Filtered by themeAdaptive/intelligent UI48 papersclear ✕

Using user emotions to trigger UI adaptationUniv. Grenoble Alpes · 2025User experience is an important user interface quality factor that needs to be maintained during interaction. With this goal, we propose to adapt user interfaces to users’ emotions. This requires being able to detect at runtime negative emotions to trigger adaptations. However, emotions depend on users’ gender and age. This makes it difficult to identify negative emotions and their cause. This paper studies the categorization of emotions depending on users’ age and gender and two user experience dimensions (aesthetics and usability). Thanks to an experiment, it proposes some emotions thresholds that can be used for detecting usability and aesthetics problems from users’ age and gender. These results are used in a user interface adaptation system to infer negative emotions and user interface problems.

UX of AI & PersonalizationResearch Papers

Recommendations user interface in web-based e-commerce systemsOleksandr Yeroshkin, Janusz Sobecki · 2024In the world of dynamic e-commerce, competition is fierce, and the ability to attract and retain customers is becoming crucial. In this reality, adaptive user interfaces (UI) play an important role, allowing e-commerce platforms to personalize the shopping experience to better understand and meet customer needs. When combined with advanced artificial intelligence (AI) technologies, adaptive UI can bring revolutionary changes to the way customers interact with e-commerce platforms. In this article, we will discuss how AI can be used to design and improve adaptive user interfaces in e-commerce systems, and what benefits can result from this combination. © 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ([https://creativecommons.org/licenses/by-nc-nd/4.0](https://creativecommons.org/licenses/by-nc-nd/4.0)) Peer-review under responsibility of the scientific committee of the 28th International Conference on Knowledge Based and Intelligent information and Engineering Systems Oleksandr Yeroshkin et al. / Procedia Computer Science 246 (2024) 2874–2881 2875 tion of an experimental approach in a controlled environment. During the implementation and verification phases, we will be able to generate alternative solutions and evaluate them according to the UX and/or conversion requirements. The paper is structured as follows. In the second section, the state of the art is presented. Then, in the following section, an example of a commercial system whose user interface will be adapted is given. In the fourth section, the proposed solution of adapted UI based on AI is described. In the final section, we summarize the paper and give some information on the proposed future work. 2. State of the art In the beginning, UI recommendation services did not use artificial intelligence methods [12]. According to Baraglia [4] and Kopel [10], mainly traditional approaches were used at that time. However, currently we are dealing with solutions that intensively use various AI algorithms. Automating user interface optimization can be achieved using a variety of techniques based on both traditional ap- proaches and AI and machine learning (ML) algorithms. When it comes to optimizing user experience in e-commerce, there are three approaches: adaptable, semi-adaptive, and fully adaptive [1]. The first involves manual adjustment by a user or expert according to their usage criteria [18]. The second involves manual adjustment supported by system recommendations which is a kind of evol

UX of AI & PersonalizationResearch Papers

SituationAdapt: Contextual UI Optimization in Mixed Reality with Situation Awareness via LLM ReasoningZhipeng Li, Christoph Gebhardt et al. · 2024Mixed Reality is increasingly used in mobile settings beyond con­ trolled home and ofce spaces. This mobility introduces the need for user interface layouts that adapt to varying contexts. However, existing adaptive systems are designed only for static environments. In this paper, we introduce SituationAdapt, a system that adjusts Mixed Reality UIs to real-world surroundings by considering envi­ ronmental and social cues in shared settings. Our system consists of perception, reasoning, and optimization modules for UI adaptation. Our perception module identifes objects and individuals around the user, while our reasoning module leverages a Vision-and-Language Model to assess the placement of interactive UI elements. This en­ sures that adapted layouts do not obstruct relevant environmental cues or interfere with social norms. Our optimization module then generates Mixed Reality interfaces that account for these consid­ erations as well as temporal constraints. For evaluation, we frst validate our reasoning module’s capability of assessing UI contexts in comparison to human expert users. In an online user study, we then establish SituationAdapt’s capability of producing context- aware layouts for Mixed Reality, where it outperformed previous adaptive layout methods. We conclude with a series of applications and scenarios to demonstrate SituationAdapt’s versatility. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for proft or commercial advantage and that copies bear this notice and the full citation on the frst page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specifc permission and/or a fee. Request permissions from permissions@acm.org. UIST ’24, October 13–16, 2024, Pittsburgh, PA, USA © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM. ACM ISBN 979-8-4007-0628-8/24/10 https://doi.org/10.1145/3654777.3676470 CCS CONCEPTS • Human-centered computing → Mixed / augmented reality; Virtual reality; Interactive systems and tools.

UX of AI & PersonalizationResearch Papers

Real-Time Personalized User Interface Adaptation Using Reinforcement LearningAbdulrahman Khamaj, Abdulelah M. Ali · 2024Developing a dynamic, personalized user interface that changes in real-time in response to user behavior is the goal. This paper supplies a modern method to beautify consumers enjoy using Reinforcement Learning (RL) and a Deep Q Network (DQN). Through support examination, the task objectives are to upgrade buyer connections and increment commitment, delight, and undertaking of consummation rates. Users who utilize traditional user interfaces get a common experience because they’re impersonal and unflexible. The potential for higher engagement and happiness levels is limited in the absence of real-time changes based on individual preferences and behaviors. To overcome this problem, the study suggests a cunning technique for a getting-to-comprehend layout that may constantly analyze and modify patron communications. This evaluation is new as it provides a blended RL and DQN framework that modifies person interfaces grade by grade. Dissimilar to conventional methodologies, the proposed form adjusts the utilization of well-known, over-the-top prize moves with the development of the most recent ones through an investigation double-dealing system. EventType, contentId, personId, sensorId, and timestamp are instances of timestamped insights handles that give a thorough skill of client conduct and license planned and nuanced changes.

UX of AI & PersonalizationResearch Papers

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

MineXR: Mining Personalized Extended Reality InterfacesHyunsung Cho · 2024Extended Reality (XR) interfaces offer engaging user experiences, but their effective design requires a nuanced understanding of user behavior and preferences. This knowledge is challenging to obtain without the widespread adoption of XR devices. We intro­ duce MineXR, a design mining workflow and data analysis platform for collecting and analyzing personalized XR user interaction and This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike International 4.0 License. CHI ’24, May 11–16, 2024, Honolulu, HI, USA © 2024 Copyright held by the owner/author(s). ACM ISBN 979-8-4007-0330-0/24/05 https://doi.org/10.1145/3613904.3642394 experience data. MineXR enables elicitation of personalized in­ terfaces from participants of a data collection: for any particular context, participants create interface elements using application screenshots from their own smartphone, place them in the envi­ ronment, and simultaneously preview the resulting XR layout on a headset. Using MineXR, we contribute a dataset of personalized XR interfaces collected from 31 participants, consisting of 695 XR widgets created from 178 unique applications. We provide insights for XR widget functionalities, categories, clusters, UI element types, and placement. Our open-source tools and data support researchers and designers in developing future XR interfaces. CCS CONCEPTS • Human-centered computing → Mixed / augmented reality; Systems and tools for interaction design. CHI ’24, May 11–16, 2024, Honolulu, HI, USA Cho et al.

