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It ’ s just distributed computing: Rethinking AI governanceIndia (TRAI · 2025What we now lump under the unitary label “artificial intelligence” is not a single technology, but a highly varied set of machine learning appli­ cations enabled and supported by a globally ubiquitous system of distributed computing. The paper introduces a 4 part conceptual framework for analyzing the structure of that system, which it labels the digital ecosystem. What we now call “AI” is then shown to be a general functionality of distributed computing. "AI” has been present in primitive forms from the origins of digital computing in the 1950s. Three short case studies show that large-scale machine learning applications have been present in the digital ecosystem ever since the rise of the Internet. and provoked the same public policy concerns that we now associate with “AI.” The governance problems of “AI” are really caused by the development of this digital ecosystem, not by LLMs or other recent applications of machine learning. The paper then examines five recent proposals to “govern AI”and maps them to the constituent elements of the digital ecosystem model. This mapping shows that real-world attempts to assert governance authority over AI capabilities requires systemic control of all four elements of the digital ecosystem: data, computing power, networks and software. “Governing AI,” in other words, means total control of distributed computing. A better alternative is to focus governance and regulation upon specific appli­ cations of machine learning. An application-specific approach to governance allows for a more decentralized, freer and more effective method of solving policy conflicts.

UX of AI & PersonalizationResearch Papers

Advancements in Context Recognition for Edge Devices and Smart Eyewear: Sensors and ApplicationsFrancesca Palermo, Luca Casciano · 2025Edge devices have garnered significant attention for their ability to process data locally, providing low-latency, context-aware services without the need for extensive reliance on cloud computing. This capability is particularly crucial in context recognition, which enables dynamic adaptation to a user’s real-time environment. Applications range from health monitoring and augmented reality to smart assistance and social interaction analysis. Among edge devices, smart eyewear has emerged as a promising platform for context recognition due to its ability to unobtrusively capture rich, multi-modal sensor data. However, the deployment of context-aware systems on such devices presents unique challenges, including real- time processing, energy efficiency, sensor fusion, and noise management. This manuscript provides a comprehensive survey of context recognition in edge devices, with a specific emphasis on smart eyewear. It reviews the state-of-the-art sensors and applications for context inference. Furthermore, the paper discusses key challenges in achieving reliable, low-latency context recognition while addressing energy and computational constraints. By synthesizing advancements and identifying gaps, this work aims to guide the development of more robust and efficient solutions for context recognition in edge computing. INDEX TERMS Context recognition, edge computing, smarteyewear, wearable technology.

UX of AI & PersonalizationResearch Papers

Identifying Hand-based Input Preference Based on Wearable EEGKaining Zhang∗ · 2024Understanding user input preference can improve the user experi- ence, however automatically determining preference can be diffi- cult. In this paper, we designed an EEG-based method for directly evaluating hand-based input preference for touch and mid-air ges- tures on a smartwatch. We conducted a two-phase experiment, recording EEG data from 18 participants as they performed ges- tures and captured their ratings (Phase 1) and preference choices (Phase 2) for each gesture. Our analysis uncovered distinct EEG patterns between preferred and non-preferred gestures, including significant differences in Power Spectral Density (PSD), Coherence (Coh), and Sample Entropy (SE) features. When participants en- gaged with their preferred input gestures, we identified decreased brain activity (PSD) in the central and occipital regions, reduced brain connectivity (Coh) in the delta and alpha bands, and increased brain complexity (SE) in multiple sizes. These insights offer the potential to develop rapid detection of user intent for interactive computing devices by analysing brain signals. 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 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 specific permission and/or a fee. Request permissions from permissions@acm.org. AHs 2024, April 04–06, 2024, Melbourne, VIC, Australia © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM. ACM ISBN 979-8-4007-0980-7/24/04 https://doi.org/10.1145/3652920.3653028 CCS CONCEPTS • Human-centered computing →User studies; Gestural input; Laboratory experiments.

