Integrating Human Feedback into a Reinforcement Learning-Based Framework for Adaptive User Interfaces
Adaptive User Interfaces (AUI) play a crucial role in modern software applications by dynamically adjusting interface elements to accommodate users’ diverse and evolving needs. However, existing adaptation strategies often lack real-time responsiveness. Reinforcement Learning (RL) has emerged as a promising approach for addressing complex, sequential adaptation challenges, enabling adaptive systems to learn optimal policies based on previous adaptation experiences. Although RL has been applied to AUIs,integrating RL agents effectively within user interactions remains a challenge. In this paper, we enhance a RL-based Adaptive User Interface adaption framework by incorporating personalized human feedback directly into the leaning process. Unlike prior approaches that rely on a single pre-trained RL model, our approach trains a unique RL agent for each user, allowing individuals to actively shape their personal RL agent’s policy, potentially leading to more personalized and responsive UI adaptations. To evaluate this approach, we conducted an empirical study to assess the impact of integrating human feedback into the RL-based Adaptive User Interface adaption framework and its effect on User Experience (UX). The study involved 33 participants interacting with AUIs incorporating human feedback and non-adaptive user interfaces in two domains: an e-learning platform and a trip-planning application. The results suggest that incorporating human feedback into RL-driven adaptations significantly enhances UX, offering promising directions for advancing adaptive capabilities and user-centered design in AUIs.
Wed 18 JunDisplayed time zone: Athens change
13:30 - 15:00 | Human Factors in Software EngineeringLearnings/Reflections of Evaluation and Assessment projects in Software Engineering / Research Papers / Industry Papers at Glass Room Chair(s): Viktoria Stray University of Oslo / SINTEF | ||
13:30 15mPaper | Exploring turnover, retention and growth in an OSS Ecosystem Learnings/Reflections of Evaluation and Assessment projects in Software Engineering Tien Rahayu Tulili University of Groningen, Ayushi Rastogi University of Groningen, The Netherlands, Andrea Capiluppi University of Groningen Pre-print | ||
13:45 15mTalk | From Questions to Insights: Exploring XAI Challenges Reported on Stack Overflow Questions Research Papers Saumendu Roy University of Saskatchewan, Saikat Mondal University of Saskatchewan, Banani Roy University of Saskatchewan, Chanchal K. Roy University of Saskatchewan Pre-print | ||
14:00 15mTalk | How Do Communities of ML-Enabled Systems Smell? A Cross-Sectional Study on the Prevalence of Community Smells Research Papers Giusy Annunziata University of Salerno, Stefano Lambiase University of Salerno, Fabio Palomba University of Salerno, Gemma Catolino University of Salerno, Filomena Ferrucci Università di Salerno Pre-print | ||
14:15 15mPaper | Integrating Human Feedback into a Reinforcement Learning-Based Framework for Adaptive User Interfaces Learnings/Reflections of Evaluation and Assessment projects in Software Engineering Daniel Gaspar Figueiredo Universitat Politècnica de València, Spain, Marta Fernández-Diego Universitat Politècnica de València, Silvia Abrahão Universitat Politècnica de València, Emilio Insfran Universitat Politècnica de València, Spain Pre-print | ||
14:30 10mTalk | MBSR at Work: Perspectives from an Instructor and Software Developers Industry Papers Simone Romano University of Salerno, Alberto Conforti University of Torino, Gloria Guidetti University of Torino, Sara Viotti University of Torino, Rachele Ceschin Nuovo Centro Clinico, Giuseppe Scanniello University of Salerno | ||
14:40 15mTalk | Modeling Communication Perception in Development Teams Using Monte Carlo Methods Research Papers Marc Herrmann Leibniz University Hannover, Martin Obaidi Leibniz Universität Hannover, Jil Klünder University of Applied Sciences | FHDW Hannover Pre-print |