UX of AI & PersonalizationResearch Papers

Next-Gen Human-Computer Interaction: A HybridRavindra Changal · 2024In the rapidly evolving landscape of human- computer interaction (HCI), the demand for personalized and adaptive user experiences has grown exponentially. To meet this demand, Research propose a groundbreaking approach leveraging the fusion of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) architectures. This hybrid LSTM-CNN model is designed to enhance adaptability, responsiveness, and user engagement across various interactive platforms.Traditional HCI models often struggle to effectively capture the dynamic nature of user behavior and preferences. By integrating LSTM and CNN, This model achieves a synergistic blend of temporal and spatial feature extraction capabilities. The LSTM component excels in capturing sequential dependencies and long-term patterns, enabling the system to learn from past interactions and anticipate future user actions. Meanwhile, the CNN component efficiently processes spatial information, extracting meaningful features from multimedia inputs such as images, videos, and text.One of the key strengths of proposed model is its adaptability to diverse user contexts and preferences. Through continuous learning and adaptation, it dynamically adjusts its interface, content, and interaction patterns to match the evolving needs and preferences of individual users. Moreover, the hybrid architecture enables real-time processing of multimodal inputs, facilitating seamless interaction across a wide range of devices and platforms.In experimental evaluations, Hybrid LSTM-CNN model demonstrated superior performance compared to baseline methods in terms of user satisfaction, engagement, and task completion rates. Furthermore, it exhibited robustness and scalability, making it suitable for deployment in real-world applications across domains such as e-commerce, entertainment, education, and healthcare.In summary, The proposed approach represents a significant advancement in HCI research, paving the way for next-generation interactive systems that deliver highly adaptive and personalized user experiences.

UX of AI & PersonalizationResearch Papers

One Size Does Not Fit All: Multivariant User Interface Personalization in E-CommerceAdam Wasilewski · 2024One of the most visible manifestations of the changes brought about by the digitization of everyday life is undoubtedly the spread of electronic commerce. It is difficult to think of the digital economy without considering transactions through electronic channels. In turn, the user interface (UI) is the key to e- commerce, as it is usually the first and primary point of contact between business and consumer. A key trend in e-commerce is the personalization of communications, which can improve the user experience, increase customer satisfaction and deliver tangible business benefits. Today, it is technically possible to base this personalization on an analysis of user behavior using artificial intelligence and machine learning techniques. A common form of personalization in e-commerce is the use of product recommendation systems, but the user interface can be tailored much more extensively. The approach described and discussed in this paper is a multivariant user interface that allows the layout to be tailored to the characteristics, attributes, and behaviors of customer groups generated using machine learning techniques. The results of the research carried out make it possible to verify the practicality of the proposed solution and provide an opportunity to identify development directions that take into account the potential of artificial intelligence. The application of the concept described in the paper is broad, covering all aspects of e-commerce design that require compromises when serving a single UI variant, but allow flexibility and customization for different users when serving a multivariant UI. INDEX TERMS Artificial intelligence, e-commerce, machine learning, personalization, user interface.

UX of AI & PersonalizationResearch Papers

Clustering Methods for Adaptive e-Commerce User InterfacesAdam Wasilewski · 2023Typical online shops have one interface provided to all users, regardless of their use of the shop. Meanwhile, user behavior varies and therefore different interfaces could be provided to different user groups. Various methods can be used to cluster users, including those using artificial intelligence (AI) methods. AI-based personalization allows e- commerce businesses to provide tailored recommendations to each indi- vidual customer based on preferences, purchase history, and behavior on the website. This article presents a study of the impact of an AI- based clustering method on the effectiveness of a dedicated user inter- face implemented and delivered to the customers of an e-shop. The first study included five methods, and two of them - agglomerative clustering and K-means clustering - were selected for detailed analysis. For both of these methods, an in-depth research was carried out and the impact of the clustering method on the quality of user clusters, as measured by the effectiveness of the dedicated interface in relation to the effectiveness of the default interface, was verified.

UX of AI & PersonalizationResearch Papers

Emoticontrol : Emotions-based Control of User-Interfaces AdaptationsKARTHIK VAIDHYANATHAN, IIIT Hyderaba · 2023Emotions are integral to human nature, and their existence, duration, and evolution could lead to specific behaviors. If emotions and behaviors are ignored in the design of socio-technical systems, they will fail or cause discomfort. User interfaces (UIs) are elements of interactive systems able to trigger or moderate emotions. UIs are increasingly designed adaptive to users' various characteristics, intending to improve their satisfaction, performance, and decisions. However, previous adaptation supervising approaches are not effectively adopted in real life since they neglect the dynamic behaviors of humans or systems. This paper proposes Emoticontrol, a quality-driven approach to adapting UIs to users' emotions using Model-Free Reinforcement Learning (MFRL). The approach aims to maximize applying the essential adaptations and minimize the unnecessary ones towards users' enhanced quality of experience (QoE). The approach also considers improving the software quality of service (QoS) by designing software architecture alternatives. We chose emergency evacuation training as a suitable evaluation domain since people experience intense emotions in potential danger. We performed experiments with a mobile application we developed that acts as a recommender system in evacuation training. By taking contextual input of the users' basic emotions from face recognition, the application intelligently adapts its UI to quickly lead people to safe areas while keeping them emotionally controlled. We consider software performance a crucial QoS; thus, we adopt and test architectures that facilitate an acceptable level of performance. The evaluation process confirms the efficiency and effectiveness of the MFRL in iterations, as well as compared to other UI adaptation techniques.

UX of AI & PersonalizationResearch Papers

Adaptive user interface for workflow-ERP systemMarcin Smereka, Grzegorz Kołaczek et al. · 2023In this paper, the problem of user interface recommendations for workflow management systems is investigated. The user interface is automatically adapted using a software tool based on content-based filtering. This tool collects information about the way processes are carried out in an organization, analyzes and processes the data, and recommends the next action to the user in order to increase efficiency, facilitate training, and improve decision-making. The proposed tool was verified in the real environment within three organizations. For each organization, after at least several weeks of learning, the tool was able to offer suggestions that were selected by real users. 2382 Marcin Smereka et al. / Procedia Computer Science 225 (2023) 2381–2391 The aim of the project carried out by Sente was to investigate the possibility of automatically analyzing user behavior in business software (especially ERP class) in order to automatically adapt the way processes are carried out to these behaviors using content-based filtering algorithms. A tool that would be able to collect information about the way processes are carried out in an organization, analyze and process the collected data and recommend the next action to the user could increase the efficiency of using the ERP system, and facilitate training and decision-making processes. 2. State of the art According to Kobsa [7], proper user model selection should be the starting point when designing the user inter- face. However, with the growing number of potential users, particularly in web-based systems, the differences among them are also increasing. Consequently, the user model is becoming more complex, resulting in a highly differentiated proposed user interface, such as in interaction styles. [14]. The user differences may reflex their demographical, psy- chological, sociological, and anthropological user characteristics, which have an influence on the users’ information needs, interaction habits, and potential limitations of the interaction systems usage. The consequence of these differ- ences is causing difficulties in modeling these users in the standard way [7], in a result more sophisticated solutions have been proposed, for example, one enhanced with the application of ontologies [12]. The ontologies may be used to enhance information systems design and development on at least several different levels, such as database integration, business logic, or Graphical User Interfaces (GUI) [8]. User interfaces have been also ado