UX of AI & PersonalizationResearch Papers

Predictive Analytics for Website User BehaviorPawan Vankhede · 2024This study examines the application of predictive analytics to user behavior analysis on websites, with a particular emphasis on a dataset of customer behavior from e-commerce. To forecast behavioral patterns, the study will apply the random forest method. Additionally, exploratory data analysis (EDA) will be carried out to extract insights from the dataset. The dataset employed in this study includes a variety of factors, such as purchase history, browsing habits, and consumer demographics, among other pertinent data. The research attempts to derive useful insights that can improve the comprehension and prediction of user behavior on the website by utilizing predictive analytics approaches. In this study, the random forest algorithm which is well-known for its capacity to manage intricate datasets and generate precise predictions is employed. The method builds several decision trees and combines their predictions to produce more reliable and accurate outcomes by utilizing ensemble learning. Predicting different aspects of user behavior, including browsing patterns, chance of making a purchase, and customer segmentation, is the aim. When paired with the insights from EDA, the outcomes of the behavioral analysis prediction made with the random forest algorithm can provide e-commerce companies with useful data. Gaining insight into user behavior can help with customer engagement, website design optimization, personalization, and overall user experience enhancement.

UX of AI & PersonalizationResearch Papers

Article LIME-Mine: Explainable Machine Learning for User Behavior Analysis in IoT ApplicationsXiaobo Cai · 2024In Internet of Things (IoT) applications, user behavior is influenced by factors such as network structure, user activity, and location. Extracting valuable patterns from user activity traces can lead to the development of smarter, more personalized IoT applications and improved user experience. This paper proposes a LIME-based user behavior preference mining algorithm that leverages Explainable AI (XAI) techniques to interpret user behavior data and extract user preferences. By training a black-box neural network model to predict user behavior using LIME and approximating predictions with a local linear model, we identify key features influencing user behavior. This analysis reveals user behavioral patterns and preferences, such as habits at specific times, locations, and device states. Incorporating user behavioral information into the resource scheduling process, combined with a feedback mechanism, establishes an active discovery network of user demand. Our approach, utilizing edge computing capabilities, continuously fine-tunes and optimizes resource scheduling, actively adapting to user perceptions. Experimental results demonstrate the effectiveness of feedback control in satisfying diverse user resource requests, enhancing user satisfaction, and improving system resource utilization.

UX of AI & PersonalizationResearch Papers

An empirical study of AI techniques in mobile applications ✩Haoye Tian b, Zhijie Wang c et al. · 2024The integration of artificial intelligence (AI) into mobile applications has significantly transformed various domains, enhancing user experiences and providing personalized services through advanced machine learning (ML) and deep learning (DL) technologies. AI-driven mobile apps typically refer to applications that leverage ML/DL technologies to perform key tasks such as image recognition and natural language processing. Despite existing research exploring how mobile apps exploit AI techniques, they have the following main limitations: (1) Most existing studies focus on DL-based apps, with limited research on ML-based apps. (2) Existing research typically focuses on investigating the apps and the technologies utilized in the apps, lacking user-level analysis. (3) The number of apps studied is limited, with only 1,000 to 2,000 ML/DL apps identified after filtering. To fill the gap, in this paper, we conducted the most extensive empirical study on AI applications, exploring on-device ML apps, on-device DL apps, and AI service-supported (cloud-based) apps. Our study encompasses 56,682 real- world AI applications, focusing on three crucial perspectives: (1) Application analysis, where we analyze the popularity of AI apps and investigate the update states of AI apps; (2) Framework and model analysis, where we analyze AI framework usage and AI model protection; (3) User analysis, where we examine user privacy protection and user review attitudes. Our study has strong implications for AI app developers, users, and AI R&D. On one hand, our findings highlight the growing trend of AI integration in mobile applications, demonstrating the widespread adoption of various AI frameworks and models. On the other hand, our findings emphasize the need for robust model protection to enhance app security. Additionally, our study highlights the importance of user privacy and presents user attitudes towards the AI technologies utilized in current AI apps. We provide our AI app dataset (currently the most extensive AI app dataset) as an open-source resource for future research on AI technologies utilized in mobile applications.

