an adaptive user interface (AUI) that leverages real-time keystroke dynamics to infer user cognitive and emotional states, thereby dynamically adjusting UI elements to enhance digital well-being. Integrating principles from behavioral data analysis, human- computer interaction (HCI), and cognitive psychology, our research aims to address the critical need for more responsive and user-centric digital environments. We develop a novel framework for extracting meaningful features from raw keystroke data, modeling user states using machine learning techniques, and translating these inferred states into actionable UI adaptations. Through a series of controlled experiments, we demonstrate the efficacy of our AUI in mitigating cognitive load, improving task performance, and fostering a more positive user experience. Our findings highlight the significant potential of keystroke dynamics as a robust indicator of user well-being and provide a foundational blueprint for future advancements in intelligent and empathetic UI design. This work contributes to the growing body of knowledge at the intersection of technology, psychology, and design, paving the way for digital systems that are not only efficient but also inherently supportive of human flourishing.

Adaptive User Interfaces (AUI) have to exploit Artificial Intelligence powerful to adapt User Interfaces (UI)s to People With Disabilities (PWD). Thus, Machine Learning methods could master disabilities problems and barriers. In this paper, we propose a Reinforcement Learning (RL) based approach for resolving and surmounting PWD-UI interactions barriers since RL is good for learning good behavior. Hence, we have called the approach as RL-AUIAC as Reinforcement Learning of Adaptive User Interfaces for Accessibility Context. RL-AUIAC is based on three Knowledge Layers (KL)s depending on the kind of adaptation and the resolved problem at each layer. KL uses the Exploration-Exploitation dilemma to respond to the question: what to learn from each planned-adaptation sequences? In fact, Disability Knowledge Layer (DKL)learns UI structure behavior depending on the disability profile. Modality Knowledge Layer(MKL) learns facilities of adaptation on the basis of the couple . Platform Knowledge Layer (PKL) explores-exploits platform-knowledge to learn adaptation facilities on the basis of .

This work presents the design and analysis of an Adaptive User Interface (AUI) for a desktop application that uses a novel solution for the recognition of the emotional state of a user through both facial expressions and body posture from an RGB-D sensor. Six basic emotions are recognized through facial expressions in addition to the physiological state, which is recognized through the body posture. The facial expressions and body posture are acquired in real-time from a Kinect sensor. A scoring system is used to improve recognition by minimizing the confusion between the different emotions. The implemented solution achieves an accuracy rate of above 90%. The recognized emotion is then used to derive an Automatic AUI where the user can use speech commands to modify the User Interface (UI) automatically. A comprehensive user study is performed to compare the usability of an Automatic, Manual, and a Hybrid AUI. The AUIs are evaluated in terms of their efficiency, effectiveness, productivity, and error safety. Additionally, a comprehensive analysis is performed to evaluate the results from the viewpoint of different genders and age groups. Results show that the hybrid adaptation improves usability in terms of productivity and efficiency. Finally, a combination of both automatic and hybrid AUIs result in significantly positive user experience compared to the manual adaptation.

The Nintendo Switch is a popular hybrid portable gaming console that employs a novel modular design, enabling it to be played in multiple controller configurations: Handheld mode, left and right JoyCon Sticks, and the JoyCon Grip. However, there are notes of user complaints about the complexity of performing basic tasks as a result of the unintuitiveness of its user interface. This can result to poor gaming experience and may potentially affect customer buying decisions. To assess the affordance of the user interface design of the Nintendo Switch, a total of 16 Filipino participants, classified as either novice or expert users, based on familiarity with the Switch or other gaming consoles and their functionalities, were made to perform a predetermined set of tasks on navigating through the Nintendo Switch’s basic menus, and playing a racing game across different controller orientations. Affordance, overall usability, and gaming experience satisfaction were measured through 5-point Likert scales (5 being the highest) for affordance surveys, system usability scale (SUS) surveys, and in-task and post-task surveys, respectively. Separate sets of surveys were done for general navigation and gaming tasks. Additionally, quantitative measures of effectiveness (task completion success rates) and efficiency (time to successfully complete tasks) were taken for general navigation tasks, and compared between novice and expert users. Task efficiency rates were also compared to baseline values obtained from experienced Nintendo Switch users. Results show a significant difference between expert and novice users’ effectiveness rates, with expert users all successfully performing 73% of navigation tasks, compared to novice rates of 18%. Efficiency rates for both expert and novice participant groups consistently fell significantly behind baseline task completion times. Affordance survey results showed consistent ratings across different controller configurations for expert users, and high preference for JoyCon Grip and Handheld mode configurations for novice participants. Of all SUS scores, only Game Navigation for expert users scored above the 68-point threshold. Recommendations to improve the Nintendo Switch user interface affordance and overall user satisfaction were made.

Adapting the user interface of a software system to the requirements of the context of use continues to be a major challenge, particularly when users become more demanding in terms of adaptation quality. A considerable number of methods have, over the past three decades, provided some form of modelling with which to support user interface adaptation. There is, however, a crucial issue as regards in analysing the concepts, the underlying knowledge, and the user experience afforded by these methods as regards comparing their benefits and shortcomings. These methods are so numerous that positioning a new method in the state of the art is challenging. This paper, therefore, defines a conceptual reference framework for intelligent user interface adaptation containing a set of conceptual adaptation properties that are useful for model-based user interface adaptation. The objective of this set of properties is to understand any method, to compare various methods and to generate new ideas for adaptation. We also analyse the opportunities that machine learning techniques could provide for data processing and analysis in this context, and identify some open challenges in order to guarantee an appropriate user experience for end-users. The relevant literature and our experience in research and industrial collaboration have been used as the basis on which to propose future directions in which these challenges can be addressed.