UX of AI & PersonalizationResearch Papers

User Interface and Architecture Adaption Based onMahyar T. Moghaddam∗, Mina Alipour et al. · 2023This paper shows how emotions and behavior considerations in socio-technical systems lead to high-quality self-adaptations, both at application and architecture levels. In our approach, an interactive control system assesses the reconfigurations that enhance the quality of service (QoS) while considering humans’ quality of experience (QoE). We use a Model-Free Reinforcement Learning (MFRL) approach to self- adapt user interfaces (UIs) to users’ emotions. The approach aims to maximize applying the essential adaptations and minimize the unnecessary ones towards users’ QoE, i.e., task comple- tion and satisfaction. If the control system detects a drop in QoS in emotion-based adaptations or other functions, another level of adaptation reconfigures the architecture towards better quality. We chose emergency evacuation training as a suitable evaluation domain since people experience intense emotions in such potential danger. We performed experiments with a mobile application we developed that acts as a recommender system in emergency training. By taking contextual input of the users’ basic emotions from face recognition and their mobility behavior, the application intelligently adapts its UI to quickly lead people to safe areas while keeping them emotionally stable. In addition to UI adaptation, the system is capable of architecture-level adaptations to decrease response time if required. The evaluation process confirms the efficiency of the MFRL in iterations, as well as compared to other possible UI adaptation techniques. The emerging results also show that architecture-level adaptations positively impact the system performance and users’ emotions and performance. Index Terms—Software Architecture, Emotions, Behaviors, Reinforcement Learning, User Interface, Emergency.

UX of AI & PersonalizationResearch Papers

InteractionAdapt: Interaction-driven Workspace Adaptation for Situated Virtual Reality EnvironmentsYi Fei Cheng · 2023Virtual Reality (VR) has the potential to transform how we work: it enables fexible and personalized workspaces beyond what is possi­ ble in the physical world. However, while most VR applications are designed to operate in a single empty physical space, work environ­ ments are often populated with real-world objects and increasingly diverse due to the growing amount of work in mobile scenarios. In this paper, we present InteractionAdapt, an optimization-based method for adapting VR workspaces for situated use in varying everyday physical environments, allowing VR users to transition between real-world settings while retaining most of their personal­ ized VR environment for efcient interaction to ensure temporal consistency and visibility. InteractionAdapt leverages physical afor­ dances in the real world to optimize UI elements for the respectively most suitable input technique, including on-surface touch, mid-air touch and pinch, and cursor control. Our optimization term thereby models the trade-of across these interaction techniques based on Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for proft or commercial advantage and that copies bear this notice and the full citation on the frst page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specifc permission and/or a fee. Request permissions from permissions@acm.org. UIST ’23, October 29–November 01, 2023, San Francisco, CA, USA © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM. ACM ISBN 979-8-4007-0132-0/23/10...$15.00 https://doi.org/10.1145/3586183.3606717 experimental fndings of 3D interaction in situated physical envi­ ronments. Our two evaluations of InteractionAdapt in a selection task and a travel planning task established its capability of support­ ing efcient interaction, during which it produced adapted layouts that participants preferred to several baselines. We further show­ case the versatility of our approach through applications that cover a wide range of use cases. CCS CONCEPTS • Human-centered computing → Mixed / augmented reality; Virtual reality; User interface management systems.

UX of AI & PersonalizationResearch Papers

Toward Changing Users behavior with Emotion-based Adaptive SystemsMina Alipour · 2023Interactive computer systems’ designers emphasize the importance of considering humans, their emotions, and behaviors as first-class entities. Emotions are integral parts of human nature, and ignor- ing that can lead the interactive systems to failure, low quality, or discomfort. User interfaces (UIs) are increasingly becoming adap- tive to users’ various characteristics, intending to improve users’ satisfaction, performance, and decisions. However, the previous approaches proposed for supervising such adaptations are not effec- tively adopted in real-life problems. This paper proposes the novel approach to adapting UIs to users’ emotions using Model-Free Re- inforcement Learning (MFRL). The approach aims to maximize applying the essential adaptations and minimize the unnecessary ones towards users’ task completion and satisfaction. We chose emergency evacuation training as a suitable evaluation domain since people experience intense emotions in potential danger. We performed experiments with a mobile application we developed that acts as a recommender system in emergency training. By taking contextual input of the users’ basic emotions from face recognition, the application intelligently adapts its UI to quickly lead people to safe areas while arousing target emotions. The research includes lit- erature analysis, surveys, and further adopting an iterative process in implementation and experimentation. The evaluation process confirms the efficiency and effectiveness of the MFRL in iterations, as well as compared to other possible UI adaptation techniques, i.e., rule-based and sequential adaptation.

UX of AI & PersonalizationResearch Papers

Algorithms for the Development of Adaptive Web Interfaces: A Systematic Literature ReviewMichael Rodrigues da Silva · 2022The User Interface (UI) is a fundamental part of web interfaces, being considered a fundamental aspect of user satisfaction when using a web application. The UI that offers a good User Experience (UX) is the one that makes the interaction as simple and efficient as possible for the user to achieve the previously desired goals. In this way, web interfaces that have adaptive interfaces tend to offer a better UX, as they seek to adapt to the needs and desires of users. Therefore, this paper carries out a Systematic Literature Review (SLR) to identify the most used algorithms for the development of Adaptive Web Interfaces (AWI). The results indicate that the use of Machine Learning (ML) techniques, both to improve the performance of some task and to induce results, are the most popular ways to develop AWI. During the execution of SLR, 294 articles were analyzed using automated and manual searches, and inclusion, exclusion and quality criteria were applied to them. The research findings allow us to state that the topic is quite relevant to the academic community, with an average publication of 20 papers each year.1

UX of AI & PersonalizationResearch Papers

ScalAR: Authoring Semantically Adaptive Augmented Reality Experiences in Virtual RealityXun Qian · 2022Augmented Reality (AR) experiences tightly associate virtual con- tents with environmental entities. However, the dissimilarity of different environments limits the adaptive AR content behaviors under large-scale deployment. We propose ScalAR, an integrated workflow enabling designers to author semantically adaptive AR ex- periences in Virtual Reality (VR). First, potential AR consumers col- lect local scenes with a semantic understanding technique. ScalAR then synthesizes numerous similar scenes. In VR, a designer au- thors the AR contents’ semantic associations and validates the design while being immersed in the provided scenes. We adopt a decision-tree-based algorithm to fit the designer’s demonstrations as a semantic adaptation model to deploy the authored AR expe- rience in a physical scene. We further showcase two application scenarios authored by ScalAR and conduct a two-session user study where the quantitative results prove the accuracy of the AR content rendering and the qualitative results show the usability of ScalAR. This work is licensed under a Creative Commons Attribution International 4.0 License. CHI ’22, April 29-May 5, 2022, New Orleans, LA, USA © 2022 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-9157-3/22/04. https://doi.org/10.1145/3491102.3517665 CCS CONCEPTS • Human-centered computing →Mixed / augmented reality; Virtual reality; Interactive systems and tools.

