Paper Number
ECIS2026-1491
Paper Type
CRP
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.
Recommended Citation
Alahmad, Rasha, "AI System Memory and Innovation In Human-AI Collaboration" (2026). ECIS 2026 Proceedings. 5.
https://aisel.aisnet.org/ecis2026/is_adopt/is_adopt/5
AI System Memory and Innovation In Human-AI Collaboration
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.
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