{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T17:05:03Z","timestamp":1776099903676,"version":"3.50.1"},"reference-count":73,"publisher":"Association for Computing Machinery (ACM)","issue":"1","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGIR Forum"],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:p>\n            Generative AI-based conversational systems are rapidly becoming the default gateway to information, yet their educational potential and risks remain under-examined. This paper introduces\n            <jats:italic toggle=\"yes\">Chat as Learning<\/jats:italic>\n            (CaL), a paradigm that views dialogue not as a veneer over search but as the central mechanism for knowledge construction. We present a three-layer framework that couples fine-grained learner modeling with adaptive prompting support and a retrieval-augmented generator that blends the model's prior knowledge with verified, up-to-date sources. Each conversational turn functions as a diagnostic probe, which enables real-time refinement of intent, mastery level, and motivational state. Building on this architecture, we map a cross-disciplinary research agenda that spans user models, reinforcement-optimized prompt policies, information-quality and knowledge gap signaling, multimodal assessment instruments, and bias mitigation. By foregrounding both the promise of democratized expertise and the threat of over-reliance on fluent yet fallible outputs, CaL establishes an urgent agenda for reproducible benchmarks and responsible deployment of conversational learning systems.\n          <\/jats:p>","DOI":"10.1145\/3769733.3769735","type":"journal-article","created":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:04:53Z","timestamp":1760112293000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Chat as Learning in Interactive Information Retrieval and Generation"],"prefix":"10.1145","volume":"59","author":[{"given":"Ran","family":"Yu","sequence":"first","affiliation":[{"name":"GESIS - Leibniz Institute for the Social Sciences, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiqun","family":"Liu","sequence":"additional","affiliation":[{"name":"The University of Oklahoma, OK, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,10,10]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Abridged Edition. 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