UX of AI & PersonalizationResearch Papers

Real-Time Adaptation of Context-Aware Intelligent User Interfaces, for Enhanced Situational AwarenessZinova Stefanidi, George Margetis · 2022In this work, a novel computational approach for the dynamic adaptation of User Inter- faces (UIs) is proposed, which aims at enhancing the Situational Awareness (SA) of users by leveraging the current context and providing the most useful information, in an optimal and efficient manner. By combining Ontology modeling and reasoning with Combinatorial Optimization, the system decides what information to present, when to present it, where to visualize it in the display - and how, taking into consideration contextual factors as well as placement constraints. The main objective of the proposed approach is to optimize the SA associated with the displayed UI at run-time, while avoiding information overload and induced stress. In the context of this work, we have deployed our computational approach to the use case of an Augmented Reality (AR) system for Law Enforcement Agents (LEAs). To explore the benefits and limitations of the developed system, two evaluations have been conducted. The first one was an expert-based evaluation with LEAs and User Experience (UX) experts, assessing the appropriateness of the system’s decisions. The second one was a user-based evaluation involving LEAs from different agencies, estimating the SA, the mental workload and the overall UX associated with the system, through an AR simulation. The results indicate that the system enhances SA, and while not imposing workload, it provides an overall positive UX. INDEX TERMS Adaptive user interfaces, augmented reality, context-awareness, intelligent user interfaces, ontology modeling, ontology reasoning, situational awareness, user interface optimization.

UX of AI & PersonalizationResearch Papers

AUIT – the Adaptive User Interfaces Toolkit for Designing XR ApplicationsAnna Maria Feit · 2022Adaptive user interfaces can improve experiences in Extended Re­ ality (XR) applications by adapting interface elements according to the user’s context. Although extensive work explores diferent adaptation policies, XR creators often struggle with their imple­ mentation, which involves laborious manual scripting. The few available tools are underdeveloped for realistic XR settings where it is often necessary to consider conficting aspects that afect an adap­ tation. We fll this gap by presenting AUIT, a toolkit that facilitates the design of optimization-based adaptation policies. AUIT allows creators to fexibly combine policies that address common objec­ tives in XR applications, such as element reachability, visibility, and consistency. Instead of using rules or scripts, specifying adaptation policies via adaptation objectives simplifes the design process and This work is licensed under a Creative Commons Attribution International 4.0 License. UIST ’22, October 29-November 2, 2022, Bend, OR, USA © 2022 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-9320-1/22/10. https://doi.org/10.1145/3526113.3545651 enables creative exploration of adaptations. After creators decide which adaptation objectives to use, a multi-objective solver fnds appropriate adaptations in real-time. A study showed that AUIT allowed creators of XR applications to quickly and easily create high-quality adaptations. CCS CONCEPTS • Human-centered computing → Systems and tools for in­ teraction design; Gestural input; Mixed / augmented reality; Virtual reality; User interface toolkits.

UX of AI & PersonalizationResearch Papers

Model-based intelligent user interface adaptation: challenges and future directionsSilvia Abrahao · 2021Adapting 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.

UX of AI & PersonalizationResearch Papers

Adapting User Interfaces with Model-based Reinforcement LearningKashyap Todi · 2021Adapting an interface requires taking into account both the positive and negative efects that changes may have on the user. A carelessly picked adaptation may impose high costs to the user – for example, due to surprise or relearning efort – or “trap” the process to a suboptimal design immaturely. However, efects on users are hard to predict as they depend on factors that are latent and evolve over the course of interaction. We propose a novel approach for adaptive user interfaces that yields a conservative adaptation policy: It fnds benefcial changes when there are such and avoids changes when there are none. Our model-based reinforcement learning Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for proft or commercial advantage and that copies bear this notice and the full citation on the frst page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specifc permission and/or a fee. Request permissions from permissions@acm.org. CHI ’21, May 8–13, 2021, Yokohama, Japan © 2021 Association for Computing Machinery. ACM ISBN 978-1-4503-8096-6/21/05...$15.00 https://doi.org/10.1145/3411764.3445497 method plans sequences of adaptations and consults predictive HCI models to estimate their efects. We present empirical and simulation results from the case of adaptive menus, showing that the method outperforms both a non-adaptive and a frequency-based policy. CCS CONCEPTS • Human-centered computing → Interactive systems and tools.

UX of AI & PersonalizationResearch Papers

Multimodal Differentiation of Obstacles in Repeated Adaptive Human-Computer InteractionsFelix Putze · 2021Human Computer Interaction can be impeded by various interac- tion obstacles, impacting a user’s perception or cognition. In this work, we detect and discriminate such interaction obstacles from different data modalities to compensate for them through User Interface (UI) adaptation. For example, we detect memory-based obstacles from brain activity and compensate through repetition of information in the UI; we detect visual obstacles from user behavior and compensate by complementing visual with auditory informa- tion in the UI. Online cognitive adaptive systems should be able to decide the most suitable UI adaptation given inputs from several obstacles detectors. In this paper, we employ a Bayesian fusion approach upon different underlying obstacles detectors over mul- tiple consecutive interaction sessions. Experimental results show that the model promisingly outperforms the baseline in the first interaction with an average accuracy of 72.5% and further improves drastically in subsequent interactions with additional information, with an average accuracy of 98%. CCS CONCEPTS • Human-centered computing →User models.

UX of AI & PersonalizationResearch Papers

User Context Ontology for Adaptive Mobile-Phone InterfacesReceived June · 2021The Adaptive User Interface (AUI) adapts to the changes in the context of use and provides improved interaction abilities for different users. The adaptivity in the user interfaces requires in depth knowledge of context. There is a need to enrich user profiles to achieve the personalized services with the ability to adapt the user’s context. The context can be reflected in a particular kind of knowledge and hence modeled as ontology. Ontology based context models are effective means to handle complex situations that support the sharing or integration of context information. This paper presents ontology based context model using OWL for adaptive mobile devices. It models the context over its four major elements including device, user, environment (location and time) and activity. The proposed ontology was derived in different classes, relationships, associations, dependencies and constraints to model dynamic context. Ontologies present a standardized, consistent and shareable context model. The context model and consequent context snapshots can be acknowledged by AUI to present a suitable user interface. The ontology was developed using Protégé on the basis of each context type having different values. Semantic querying (SPARQL) was used for knowledge acquisition. Moreover, the Pellet and HermiT Reasoner were used to verify the rules, relations and constraints to avoid the inconsistency between classes. Comparative to other context models for adaptive interfaces, ontological model provides more of scalability and growth with learning new context in to the shared context knowledge. INDEX TERMS Adaptive user interface, context aware interface, ontology driven interfaces, knowledge representation, knowledge engineering.

