Paper Type
Complete
Paper Number
PACIS2026-1925
Description
The Internet of Things generates vast streams of temporal data, yet translating these streams into actionable knowledge remains a persistent design challenge for Information Systems research. Generative AI, particularly Large Language Models, introduces new possibilities for interpreting, mediating, and augmenting such data. However, the emerging literature at this intersection is fragmented across technical domains and lacks systematic consolidation from a sociotechnical design perspective. This paper presents a structured review of 35 recent studies combining Generative AI with IoT and adjacent time series contexts. A two-stage analytical strategy pairs AI-assisted topic modeling with interpretive concept coding to identify four recurring themes: temporal intelligence through hybrid architectures, agentic mediation between users and sensor infrastructures, explainability demands in anomaly detection, and generative augmentation beyond forecasting. The synthesis reveals strong model-centric experimentation alongside underdeveloped organizational, individual, and societal design dimensions. These imbalances are consolidated into a research agenda for future artifact-oriented IS inquiry.
Recommended Citation
Arz von Straussenburg, Arnold F. and Riehle, Dennis M., "When Models Meet Sensors – A Structured Review and IS Research Agenda for Generative AI-Enabled IoT Artifacts" (2026). PACIS 2026 Proceedings. 11.
https://aisel.aisnet.org/pacis2026/iot_smartcity/iot_smartcity/11
When Models Meet Sensors – A Structured Review and IS Research Agenda for Generative AI-Enabled IoT Artifacts
The Internet of Things generates vast streams of temporal data, yet translating these streams into actionable knowledge remains a persistent design challenge for Information Systems research. Generative AI, particularly Large Language Models, introduces new possibilities for interpreting, mediating, and augmenting such data. However, the emerging literature at this intersection is fragmented across technical domains and lacks systematic consolidation from a sociotechnical design perspective. This paper presents a structured review of 35 recent studies combining Generative AI with IoT and adjacent time series contexts. A two-stage analytical strategy pairs AI-assisted topic modeling with interpretive concept coding to identify four recurring themes: temporal intelligence through hybrid architectures, agentic mediation between users and sensor infrastructures, explainability demands in anomaly detection, and generative augmentation beyond forecasting. The synthesis reveals strong model-centric experimentation alongside underdeveloped organizational, individual, and societal design dimensions. These imbalances are consolidated into a research agenda for future artifact-oriented IS inquiry.
Comments
10-IoT