UX of AI & PersonalizationResearch Papers

Machine Learning Algorithms for Improved Product Design User ExperienceXueli Wang, Bo Hu · 2024With the rapid advancement of technology and the increasing demand for user-centric products, the integration of machine learning techniques has become imperative. This paper explores the transformative potential of integrating Particle Swarm Optimization (PSO), Deep Reinforcement Learning (DRL), and other machine learning algorithms such as neural networks, decision trees, and support vector machines into product design processes. Our novel hybrid framework leverages PSO’s global search capabilities and DRL’s adaptive learning to optimize product designs in a manner that traditional methods cannot achieve. By employing predictive modeling, clustering, and recommendation systems, designers can gain valuable insights into user needs and preferences, facilitating the creation of more intuitive and personalized products. We demonstrate that this integrated approach significantly improves design efficiency and user satisfaction. Key findings include a 25% reduction in design iteration time and a 30% increase in user satisfaction scores compared to traditional optimization methods. Additionally, our methodology provides a flexible and scalable solution adaptable to various product design contexts, showcasing its broad applicability and effectiveness. The incorporation of real-time feedback mechanisms allows for continuous refinement and adaptation of product designs to meet evolving user expectations. This study contributes to the field by presenting a comprehensive, multi-technique optimization framework that bridges existing gaps and sets a new standard for user-centric product design optimization. Ultimately, this research underscores the significance of embracing machine learning as a powerful tool for revolutionizing the product design landscape and delivering superior user experiences. INDEX TERMS Machine learning algorithms, product design, user experience enhancement, user data analysis, predictive modeling.

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

Tracing and Visualizing Human-ML/AI Collaborative Processes through Artifacts of Data WorkJen Rogers · 2023Automated Machine Learning (AutoML) technology can lower bar- riers in data work yet still requires human intervention to be func- tional. However, the complex and collaborative process resulting from humans and machines trading off work makes it difficult to trace what was done, by whom (or what), and when. In this research, we construct a taxonomy of data work artifacts that captures Au- toML and human processes. We present a rigorous methodology for its creation and discuss its transferability to the visual design process. We operationalize the taxonomy through the development of AutoML Trace a visual interactive sketch showing both the con- text and temporality of human-ML/AI collaboration in data work. Finally, we demonstrate the utility of our approach via a usage sce- nario with an enterprise software development team. Collectively, our research process and findings explore challenges and fruitful avenues for developing data visualization tools that interrogate the sociotechnical relationships in automated data work. Availability of Supplemental Materials: https://osf.io/3nmyj/ ?view_only=19962103d58b45d289b5c83421f48b36 This work is licensed under a Creative Commons Attribution International 4.0 License. CHI ’23, April 23–28, 2023, Hamburg, Germany © 2023 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-9421-5/23/04. https://doi.org/10.1145/3544548.3580819 CCS CONCEPTS • Human-centered computing →Visualization theory, concepts and paradigms; • Computing methodologies →Artificial intelli- gence.

UX of AI & PersonalizationResearch Papers

A study of UX practitioners roles in designing real-world, enterprise ML systemsSabah Zdanowsk · 2022Opportunities for AI and machine learning (ML) are vast in current interactive systems development. However, comparatively little is known about how functionality for the system behind the interface is designed and how design methodologies such as user-centered de- sign have influence. This research focuses on how interdisciplinary teams that include UX practitioners design real-world enterprise ML systems outside of big technology companies. We conducted a survey with product managers, and interviews with interdisci- plinary teams and individual UX practitioners. The findings show that nontechnical UX practitioners are highly capable in designing AI/ML systems. In addition to applying UX and interaction design expertise to make decisions regarding functionality, they employ skills that aid collaboration across interdisciplinary teams. How- ever, our findings suggest existing HCI design techniques such as prototyping and simulating complexity of enterprise ML systems are insufficient. We propose adaptations to design practices and conclude that some existing research should be reconsidered. CCS CONCEPTS • Human-centered computing →Empirical studies in HCI; • Software and its engineering →Designing software.