UX of AI & PersonalizationResearch Papers

SemanticAdapt: Optimization-based Adaptation of Mixed Reality Layouts Leveraging Virtual-Physical Semantic ConnectionsYifei Cheng∗ · 2021We present an optimization-based approach that automatically adapts Mixed Reality (MR) interfaces to different physical envi- ronments. Current MR layouts, including the position and scale of virtual interface elements, need to be manually adapted by users ∗This work was done while Yifei Cheng was an intern at Tsinghua University. †The corresponding author. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. UIST’21, October 2021, Virtual © 2021 Association for Computing Machinery. ACM ISBN 978-1-4503-8635-7/21/10...$15.00 https://doi.org/10.1145/3472749.3474750 whenever they move between environments, and whenever they switch tasks. This process is tedious and time consuming, and ar- guably needs to be automated for MR systems to be beneficial for end users. We contribute an approach that formulates this challenge as a combinatorial optimization problem and automatically decides the placement of virtual interface elements in new environments. To achieve this, we exploit the semantic association between the virtual interface elements and physical objects in an environment. Our optimization furthermore considers the utility of elements for users’ current task, layout factors, and spatio-temporal consistency to previous layouts. All those factors are combined in a single linear program, which is used to adapt the layout of MR interfaces in real time. We demonstrate a set of application scenarios, showcasing the versatility and applicability of our approach. Finally, we show that compared to a naive adaptive baseline approach that does not 282 UIST’21, October 2021, Virtual Cheng and Yan, et al. take semantic associations into account, our approach decreased the number of manual interface adaptations by 33%. CCS CONCEPTS • Human-centered computing →Mixed / augmented reality; Virtual reality; User interface management systems.

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Effect of Adaptive Guidance and Visualization Literacy on Gaze Attentive Behaviors and Sequential Patterns on Magazine-Style Narrative VisualizationsOSWALD BARRAL, SÉBASTIEN LALLÉ et al. · 2021We study the effectiveness of adaptive interventions at helping users process textual documents with embedded visualizations, a form of multimodal documents known as Magazine-Style Narrative Visualizations (MSNVs). The interventions are meant to dynamically highlight in the visualization the datapoints that are described in the textual sentence currently being read by the user, as captured by eye-tracking. These interventions were previously evaluated in two user studies that involved 98 participants reading excerpts of real-world MSNVs during a 1-hour session. Participants' outcomes included their subjective feedback about the guidance, and well as their reading time and score on a set of comprehension questions. Results showed that the interventions can increase comprehension of the MSNV excerpts for users with lower levels of a cognitive skill known as visualization literacy. In this article, we aim to further investigate this result by leveraging eye-tracking to analyze in depth how the participants processed the interventions depending on their levels of visualization literacy. We first analyzed summative gaze metrics that capture how users process and integrate the key components of the narrative visualizations. Second, we mined the salient patterns in the users' scanpaths to contextualize how users sequentially process these components. Results indicate that the interventions succeed in guiding attention to salient components of the narrative visualizations, especially by generating more transitions between key components of the visualization (i.e., datapoints, labels, and legend), as well as between the two modalities (text and visualization). We also show that the interventions help users with lower levels of visualization literacy to better map datapoints to the legend, which likely contributed to their improved comprehension of the documents. These findings shed light on how adaptive interventions help users with different levels of visualization literacy, informing the design of personalized narrative visualizations.

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An Emotion-Oriented Problem Space for UIMina Alipour · 2021Emotions significantly affect the human interaction process with computers. They constitute a dynamic user’s charac- teristic that can be crucial for adapting user interfaces (UI) to the user’s context. However, their impact on UI adaptation concepts and techniques is not well understood. This paper proposes a problem space that studies the design dimensions to be considered in UI adaptation to emotions. In particular, this emotion-oriented problem space for UI adaptation, highlights the temporal aspects of emotions. It is obtained from two literature reviews realized to identify the gaps in existing problem spaces and the potentials of including emotional aspects inside them. The proposed problem space can benefit all adaptive systems that aim to improve users’ and system’s performance based on users’ reactions to the system. Index Terms—User interface adaptation, Problem space, Emo- tion adaptation,Temporal adaptation.

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UX designers pushing AI in the enterprise: a case for adaptive UIsJohn Zimmerman · 2020- → Adaptive user interfaces (AUIs) provide an untapped opportunity for UX designers to innovate with AI. - → AUIs seem well suited for enterprise applications, where it is easier to estimate the value of saving a worker time by automating mundane and repetitive tasks. - → AUIs raise new ethical questions about user privacy and about the unintended impacts of making workers faster at their work.

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Combinatorial Optimization of Graphical User Interface DesignsAntti Oulasvirt · 2020| The graphical user interface (GUI) has become the prime means for interacting with computing systems. It lever- ages human perceptual and motor capabilities for elementary tasks such as command exploration and invocation, informa- tion search, and multitasking. For designing a GUI, numerous interconnected decisions must be made such that the out- come strikes a balance between human factors and technical objectives. Normally, design choices are specified manually and coded within the software by professional designers and developers. This article surveys combinatorial optimization as a flexible and powerful tool for computational generation and adaptation of GUIs. As recently as 15 years ago, applications were limited to keyboards and widget layouts. The obstacle has been the mathematical definition of design tasks, on the one hand, and the lack of objective functions that capture essential aspects of human behavior, on the other. This article presents definitions of layout design problems as integer programming tasks, a coherent formalism that permits identification of problem types, analysis of their complexity, and exploitation of known algorithmic solutions. It then surveys advances in Manuscript received April 8, 2019; revised October 9, 2019 and January 12, 2020; accepted January 17, 2020. This work was supported in part by the European Research Council (ERC) through the European Union’s Horizon 2020 Research and Innovation Program under Grant 637991 and in part by the Academy of Finland projects Bayesian Artefact Design (BAD) and Human Automata. (Corresponding author: Antti Oulasvirta.) Antti Oulasvirta is with the Department of Communications and Networking, School of Electrical Engineering, Aalto University, 02150 Espoo, Finland, and also with the Finnish Center for Artificial Intelligence (FCAI), 02015 Espoo, Finland (e-mail: antti.oulasvirta@aalto.fi). Niraj Ramesh Dayama and Morteza Shiripour are with the Department of Communications and Networking, School of Electrical Engineering, Aalto University, 02150 Espoo, Finland. Maximilian John and Andreas Karrenbauer are with the Max Planck Institute for Informatics, 66123 Saarbrücken, Germany. Digital Object Identifier 10.1109/JPROC.2020.2969687 formulating evaluative functions for common design-goal foci such as user performance and experience. The convergence of these two advances has expanded the range of solvable problems. Approaches to practical deployment are outlined with a wide spectrum of applica

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A Long-Term Evaluation of Adaptive Interface Design for Mobile Transit InformationAlexander Haig · 2020Personalization of user experience has a long history of success in the HCI community. More recently the community has focused on adaptive user interfaces, supported by machine learning, that reduce interaction efforts and improves user experience by collaps- ing transactions and pre-filtering results. However, generally, these more recent results have only been demonstrated in the laboratory environment. In this paper, we share the case of a deployed mobile transit app that adapts based on users’ previous usage. We examine the impact of adaptation, both good and bad, and user abandon- ment rates. We conducted an 18-month assessment where 2,616 participants (with and without vision impairments) were recruited and participated in an A/B study. Finally, we draw some insights on some unusual effects that appear over the long term. CCS CONCEPTS • Human-centered computing →Empirical studies in inter- action design.

