Proposes a low-code/no-code pipeline for modeling and generating personalized conversational agents, implemented on an open-source platform with a pilot usability study.
Towards a unified user modeling language for engineering human centered ai systems
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
In today's digital society, personalization has become a crucial aspect of software applications, significantly impacting user experience and engagement. A new wave of intelligent user interfaces, such as AI-based conversational agents, has the potential to enable such personalization beyond what other types of interfaces could offer in the past. Personalization requires the ability to specify a complete user profile, covering as many dimensions as possible, such as potential accessibility constraints, interaction preferences, and even hobbies. Yet, existing solutions for user modeling mostly focus on individual aspects at a very coarse level, severely limiting the potential adaptations for personalization. In this sense, this paper presents a unified user modeling language, aimed to combine previous approaches, both from the modeling community and other user-centric fields, in a single proposal. This language has been implemented on top of the open source BESSER low-code platform. Additionally, a proof of concept leveraging user profiles modeled with our language to automatically adapt a conversational agent has also been developed.
citation-role summary
citation-polarity summary
fields
cs.SE 1years
2026 1verdicts
UNVERDICTED 1roles
method 1polarities
use method 1representative citing papers
citing papers explorer
-
A Low-Code Approach for the Automatic Personalization of Conversational Agents
Proposes a low-code/no-code pipeline for modeling and generating personalized conversational agents, implemented on an open-source platform with a pilot usability study.