UX of AI & PersonalizationResearch Papers

Personalized User Interface Elements Recommendation SystemHao Liu, Xiangxian Li et al. · 2022This paper introduces a personalized user interface element recom- mendation system, in which the model can recommend personalized user inter- face elements by introducing user features and user evaluations in the offline training. Through experiments, we found that compared with common machine learning algorithms, the Field-aware Factorization Machine that introduced user feature intersections has achieved a better accuracy in the recommendation, which shows the advantages of introducing user features and feature intersections in the recommendation of interface elements.

UX of AI & PersonalizationResearch Papers

Inertial Motion Tracking on Mobile and Wearable Devices: Recent Advancements and ChallengesZhipeng Song, Zhichao Cao et al. · 2021Motion tracking via Inertial Measurement Units (IMUs) on mobile and wearable devices has attracted significant interest in recent years. High-accuracy IMU-tracking can be applied in various applications, such as indoor navigation, gesture recognition, text input, etc. Many efforts have been devoted to improving IMU-based motion tracking in the last two decades, from early calibration techniques on ships or airplanes, to recent arm motion models used on wearable smart devices. In this paper, we present a comprehensive survey on IMU-tracking techniques on mobile and wearable devices. We also reveal the key challenges in IMU-based motion tracking on mobile and wearable devices and possible directions to address these challenges. Key words: Inertial Measurement Units (IMUs); motion tracking; accelerometer; gyroscope; magnetometer; sensor fusion

UX of AI & PersonalizationResearch Papers

Artificial intelligence in E‑Commerce: a bibliometric study and literature reviewBawack · 2021This paper synthesises research on artificial intelligence (AI) in e-commerce and proposes guidelines on how information systems (IS) research could contribute to this research stream. To this end, the innovative approach of combining bibliometric analysis with an extensive literature review was used. Bibliometric data from 4335 documents were analysed, and 229 articles published in leading IS journals were reviewed. The bibliometric analysis revealed that research on AI in e-commerce focuses primarily on recommender systems. Sentiment analysis, trust, personalisation, and optimisation were identified as the core research themes. It also places China- based institutions as leaders in this researcher area. Also, most research papers on AI in e-commerce were published in computer science, AI, business, and management outlets. The literature review reveals the main research topics, styles and themes that have been of interest to IS scholars. Proposals for future research are made based on these findings. This paper presents the first study that attempts to synthesise research on AI in e-commerce. For researchers, it contributes ideas to the way forward in this research area. To practitioners, it provides an organised source of information on how AI can support their e-commerce endeavours.

UX of AI & PersonalizationResearch Papers

Personalization in Practice: Methods and ApplicationsDmitri Goldenberg · 2021Personalization is one of the key applications in machine learning with widespread usage across e-commerce, entertainment, pro- duction, healthcare and many other industries. While various ma- chine learning techniques present novel state-of-the-art advances and super-human performance year-over-year, personalization and recommender-systems applications are often late-adopters of novel solutions due to problem hardness and implementation complexity. This tutorial presents recent advances across the personaliza- tion industry and demonstrates their practical applications in real case-studies of world-leading online platforms. Key trends such as deep learning, causality and active exploration with bandits are depicted with real examples and demonstrated alongside their busi- ness considerations and implementation challenges. Rising topics like explainability, fairness, natural interfaces and content gener- ation are covered, touching on aspects of both technology and user experience. Our tutorial relies on recent advances in the field and on work conducted at Booking.com, where we implement per- sonalization models on one of the world’s leading online travel platform. CCS CONCEPTS • Information systems →Personalization; Recommender sys- tems.