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Self-adaptation of Workflow Business Software to the User's Requirements and BehaviorUser Interface, User Experience et al. · 2020The main goal of the presented paper is to propose a method for adaptation of the user interface of workflow software to increase its efficiency, reduce the number of errors, and improve its UX. The authors assumed that the system adaptation will be achieved by application to intelligent methods for modeling user as well as system. In order to do this, a special tool for data gathering has been designed and in the next steps of the research, this tool will be also implemented in a real environment. A unique value of the paper is that after many years of theoretical research, the first attempt to implement a practical solution for self-adapting and the personalized interface for workflow systems was done. Janusz Sobecki et al. / Procedia Computer Science 176 (2020) 3506–3513 3507 adaptation [3]. This AI approach for user interface adaptation may be also enhanced with the application of user interface ontologies [4]. 1.1. Related Works In user interface design we should start with proper user model [1] however, we should always remember that ever-increasing number of users, especially of web-based systems, also brings the increase of differences among their users and interaction styles [3]. The user differences may reflex their demographical, psychological as well as sociological user characteristics, which have an influence on the users' information needs and interaction habits. The consequence of these differences is causing difficulties in modeling these users in the standard way [1], so since many years more sophisticated solutions have been proposed, for example, one enhanced with the application of ontologies [5], which was further applied in SOA systems development [4] and [6]. The information systems design and development have been enhanced by ontologies on at least several different levels, such as database integration, business logic, or Graphical User Interfaces (GUI) [6]. The before mentioned work presents an approach for mapping formal ontologies to GUI. This supports device-independent GUI construction and semi-automatic GUI modeling. This issue was also been raised in other work [7], as well as [8]. User interfaces have been also adopted by means of application different recommender methods such as Demographic Filtering, Content-Based Filtering, Collaborative Filtering or Hybrid Approach [9, 15], wherein user grouping or classification different machine learning algorithms may be applied, or the recommender method hybridization may be based

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User interface design patterns and ontology models for adaptive mobile applications2020Mobile applications are an essential element in pervasive and ubiquitous computing, and they face many challenges during their generation process from the analysis of user needs to the design of specific mobile interfaces and their development in several technological platforms. Moreover, the rise of Ambient Intelligent and context-aware environments also introduces multiple interaction aspects to be considered when using mobile devices in this kind of scenarios. The present work seeks to examine the role of design patterns and ontology models in order to help with the generation of mobile applications, which can be adapted at runtime to the various user needs, different context scenarios, interactive design modes, or technology requirements. In this way, an ontology-based framework is introduced to represent, design, and support the adaptation of user interfaces in mobile appli- cations by using design patterns according to these user needs or preferences and the context around them. This framework provides developers with a client-server architecture that enables the access to an expert knowledge base of user, context, and pattern information together with a set of inference rules, which allow the dynamic selection of interface design patterns and the runtime adaptation of the user interface features. These ontology models and inference rules are key components of the proposed framework, and their implementation has helped to produce an example of mobile application supporting user interface adap- tation processes for disabled people, which can be required in Ambient Intelligent environments. Three examples of user scenarios have been considered to assess the framework potential, and usability dimensions have been tested by a limited set of users through the produced mobile application, making the usefulness of generated adaptive user interfaces apparent.

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Exploring a Design Space of Graphical Adaptive Menus: Normal vs. Small ScreensJEAN VANDERDONCKT · 2019Graphical Adaptive Menus are Graphical User Interface menus whose predicted items of immediate use can be automatically rendered in a prediction window. Rendering this prediction window is a key question for adaptivity to enable the end-user to efficiently differentiate predicted items from normal ones and to consequently select appropriate items. Adaptivity for graphical menus has been investigated more for normal screens, such as desktops, than for small screens, such as smartphones, where real estate imposes severe rendering constraints. To address this question, this article defines and explores a design space where graphical adaptive menus are structured based on Bertin's eight visual variables (i.e., position, size, shape, value, color, orientation, texture, and motion) and their combination by comparing their rendering for small screens with respect to normal screens. Based on this design space, previously introduced graphical adaptive menus are revisited in terms of four stability properties (i.e., spatial, physical, format, and temporal), and new menu designs are introduced and discussed for both normal and small screens. The resulting set of graphical adaptive menu has been subject to a preference analysis from which a particular design emerged: the cloud menu, where predicted items are arranged in an adaptive tag cloud. We investigate empirically the effect of the cloud menu on the item selection time and the error rate with respect to a static menu and an adaptive linear menu. This article then suggests a set of usability guidelines for designers and practitioners to design graphical adaptive menus in general and cloud menus in particular.

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Context- and Data-driven Satisfaction Analysis of User Interface Adaptations Based on Instant User FeedbackUsability testing · 2019Modern User Interfaces (UIs) are increasingly expected to be plastic, in the sense that they retain a constant level of usability, even when subjected to context (platform, user, and environment) changes at runtime. Adaptive UIs have been promoted as a solution for context variability due to their ability to automatically adapt to the context-of-use at runtime. However, evaluating end-user satisfaction of adaptive UIs is a challenging task, because the UI and the context-of-use are both constantly changing. Thus, an acceptance analysis of UI adaptation features should consider the context-of-use when adaptations are triggered. Classical usability evaluation methods like usability tests mostly focus on a posteriori analysis techniques and do not fully exploit the potential of collecting implicit and explicit user feedback at runtime. To address this challenge, we present an on-the-fly usability testing solution that combines continuous context monitoring together with collection of instant user feedback to assess end-user satisfaction of UI adaptation features. The solution was applied to a mobile Android mail application, which served as basis for a usability study with 23 participants. A data-driven end-user satisfaction analysis based on the collected context information and user feedback was conducted. The main results show that most of the triggered UI adaptation features were positively rated.

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Self-Adaptation in Mobile Apps: a SystematicGrua · 2019With their increase, smartphones have become more integral components of our lives but due to their mobile nature it is not possible to develop a mobile application the same way another software system would be built. In order to always provide the full service, a mobile application needs to be able to detect and deal with changes of context it may be presented with. A suitable method to achieve this goal is self-adaptation. However, as of today it is difficult to have a clear view of existing research on self-adaptation in the context of mobile applications. In this paper, we apply the systematic literature review methodology on selected peer-reviewed papers focusing on self- adaptability in the context of mobile applications. Out of 607 potentially relevant studies, we select 44 primary studies via carefully-defined exclusion and inclusion criteria. We use known modelling dimensions for self-adaptive software systems as our classification framework, which we apply to all selected primary studies. From the synthesized data we obtained, we produce an overview of the state of the art. The results of this study give a solid foundation to plan for future research and practice on engineering self-adaptive mobile applications.

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Integrated model-driven development of self-adaptive user interfacesEnes Yigitbas · 2019Modern user interfaces (UIs) are increasingly expected to be plastic, in the sense that they retain a constant level of usability, even when subjected to context changes at runtime. Self-adaptive user interfaces (SAUIs) have been promoted as a solution for context variability due to their ability to automatically adapt to the context-of-use at runtime. The development of SAUIs is a challenging and complex task as additional aspects like context management and UI adaptation have to be covered. In classical model-driven UI development approaches, these aspects are not fully integrated and hence introduce additional complexity as they represent crosscutting concerns. In this paper, we present an integrated model-driven development approach where a classical model-driven development of UIs is coupled with a model-driven development of context-of-use and UI adaptation rules. We base our approach on the core UI modeling language IFML and introduce new modeling languages for context- of-use (ContextML) and UI adaptation rules (AdaptML). The generated UI code, based on the IFML model, is coupled with the context and adaptation services, generated from the ContextML and AdaptML model, respectively. The integration of the generated artifacts, namely UI code, context, and adaptation services in an overall rule-based execution environment, enables runtime UI adaptation. The benefit of our approach is demonstrated by two case studies, showing the development of SAUIs for different application scenarios and a usability study which has been conducted to analyze end-user satisfaction of SAUIs.

