{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:39:18Z","timestamp":1699576758869},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI-SS"],"abstract":"<jats:p>In this position paper we advocate a Reciprocal Human Machine Learning paradigm based on two theories of human-human learning behavior. Drawing from J\u00f6rg's theory of reciprocal learning in dyads and the Jewish tradition of Havruta - pair-based study, we suggest that human-machine collaboration based on these established human-human collaborative forms can achieve a rich and robust human-in-the-learning-loop (HITLL) framework in which both parties experience learning over time.<\/jats:p>","DOI":"10.1609\/aaaiss.v1i1.27483","type":"journal-article","created":{"date-parts":[[2023,11,9]],"date-time":"2023-11-09T21:06:19Z","timestamp":1699563979000},"page":"94-97","source":"Crossref","is-referenced-by-count":0,"title":["Reciprocal Human Machine Learning (RHML): Human-AI Collaboration based on theories of dyadic learning"],"prefix":"10.1609","volume":"1","author":[{"given":"David","family":"Schwartz","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dov","family":"Te'Eni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Inbal","family":"Yahav","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"9382","published-online":{"date-parts":[[2023,10,3]]},"container-title":["Proceedings of the AAAI Symposium Series"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI-SS\/article\/download\/27483\/27256","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI-SS\/article\/download\/27483\/27256","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,9]],"date-time":"2023-11-09T21:06:19Z","timestamp":1699563979000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI-SS\/article\/view\/27483"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,3]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,10,3]]}},"URL":"https:\/\/doi.org\/10.1609\/aaaiss.v1i1.27483","relation":{},"ISSN":["2994-4317"],"issn-type":[{"value":"2994-4317","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,3]]}}}