UX of AI & PersonalizationResearch Papers

Measuring and Improving User Experience Through Artificial Intelligence-Aided Design[Unknown · 2020This paper aims to propose a methodology for measuring user experience (UX) by using artificial intelligence-aided design (AIAD) technology in mobile application design. Unlike the traditional assistance design tools, AIAD focuses on the rational use of artificial intelligence (AI) technology to measure and improve UX since conventional data collection methods (such as user interview and user observation) for user behavior data are inefficient and time-consuming. We propose to obtain user behavior data from logs of mobile application. In order to protect the privacy of users, only a few dimensions of information is used in the process of browsing and operating mobile application. The goal of the proposed methodology is to make the deep neural network model simulate the user's experience in the process of operating a mobile application as much as possible. We design and use projected pages of application to train neural networks for specific tasks. These projected pages consist of the click information of all users in the process of completing a certain task. Thus, features of user behavior can be aggregated and mapped in the connection layers and the hidden layers. Finally, the optimized design is executed on the social communication application to verify the efficiency of the proposed methodology.

UX of AI & PersonalizationResearch Papers

An Effective Clustering‑Based Web Page Recommendation Framework for E‑Commerce WebsitesHarpreet Singh · 2020The burgeoning e-commerce market has presented companies with the opportunity to grow their businesses through online platforms. But, the researchers have concluded that just 2.86% of e-commerce website visits lead to a purchase and one of the reasons for this missed opportunity is an unpleasant website browsing experience. Therefore, a pleasant browsing experience is the need of the hour whereby the web page recommendation systems (WPRS) provide high-quality navigation experience by providing suggestions about the web pages of interest and by taking the website users to their desired web pages in fewer clicks. In this context, this paper presents a method to improve the browsing experience of the website users by propos- ing two hybrid algorithms based on clustering for web page recommendation systems, namely a hybrid partitioning-based heuristic sequence clustering (HSC) algorithm inspired from K-medoid and DBSCAN algorithms and a hybrid tree-based sequence clustering (TSC) algorithm inspired from B-Trees and BIRCH algorithm. The testing has been performed using CTI, BMSWebView1, BMSWebView2 and MSNBC datasets. To measure the performance, the algorithm considered for the study has been evaluated using parameters like precision, recall, F1 measures and execution time. Also, an in-depth comparative analysis of state-of-the-art web page recommendation systems with the recommendation system considered for the study has been done. The results indicate that the proposed clustering-based framework was able to generate superior results than the other classes of algorithms.

UX of AI & PersonalizationResearch Papers

Artificial intelligence in recommender systemsZhang · 2020Recommender systems provide personalized service support to users by learning their previous behaviors and predicting their current preferences for particular products. Artificial intelligence (AI), particularly computational intelligence and machine learningmethodsandalgorithms,hasbeennaturallyappliedinthedevelopmentofrecommendersystemstoimproveprediction accuracy and solve data sparsity and cold start problems. This position paper systematically discusses the basic methodologies and prevailing techniques in recommender systems and how AI can effectively improve the technological development and application of recommender systems. The paper not only reviews cutting-edge theoretical and practical contributions, but also identifies current research issues and indicates new research directions. It carefully surveys various issues related to recommender systems that use AI, and also reviews the improvements made to these systems through the use of such AI approaches as fuzzy techniques, transfer learning, genetic algorithms, evolutionary algorithms, neural networks and deep learning, and active learning. The observations in this paper will directly support researchers and professionals to better understand current developments and new directions in the field of recommender systems using AI.

