TY - DATA T1 - Acquiring Semantic Knowledge for User Model Updates via Human-Agent Alignment Dialogues: Scenarios & Dialogues PY - 2023/04/17 AU - Pei-Yu Chen UR - DO - 10.4121/b7a321df-640a-483d-8c32-a18fe21e7204.v2 KW - Human-agent alignment KW - Semantic user model KW - Alignment dialogue KW - Values KW - Behaviour support technology KW - Conversational agent N2 - <p>The data is the scenarios and dialogues used in the focus groups underlying the paper "Acquiring Semantic Knowledge for User Model Updates via Human-Agent Alignment Dialogues: An exploratory focus group study" by Pei-Yu Chen, Myrthe Tielman, Dirk Heylen, Catholijn Jonker, and Birna van Riemsdijk. The goal of this paper is to explore what potential users like or dislike about certain aspects of the dialogues and identify dimensions that are important for designing good alignment dialogues.</p><p><br></p><p><strong>Study</strong></p><p>We performed an exploratory focus group user study, in which we showed participants six scenarios with different variants of how we envision such alignment dialogues might look like. The scenarios and dialogues are in textual form. After they finished reading, we asked them to discuss and compare the dialogues, and then we moved on to the next scenario and discussion. The process continued until all six scenarios were discussed. For the structure of the discussion, we prepared the following questions to guide the participants:</p><ul><li>Which version of the dialogue do you prefer, or which part of which dialogue do you prefer? Why?</li><li>Is there a certain part of the dialogue that you particularly like/not like? Why? How would you want to do it instead?</li><li>Which dialogue is more ‘intelligent’, as in has more capability in providing support?</li><li>Do you feel one dialogue is more supportive than the other?</li><li>After which dialogue do you think the agent would be more ‘on the same page’ as you?</li></ul><p><br></p><p><strong>Data & analysis</strong></p><p>We transcribed the focus group sessions and analyzed the transcriptions using inductive thematic analysis with the addition of triangulation with literature.</p> ER -