Adaptive and Accessible User Interfaces for Seniors Through Model-Driven Engineering
- URL: http://arxiv.org/abs/2502.18828v1
- Date: Wed, 26 Feb 2025 05:03:22 GMT
- Title: Adaptive and Accessible User Interfaces for Seniors Through Model-Driven Engineering
- Authors: Shavindra Wickramathilaka, John Grundy, Kashumi Madampe, Omar Haggag,
- Abstract summary: AdaptForge is a novel model-driven engineering (MDE)-based approach to support sophisticated adaptations of Flutter app user interfaces and behaviour.<n>We explain how AdaptForge employs Domain-Specific Languages to capture seniors' context-of-use scenarios.<n>We report on evaluations conducted with real-world Flutter developers to demonstrate the promise and practical applicability of AdaptForge.
- Score: 4.220379425971002
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The use of diverse apps among senior users is increasing. However, despite their diverse age-related accessibility needs and preferences, these users often encounter apps with significant accessibility barriers. Even in the best-case scenarios, they are provided with one-size-fits-all user interfaces that offer very limited personalisation support. To address this issue, we describe AdaptForge, a novel model-driven engineering (MDE)-based approach to support sophisticated adaptations of Flutter app user interfaces and behaviour based on the age-related accessibility needs of senior users. We explain how AdaptForge employs Domain-Specific Languages to capture seniors' context-of-use scenarios and how this information is used via adaptation rules to perform design-time modifications to a Flutter app's source code. Additionally, we report on evaluations conducted with real-world Flutter developers to demonstrate the promise and practical applicability of AdaptForge, as well as with senior end-users using our adapted Flutter app prototypes.
Related papers
- Developer Perceptions on Utilising Low-Code Approaches to Build Accessible and Adaptive Applications for Seniors [4.220379425971002]
AdaptForge is a low-code model-driven engineering tool that enables the efficient creation of accessible and adaptive applications for senior users.<n>This paper presents an interview-based empirical study with 18 software practitioners, evaluating AdaptForge.<n>We identify developer expectations for adopting such tools and provide empirically grounded recommendations for designing low-code tools that support accessible and adaptive software development.
arXiv Detail & Related papers (2025-08-05T00:14:52Z) - UserBench: An Interactive Gym Environment for User-Centric Agents [110.77212949007958]
Large Language Models (LLMs)-based agents have made impressive progress in reasoning and tool use, but their ability to proactively collaborate with users remains underexplored.<n>We introduce UserBench, a user-centric benchmark designed to evaluate agents in multi-turn, preference-driven interactions.
arXiv Detail & Related papers (2025-07-29T17:34:12Z) - PersonaAgent: When Large Language Model Agents Meet Personalization at Test Time [87.99027488664282]
PersonaAgent is a framework designed to address versatile personalization tasks.<n>It integrates a personalized memory module and a personalized action module.<n>Test-time user-preference alignment strategy ensures real-time user preference alignment.
arXiv Detail & Related papers (2025-06-06T17:29:49Z) - Towards a unified user modeling language for engineering human centered AI systems [1.7450893625541586]
A new wave of intelligent user interfaces, such as AI-based conversational agents, has the potential to enable such personalization.<n>This paper presents the concepts of a unified user modeling language, aimed to combine previous approaches in a single proposal.<n>A proof of concept has been developed that leverages user profiles modeled using our language to automatically adapt a conversational agent.
arXiv Detail & Related papers (2025-05-30T15:20:15Z) - Creating General User Models from Computer Use [62.91116265732001]
This paper presents an architecture for a general user model (GUM) that learns about you by observing any interaction you have with your computer.<n>The GUM takes as input any unstructured observation of a user (e.g., device screenshots) and constructs confidence-weighted propositions that capture user knowledge and preferences.
arXiv Detail & Related papers (2025-05-16T04:00:31Z) - Accessibility Recommendations for Designing Better Mobile Application User Interfaces for Seniors [4.220379425971002]
Senior users represent a growing user base for mobile applications.
Many apps fail to adequately address their accessibility challenges and usability preferences.
We developed a model-driven engineering toolset to generate adaptive mobile app prototypes tailored to seniors' needs.
arXiv Detail & Related papers (2025-04-17T06:32:05Z) - Large Language Model Empowered Recommendation Meets All-domain Continual Pre-Training [60.38082979765664]
CPRec is an All-domain Continual Pre-Training framework for Recommendation.<n>It holistically align LLMs with universal user behaviors through the continual pre-training paradigm.<n>We conduct experiments on five real-world datasets from two distinct platforms.
arXiv Detail & Related papers (2025-04-11T20:01:25Z) - Semantic Library Adaptation: LoRA Retrieval and Fusion for Open-Vocabulary Semantic Segmentation [72.28364940168092]
Open-vocabulary semantic segmentation models associate vision and text to label pixels from an undefined set of classes using textual queries.