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I see, you design: user interface intelligent design system with eye tracking and interactive genetic algorithmCheng · 2019User interfaces are important for streamlining the interactions between humans and computers. However, there are few effective approaches for collecting users’ preferences implicitly and objectively for the purpose of user interface (UI) design optimization. This paper presents an effective approach to interactive genetic algorithm (IGA) optimization-based UI design via eye tracking, including eye-movement data based users’ preference inferring, gene coding for real UI components and design features, and the visualization and interaction mechanisms. Then we design and build a prototype system that applies IGA to generate and optimize music player UI solutions automatically. An evaluation of our prototype system suggests that it can generate and identify personalized UI designs reliably with minimal user intervention and high efficiency and user satisfaction.

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Context-Aware Online Adaptation of Mixed Reality InterfacesDavid Lindlbauer, Anna Maria Feit et al. · 2019We present an optimization-based approach for Mixed Reality (MR) systems to automatically control when and where appli­ cations are shown, and how much information they display. Currently, content creators design applications, and users then manually adjust which applications are visible and how much information they show. This choice has to be adjusted every time users switch context, i.e., whenever they switch their task or environment. Since context switches happen many times a day, we believe that MR interfaces require automation to alleviate this problem. We propose a real-time approach to automate this process based on users’ current cognitive load and knowledge about their task and environment. Our system adapts which applications are displayed, how much informa­ tion they show, and where they are placed. We formulate this problem as a mix of rule-based decision making and combina­ torial optimization which can be solved effciently in real-time. We present a set of proof-of-concept applications showing that our approach is applicable in a wide range of scenarios. Finally, we show in a dual-task evaluation that our approach decreased secondary tasks interactions by 36%. Author Keywords Mixed Reality, Context-Awareness, UI Optimization CCS Concepts •Human-centered computing → Mixed / augmented real­ ity; Virtual reality; User interface management systems; Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for proft or commercial advantage and that copies bear this notice and the full citation on the frst page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specifc permission and/or a fee. Request permissions from permissions@acm.org. UIST’19, The 32nd Annual ACM Symposium on User Interface Software and Tech­ nology, October 20–23, 2019. New Orleans, LA, USA © 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM. ISBN 978-1-4503-6816-2/19/10...$15.00 DOI: 10.1145/3332165.3347945

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A Framework for the Development of a Dynamic AdaptiveVivien Johnston · 2019 The aim of this paper is to present PhD research that aims to enhance the User Experience by proposing a framework that combines the three core components of: dynamic interfaces; adaptive interfaces; and intelligent interfaces. Initial research into the field has identified a gap at the intersection of these types of interaction. A dynamic interaction understands the user, their device and their physical environment to provide a basic User Experience. An adaptive interaction understands the user’s capabilities further to implement an enhanced experience via usability and accessibility whilst recognising the flow of the user and their pipeline. The intelligent interaction builds further upon this through the incorporation of Machine Learning algorithms that assist in making the interface intelligent and provide a personalised experience for each user based upon their end goal. This in turn will reduce a user’s cognitive load and enhance their interactive experience with an interface. CCS CONCEPTS • Human-centered computing~Human computer interaction (HCI) • Human-centered computing~Usability testing • Computing methodologies~Machine learning Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. ECCE 2019, September 10-13, 2019, BELFAST, United Kingdom © 2019 Association for Computing Machinery. ACM ISBN 978-1-4503-7166-7/19/09...$15.00 https://doi.org/10.1145/3335082.3335125

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SAM: A Modular Framework for Self-Adapting Web MenusCamille Gobert · 2019This paper presents SAM, a modular and extensible JavaScript framework for self-adapting menus on webpages. SAM al­ lows control of two elementary aspects for adapting web menus: (1) the target policy, which assigns scores to menu items for adaptation, and (2) the adaptation style, which spec­ ifes how they are adapted on display. By decoupling them, SAM enables the exploration of diferent combinations inde­ pendently. Several policies from literature are readily imple­ mented, and paired with adaptation styles such as reordering and highlighting. The process—including user data logging— is local, ofering privacy benefts and eliminating the need for server-side modifcations. Researchers can use SAM to experiment adaptation policies and styles, and benchmark techniques in an ecological setting with real webpages. Prac­ titioners can make websites self-adapting, and end-users can dynamically personalise typically static web menus. CCS CONCEPTS • Human-centered computing → Human computer in­ teraction (HCI); User interface design;

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Walking with Adaptive Augmented Reality Workspaces: Design and Usage PatternsCCS CONCEPTS · 2019Mobile augmented reality may eventually replace our smartphones as the primary way of accessing information on the go. However, current interfaces provide little support to walking and to the va- riety of actions we perform in the real world. To achieve its full potential, augmented reality interfaces must support the fluid way we move and interact in the physical world. We explored how dif- ferent adaptation strategies can contribute towards this goal. We evaluated design alternatives through contextual studies and identi- fied the key interaction patterns that interfaces for walking should support. We also identified desirable properties of adaptation-based interface techniques, which can be used to guide the design of the next-generation walking-centered augmented reality workspaces. CCS CONCEPTS • Human-centered computing →Mixed / augmented reality; Graphical user interfaces; Empirical studies in HCI.

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Customizing User Experience with Adaptive Virtual RealityBachar Senno · 2018Virtual reality is becoming more prominent than ever. The improved performance of mobile devices, along with the growing interest in more immersive user experiences, have allowed virtual reality to gain solid standing in this regard: nowadays, a smartphone and a pair of VR goggles are all that is needed for a full VR experience. The uses of VR systems extend beyond home entertainment: in particular, we are tackling the possibility that VR can be used by therapists to aid patients suffering from neuro- developmental disorders (NDD) [1,2]. To that, an adaptive system that can change different aspects of the VR environment, using machine learning techniques, can go a long way in increasing the efficiency of NDD treatments.

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Rule based adaptive user interface for adaptive E-learning systemManohara Pai M. M. · 2018The term Adaptive E-learning System (AES) refers to the set of techniques and approaches that are combined together to offer online courses to the learners with the aim of providing customized resources and interfaces. Most of these systems focus on adaptive contents which are generated to the learners without considering the learning styles of the learners. Learning style of the learner defines the way of learning the contents. The system should not only meet the individual need of the contents but also the customized user interface on the portal. Hence, an AES should mainly focus on recommending learning contents with Adaptive User Interface (AUI) on the portal. The work in the paper proposes a generic approach to provide the learning contents with AUI components based on the learning styles of the learners. The learning style adopted for the work is the Felder-Silverman Learning Style Model (FSLSM). The proposed approach defines generic rules which are generated automatically for any online course with the adaptive contents. Also, the approach takes care of new learners by providing learning path as a user interface component on the portal. The experiment has been conducted on engineering students for a particular online course. The portal is validated using parameters of usability testing by generating test cases and statistical analysis has been carried out to identify the impact of AUI components on the learning process. The result shows the well adaptation of user interface components and contents based on learning styles.

