AI System Memory and Innovation in Human-AI Collaboration

AI System Memory and Innovation in Human-AI Collaboration | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article AI System Memory and Innovation in Human-AI Collaboration Rasha Alahmad This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9629649/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract AI system memory, whether systems retain prior interactions or generate responses independently, is an underexplored factor influencing innovation in human-AI collaboration. This study examines the impact of AI system memory on innovative outcomes by comparing two AI agents: adaptive, memory-based and static, memoryless. The experimental setup included two conditions: a long sequence of 20 follow-up tasks and a short sequence of 5 tasks, which each AI agent completed. The study used cosine dissimilarity, Jaccard distance, and keyword entropy to measure innovation in AI responses. Results show that static, memoryless AI systems are more likely to generate innovative outcomes than memory-based systems, challenging the prevailing IS assumption that greater adaptation leads to more innovation. The results also show that task type moderates the relationship between AI system memory and innovative outcomes. Exploitative tasks generally yield more innovative outcomes than exploratory tasks. However, this pattern varies across metrics and memory architectures. Artificial Intelligence and Machine Learning Static AI systems Adaptive AI systems Innovation Memoryless systems Memory-based systems Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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