This chapter presents the issues of user modeling and its role in adaptive human-computer interface (HCI). Particularly, it focuses on knowledge acquisition and representation in user modeling. Several related problems in the traditional user modeling systems are also identified and discussed.

An Adaptive user interface for a pen-based educational application and the related technical difficulties are discussed The Supervised Competitive Learning (SCL) model is proposed as a mechanism for a pen-based adaptive user interface. It offers a solution for one of the fundamental problems of using neural for adaptive user interface; the Grossberg's stability-plasticity dilemma.KeywordsInput PatternHandwriting RecognitionAdaptive Resonance TheoryAdaptive User InterfacePrototype PatternThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Diversity inside a group of users having their individual abilities, interests, and needs challenge the developers of eHealth projects with heterogeneous needs in information delivery and/or other eHealth services. This paper considers an adaptive user interface approach as an opportunity in addressing this challenge. We briefly overview the recent achievements in the area of user interface adaptation and discuss application of these achievements in the eHealth context. We introduce the basic elements of our framework for adaptive user interface (AUI) for eHealth systems. Then, we use this framework in our review of work in the area of AUI for eHealth applications. As a result, we conclude with a brief discussion on the current focus on AUI research in eHealth, and interesting directions for further research.

AI (Artificial Intelligence) and GUI (Graphical User Interface) are the two most cutting-edge technologies. These technologies individually can make a comprehensive change in industries and alter economic and social interactions and connections. In general, Now-a-days AI is integrated with human technology and used in some wide range of sectors like healthcare, finance to education, banking, manufacturing, transportation, etc. GUI is used in gaming, designing, AR (Augmented reality) and VR (Virtual reality), media, smart home devices, etc., Integration of Artificial Intelligence and Graphical User Interface is beneficial for both users and app developers. The convergence of AI and GUI gives dynamic applications like Data Visualization, Insights, Smart assistants, Adaptions, Navigations, and Predictive analytics. This integration will increase the ease of access for new information to users. Now we are facing some general problems based on these technologies like Adaptive User Interfaces, Automated user interface testing, AI-Enhanced data visualization, Intelligent GUI Design Assistance, Real-Time Emotion Recognition for GUI Adaptation, etc., User interfaces are often designed with a one-size-fits-all approach, which does not account for individual user preferences and abilities. Designing perceptive and attractive GUIs requires significant space and time. This chapter focus on how can we develop a UI (User Interface) with base of AI which can be capable of solving the above obstacles and gives the simple objectives and technical solutions. By this integration we can execute some future projects like AI-Enhanced Customer Support Portals, Adaptive Gaming Interfaces, Interactive Financial Management Systems, and more.

eHealth technologies have been increasingly used to foster proactive self-management skills for patients with chronic diseases. However, it is challenging to provide each user with their desired support due to the dynamic and diverse nature of the chronic disease and its impact on users. Many such eHealth applications support aspects of “adaptive user interfaces”—interfaces that change or can be changed to accommodate the user and usage context differences. To identify the state of the art in adaptive user interfaces in the field of chronic diseases, we systematically located and analysed 48 key studies in the literature with the aim of categorising the key approaches used to date and identifying limitations, gaps, and trends in research. Our data synthesis is based on the data sources used for interface adaptation, the data collection techniques used to extract the data, the adaptive mechanisms used to process the data, and the adaptive elements generated at the interface. The findings of this review will aid researchers and developers in understanding where adaptive user interface approaches can be applied and necessary considerations for employing adaptive user interfaces to different chronic disease-related eHealth applications.

Smartphones are constantly being used in different use scenarios and contexts. This Thesis puts forth the notion that Adaptive User Interfaces (AUIs) can be implemented in mobile devices to help mitigate the negative usability effects associated with certain contexts. Using an auto-scroll and an auto-scroll + zoom function, two different AUIs were implemented to see if it is possible to mitigate the negative effects associated with reading while walking. Participants using these two interfaces, along with a static interface, were tested on reading comprehension tests while either sitting or walking a course with pedestrian traffic. The results indicate that the walking context had an adverse effect on reading comprehension. Participants most preferred an AUI interface while walking, with the auto-scroll AUI resulting in faster walking, faster course completion, and faster reading. It is concluded that AUIs can be used to alleviate some negative effects associated with concurrent reading and walking.

Adaptive User Interfaces (AUIs) provide better usability, user satisfaction as well as personalized experience. AUIs bring broad benefits to end users, but their construction introduces numerous difficulties caused by extended development and maintenance efforts. One option to deal with the above problems is to generate various User Interface (UI) descriptions from a model or existing code. This paper considers the Aspect-Oriented Programming (AOP)-based approach for the AUI design. Integrations of the approach with contemporary AUI frameworks, such as XML User Interface Language (XUL), User interface protocol (UIP) and JavaServer Faces (JSF) are compared from the perspective of the development and maintenance on a case study involving context- aware (CA) application. The outcome of this paper presents advantages, disadvantages and limitations of the particular frameworks for construction of AUIs.