UX of AI & PersonalizationResearch Papers

A survey of cyber‑physical system implementations of real‑time personalized interventionsRobert Steele · 2019Advances in sensor technology and machine learning as well as the widespread use of smartphones are shifting the focus of healthcare. Emerging paradigms such as cyber-physical systems (CPSs) make possible the transition from reactive to preventive care. CPSs can be implemented to achieve effective mobile health solutions and to provide sophisticated new mechanisms to monitor an individual’s state in real-time via the use of sensors and mobile devices. Despite the significant potential impact of such systems, their implementation poses a range of complex technical challenges. This article surveys the state-of-the-art in implementations of CPSs for real-time personalized interventions. A general three layer architecture which can be used to consider current implementations is first presented along with a description of its main components. We also propose a three level taxonomy in accordance with the system capabilities. Then, the principal technical challenges, human-machine interaction challenges and future directions are discussed. Fifteen of the state-of-the-art implementations are qualitatively evaluated in terms of sensor capabilities, just-in-time reaction, interruptibility, and adherence, among other characteristics. By reviewing the state-of-the-art of the systems that have been built to-date, the focus of the review is to summarize current technical challenges and future opportunities for both future CPS implementers and behavioral scientists designing CPS for personalized interventions.

UX of AI & PersonalizationResearch Papers

Investigating How Experienced UX Designers Effectively Work with Machine LearningQian Yang · 2018Machine learning (ML) plays an increasingly important role in improving a user’s experience. However, most UX practitioners face challenges in understanding ML’s capabilities or envisioning what it might be. We interviewed 13 designers who had many years of experience designing the UX of ML-enhanced products and services. We probed them to characterize their practices. They shared they do not view themselves as ML experts, nor do they think learning more about ML would make them better designers. Instead, our participants appeared to be the most successful when they engaged in ongoing collaboration with data scientists to help envision what to make and when they embraced a data-centric culture. We discuss the implications of these findings in terms of UX education and as opportunities for additional design research in support of UX designers working with ML. Author Keywords User Experience Design; UX Practice; Machine Learning; Design Material; Interaction Design. ACM Classification Keywords H.5.m. Information Interfaces and Presentation (e.g. HCI): Miscellaneous

UX of AI & PersonalizationResearch Papers

UX Design Innovation: Challenges for Working with Machine Learning as a Design MaterialGraham Dove, Kim Halskov · 2017Machine learning (ML) is now a fairly established technology, and user experience (UX) designers appear regularly to integrate ML services in new apps, devices, and systems. Interestingly, this technology has not experienced a wealth of design innovation that other technologies have, and this might be because it is a new and difficult design material. To better understand why we have witnessed little design innovation, we conducted a survey of current UX practitioners with regards to how new ML services are envisioned and developed in UX practice. Our survey probed on how ML may or may not have been a part of their UX design education, on how they work to create new things with developers, and on the challenges they have faced working with this material. We use the findings from this survey and our review of related literature to present a series of challenges for UX and interaction design research and education. Finally, we discuss areas where new research and new curriculum might help our community unlock the power of design thinking to re-imagine what ML might be and might do. Author Keywords UX practice; design material; machine learning; interaction design; ACM Classification Keywords H.5.m. Information interfaces and presentation (e.g., HCI): Miscellaneous;

UX of AI & PersonalizationResearch Papers

Machine Learning Techniques for Recommender Systems – A Comparative Case AnalysisBinu Thomas · 2011Recommender System (RS) is one of the most popular applications of Artificial Intelligence which attracted researchers all around the world. Many machine learning algorithms are used to develop RSs. Choosing the best machine learning algorithm to provide users with a product or service is the most challenging task in the area of RSs. Now we are witnessing a paradigm shift in the purchase habits of people from in-shop to online resulting in the availability of online information exponentially growing every day. The ever-increasing online information and the number of online users create new avenues in RS. In an online shopping scenario, these systems must be able to recommend relevant items to the users. The RSs have to deal with the huge amount of information by filtering the relevant information based on the analysis made on the inputs made by the users during their online sessions. These systems can recommend appropriate items to users based on their interest and previous preference which can lead to increased sales. The three major techniques used to build a RS are content-based, collaborative based and hybrid-based. This paper presents the various applications of RSs and makes a detailed comparative study of different machine learning approaches used. The methodologies used for identifying research articles for analysis, the merits and demerits of different techniques in RSs and domain-specific applications of these techniques are well explained here with scientific review analysis.

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