arXiv subjects

Emilio Insfran

Publications and source records attributed to Emilio Insfran.

3 recordsLinked to original sources

arXiv2026

The Landscape of Generative AI in Information Systems: A Synthesis of Secondary Reviews and Research Agendas

The post-ChatGPT surge has rapidly reframed IS research and practice. As organizations and society grapple with GenAI adoption, a body of secondary studies and research agendas has emerged to synthesize early evidence and chart directions for future inquiry. This study reviews secondary and roadmap papers to synthesize the state of knowledge on GenAI's benefits and challenges in IS, and to identify future research directions. We performed a systematic search across Scopus, WoS, and eAIS for publications from 2023 onwards. Following a rigorous, multi-stage screening process, we selected a final set of 28 papers for analysis using bibliometric mapping and thematic analysis. We also conducted a quality assessment of all sources to gauge confidence in each source's contribution to the findings. GenAI offers transformative potential to drive productivity, accelerate innovation, personalize services, and democratize access to expertise. However, its adoption is constrained by interrelated challenges: technical unreliability, societal-ethical risks, and a governance vacuum. Interpreted through a socio-technical lens, our findings reveal a persistent misalignment between GenAI's fast-evolving technical subsystem and the slower-adapting social subsystem, positioning IS research as critical for achieving joint optimization. To bridge this gap, we propose a research agenda that reorients IS scholarship from analyzing impacts toward actively shaping the co-evolution of technical capabilities with organizational routines, societal values, and regulatory institutions: emphasizing hybrid human-AI ensembles, situated validation, design principles for probabilistic systems, and adaptive governance. For practitioners and policymakers, responsible adoption requires balancing automation with human augmentation alongside transparent governance and adaptive regulations to ensure broadly shared benefits.

cs.CY↗

arXiv2025

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.

cs.HC↗

arXiv2023

A Comparative Study on Reward Models for UI Adaptation with Reinforcement Learning

Adapting the User Interface (UI) of software systems to user requirements and the context of use is challenging. The main difficulty consists of suggesting the right adaptation at the right time in the right place in order to make it valuable for end-users. We believe that recent progress in Machine Learning techniques provides useful ways in which to support adaptation more effectively. In particular, Reinforcement learning (RL) can be used to personalise interfaces for each context of use in order to improve the user experience (UX). However, determining the reward of each adaptation alternative is a challenge in RL for UI adaptation. Recent research has explored the use of reward models to address this challenge, but there is currently no empirical evidence on this type of model. In this paper, we propose a confirmatory study design that aims to investigate the effectiveness of two different approaches for the generation of reward models in the context of UI adaptation using RL: (1) by employing a reward model derived exclusively from predictive Human-Computer Interaction (HCI) models (HCI), and (2) by employing predictive HCI models augmented by Human Feedback (HCI&HF). The controlled experiment will use an AB/BA crossover design with two treatments: HCI and HCI&HF. We shall determine how the manipulation of these two treatments will affect the UX when interacting with adaptive user interfaces (AUI). The UX will be measured in terms of user engagement and user satisfaction, which will be operationalized by means of predictive HCI models and the Questionnaire for User Interaction Satisfaction (QUIS), respectively. By comparing the performance of two reward models in terms of their ability to adapt to user preferences with the purpose of improving the UX, our study contributes to the understanding of how reward modelling can facilitate UI adaptation using RL.

cs.SE↗