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The Influence of Personality Traits and Cognitive Load on the Use of Adaptive User InterfacesHarvard SEAS · 2017One of the problems adaptive interfaces must solve is the is- sue of stability—users must be able to complete a familiar task reliably. Split Adaptive Interfaces, where a limited part of the screen contains copies of the interface elements pre- dicted to be of immediate use, are one technique for resolv- ing this difficulty. While prior work demonstrated that Split Adaptive Interfaces improve performance on average, the re- sults of our study demonstrate systematic individual differ- ences in the utilization of the adaptive features, which cor- relate with the stable user traits of Need for Cognition and Extraversion. Specifically, higher Need for Cognition (a will- ingness to undertake difficult mental activities) is correlated with increased utilization rates, while higher Extraversion (a general orientation towards seeking gratification from the ex- ternal world) is negatively correlated with utilization rates. Our results also demonstrate a significant negative correlation between cognitive load induced by a secondary task and the utilization of the adaptive features. This effect, however, is very small (less than two percentage points). Together, these results provide additional evidence of the usefulness of the split adaptive interface approach and a negligible effect of ad- ditional cognitive load, but also demonstrate that the approach does not benefit all users equally. Author Keywords Adaptive user interfaces, cognitive load, extraversion, need for cognition ACM Classification Keywords H.5.m. Information Interfaces and Presentation: Miscella- neous

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Deep Sequential Recommendation for Personalized Adaptive User InterfacesUnknown · 2017Adaptive user-interfaces (AUIs) can enhance the usability of complex software by providing real-time contextual adapta- tion and assistance. Ideally, AUIs should be personalized and versatile, i.e., able to adapt to each user who may perform a variety of complex tasks. But this is difficult to achieve with many interaction elements when data-per-user is sparse. In this paper, we propose an architecture for personalized AUIs that leverages upon developments in (1) deep learning, par- ticularly gated recurrent units, to efficiently learn user inter- action patterns, (2) collaborative filtering techniques that en- able sharing of data among users, and (3) fast approximate nearest-neighbor methods in Euclidean spaces for quick UI control and/or content recommendations. Specifically, inter- action histories are embedded in a learned space along with users and interaction elements; this allows the AUI to query and recommend likely next actions based on similar usage patterns across the user base. In a comparative evaluation on user-interface, web-browsing and e-learning datasets, the deep recurrent neural-network (DRNN) outperforms state-of- the-art tensor-factorization and metric embedding methods. Author Keywords Adaptive User Interface; Deep Learning; Personalization ACM Classification Keywords I.2.6. Artificial Intelligence: Learning; H.5.2 Information In- terfaces and Presentation(e.g. HCI): User Interfaces

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Designing Responsive Interactive Applications by Emotion-Tracking and Pattern-Based Dynamic User Interface AdaptationSanim Rashi, Christian Herdin · 2016Model-based user interface development environments (MB-UIDEs) can be enhanced by pattern-based frameworks to allow for richer design capa‐ bilities and more flexible responsive behavior during the runtime of the imple‐ mented target application. In this paper an experimental system prototype for integrating facial analysis and eye-tracking into a pattern-based dynamic user interface adaptation process is discussed. The resulting system evaluates the emotional state of the system users to trigger the HCI-pattern-based adaptation of the user interface. By monitoring the emotional states of the users over longer time periods, while the system changes its behavior and its appearance, conclu‐ sions about the perceived quality dimensions of the interactive application can be drawn.

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Model-based adaptive user interface based on context and user experience evaluationJamil Hussain, Anees Ul Hassan, Hafiz Syed Muhammad Bilal, Rahman Ali, Muhammad Afzal, Shujaat Hussain, Jaehun Bang,Oresti Banos, Sungyoung Lee · 2016Personalized services have greater impact on user experience to effect the level of user satisfaction. Many approaches provide personalized services in the form of an adaptive user interface. The focus of these approaches is lim- ited to specific domains rather than a generalized approach applicable to every domain. In this paper, we proposed a domain and device-independent model-based adaptive user interfacing methodology. Unlike state-of-the-art approaches, the proposed methodology is dependent on the evaluation of user context and user experience (UX). The proposed methodologyisimplementedasanadaptiveUI/UXauthoring (A-UI/UX-A) tool; a system capable of adapting user inter- face based on the utilization of contextual factors, such as user disabilities, environmental factors (e.g. light level, noise level, and location) and device use, at runtime using the adap- tation rules devised for rendering the adapted interface. To validate effectiveness of the proposed A-UI/UX-A tool and methodology, user-centric and statistical evaluation methods are used. The results show that the proposed methodology B Sungyoung Lee [sylee@oslab.khu.ac.kr](mailto:sylee@oslab.khu.ac.kr) Jamil Hussain [jamil@oslab.khu.ac.kr](mailto:jamil@oslab.khu.ac.kr) 1 Department of Computer Science & Engineering, Kyung Hee University (Global Campus), 1732 Deokyoungdae-ro, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, Republic of Korea 2 Quaid-e-Azam College of Commerce, University of Peshawar, Peshawar, Pakistan 3 College of Electronics and Information Engineering, Sejong University, Seoul, South Korea 4 Telemedicine Group, Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Postbox 217, 7500 AE Enschede, The Netherlands outperforms the existing approaches in adapting user inter- faces by utilizing the users context and experience.

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Interface Design Optimization as a Multi-Armed Bandit ProblemJ. Derek Lomas, Jodi Forlizzi et al. · 2016“Multi-armed bandits” offer a new paradigm for the AI- assisted design of user interfaces. To help designers understand the potential, we present the results of two experimental comparisons between bandit algorithms and random assignment. Our studies are intended to show designers how bandits algorithms are able to rapidly explore an experimental design space and automatically select the optimal design configuration. Our present focus is on the optimization of a game design space. The results of our experiments show that bandits can make data-driven design more efficient and accessible to interface designers, but that human participation is essential to ensure that AI systems optimize for the right metric. Based on our results, we introduce several design lessons that help keep human design judgment in the loop. We also consider the future of human-technology teamwork in AI-assisted design and scientific inquiry. Finally, as bandits deploy fewer low- performing conditions than typical experiments, we discuss ethical implications for bandits in large-scale experiments in education. Author Keywords Educational games; optimization; data-driven design; multi- armed bandits; continuous improvement; ACM Classification Keywords H.5.m. Information interfaces and presentation (e.g., HCI): Miscellaneous.

UX of AI & PersonalizationResearch Papers

Predictability and Accuracy in Adaptive User InterfacesKrzysztof Z. Gajos, Katherine Everitt et al. · 2008While proponents of adaptive user interfaces tout potential performance gains, critics argue that adaptation's unpredict- ability may disorient users, causing more harm than good. We present a study that examines the relative effects of predictability and accuracy on the usability of adaptive UIs. Our results show that increasing predictability and accuracy led to strongly improved satisfaction. Increasing accuracy also resulted in improved performance and higher utiliza- tion of the adaptive interface. Contrary to our expectations, improvement in accuracy had a stronger effect on perform- ance, utilization and some satisfaction ratings than the im- provement in predictability. AUTHOR KEYWORDS Adaptive interfaces, predictability, accuracy, user study ACM Classification Keywords H5.2 Information Interfaces and Presentation: User Inter- faces – Interactions Styles, 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