We introduce Semantic Library Adaptation (SemLA), a novel framework for training-free, test-time domain adaptation.
arXiv Detail & Related papers (2025-03-27T17:59:58Z) - Generative AI in Multimodal User Interfaces: Trends, Challenges, and Cross-Platform Adaptability [0.0]
Generative AI emerges as a key driver in reshaping user interfaces.
This paper explores the integration of generative AI in modern user interfaces.
It focuses on multimodal interaction, cross-platform adaptability and dynamic personalization.
arXiv Detail & Related papers (2024-11-15T14:49:58Z) - MDAP: A Multi-view Disentangled and Adaptive Preference Learning Framework for Cross-Domain Recommendation [63.27390451208503]
Cross-domain Recommendation systems leverage multi-domain user interactions to improve performance.
We propose the Multi-view Disentangled and Adaptive Preference Learning framework.
Our framework uses a multiview encoder to capture diverse user preferences.
arXiv Detail & Related papers (2024-10-08T10:06:45Z) - Reinforcement Learning-Based Framework for the Intelligent Adaptation of User Interfaces [0.0]
Adapting the user interface (UI) of software systems to meet the needs and preferences of users is a complex task.
Recent advances in Machine Learning (ML) techniques may provide effective means to support the adaptation process.
In this paper, we instantiate a reference framework for Intelligent User Interface Adaptation by using Reinforcement Learning (RL) as the ML component.
arXiv Detail & Related papers (2024-05-15T11:14:33Z) - Unlocking Adaptive User Experience with Generative AI [8.578448990789965]
We develop user personas and adaptive interfaces using both ChatGPT and a traditional manual process.
To obtain data for the personas we collected data from 37 survey participants and 4 interviews.
The comparison of ChatGPT generated content and manual content indicates promising results.
arXiv Detail & Related papers (2024-04-08T12:22:39Z) - Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyond [87.1712108247199]
Our goal is to establish a Unified paradigm for Multi-modal Personalization systems (UniMP)
We develop a generic and personalization generative framework, that can handle a wide range of personalized needs.
Our methodology enhances the capabilities of foundational language models for personalized tasks.
arXiv Detail & Related papers (2024-03-15T20:21:31Z) - Large Language User Interfaces: Voice Interactive User Interfaces powered by LLMs [5.06113628525842]
We present a framework that can serve as an intermediary between a user and their user interface (UI)
We employ a system that stands upon textual semantic mappings of UI components, in the form of annotations.
Our engine can classify the most appropriate application, extract relevant parameters, and subsequently execute precise predictions of the user's expected actions.
arXiv Detail & Related papers (2024-02-07T21:08:49Z) - Adapters: A Unified Library for Parameter-Efficient and Modular Transfer
Learning [109.25673110120906]
We introduce Adapters, an open-source library that unifies parameter-efficient and modular transfer learning in large language models.
By integrating 10 diverse adapter methods into a unified interface, Adapters offers ease of use and flexible configuration.
arXiv Detail & Related papers (2023-11-18T13:53:26Z) - Latent User Intent Modeling for Sequential Recommenders [92.66888409973495]
Sequential recommender models learn to predict the next items a user is likely to interact with based on his/her interaction history on the platform.
Most sequential recommenders however lack a higher-level understanding of user intents, which often drive user behaviors online.
Intent modeling is thus critical for understanding users and optimizing long-term user experience.
arXiv Detail & Related papers (2022-11-17T19:00:24Z) - Adapting User Interfaces with Model-based Reinforcement Learning [47.469980921522115]
Adapting an interface requires taking into account both the positive and negative effects that changes may have on the user.
We propose a novel approach for adaptive user interfaces that yields a conservative adaptation policy.
arXiv Detail & Related papers (2021-03-11T17:24:34Z) - Unsupervised Model Personalization while Preserving Privacy and
Scalability: An Open Problem [55.21502268698577]
This work investigates the task of unsupervised model personalization, adapted to continually evolving, unlabeled local user images.
We provide a novel Dual User-Adaptation framework (DUA) to explore the problem.
This framework flexibly disentangles user-adaptation into model personalization on the server and local data regularization on the user device.
arXiv Detail & Related papers (2020-03-30T09:35:12Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.