{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T16:51:39Z","timestamp":1789750299901,"version":"4.0.1"},"reference-count":67,"publisher":"Association for Computing Machinery (ACM)","issue":"2","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Interact. Intell. Syst."],"published-print":{"date-parts":[[2026,6,30]]},"abstract":"<jats:p>\n                    Explainable AI (XAI) offers solutions to the challenges of predictability and interpretability in adaptive interfaces, particularly in Augmented Reality (AR) systems that dynamically adapt information based on situational contexts. While traditional XAI methods highlight contextual factors influencing adaptations, they often overlook the user\u2019s internal understanding, such as their expertise and contextual perceptions. This omission can result in explanations that feel redundant or obvious. We present XAIUI, a computational approach that generates tailored explanations by integrating the system\u2019s adaptation model with a Bayesian model of the user\u2019s internal representation. Two online studies evaluated XAIUI. In the first study (N = 77), participants ranked XAIUI \u2019s explanations as most preferred compared to four ablations (\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\chi^{2}(4)=62.28, {\\textrm{p}} &lt; 0.001\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    ). In the second study (N = 110), XAIUI \u2019s explanations were rated significantly less complex (\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\chi^{2}(4)=840.855, {\\textrm{p}} &lt; 0.001\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    ) than all ablations, except showing no explanation. Our results demonstrate XAIUI \u2019s ability to deliver user-centric, concise, and intuitive explanations, highlighting its potential to enhance AI-driven interfaces.\n                  <\/jats:p>","DOI":"10.1145\/3774657","type":"journal-article","created":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T10:57:14Z","timestamp":1762167434000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["XAIUI: User Belief-Driven Explainable AI for Context-Aware Adaptive Interfaces"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2536-0208","authenticated-orcid":false,"given":"Thomas","family":"Langerak","sequence":"first","affiliation":[{"name":"Department of Information and Communications Engineering, Aalto University, Espoo, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6174-2089","authenticated-orcid":false,"given":"Kashyap","family":"Todi","sequence":"additional","affiliation":[{"name":"Meta Reality Labs, Redmond, Washington, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0546-0466","authenticated-orcid":false,"given":"Ben","family":"Lafreniere","sequence":"additional","affiliation":[{"name":"Meta Reality Labs, Toronto, Ontario, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4342-7699","authenticated-orcid":false,"given":"Ruta","family":"Desai","sequence":"additional","affiliation":[{"name":"Meta Reality Labs, Redmond, Washington, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8646-5076","authenticated-orcid":false,"given":"Tanya","family":"Jonker","sequence":"additional","affiliation":[{"name":"Meta Reality Labs, Redmond, Washington, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,3,13]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"Stefan Palan and Christian Schitter. 2023. 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