Paper Number

ECIS2025-1489

Paper Type

SP

Abstract

This study explores the integration of generative artificial intelligence (gAI) into design science research (DSR), focusing on conceptualizing and generating design principles. Recent developments in gAI have positioned these technologies as powerful collaborative tools for creating and adapting design components and enhancing efficiency and creativity in information systems (IS) research. Our research-in-progress study examines the reusability of human- and AI-generated design principles under the theoretical lens of bounded rationality. Through a survey-based comparative analysis, preliminary findings indicate that human-generated and AI-generated design principles generally achieve high levels of accessibility, importance, novelty, explanatory power, and conciseness. However, subtle differences in actionability, effectiveness, and practical applicability suggest distinct nuances in their conceptual constructions. Our study contributes to the understanding of the interplay between human cognitive processes and gAI capabilities. It highlights implications for integrating gAI into the conceptual phase of knowledge generation in IS and design research.

Author Connect URL

https://authorconnect.aisnet.org/conferences/ECIS2025/papers/ECIS2025-1489

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Jun 18th, 12:00 AM

Should A(I) Design? - Toward a Comparative Reusability Analysis of Design Principles Conceptualized by Generative AI and Humans

This study explores the integration of generative artificial intelligence (gAI) into design science research (DSR), focusing on conceptualizing and generating design principles. Recent developments in gAI have positioned these technologies as powerful collaborative tools for creating and adapting design components and enhancing efficiency and creativity in information systems (IS) research. Our research-in-progress study examines the reusability of human- and AI-generated design principles under the theoretical lens of bounded rationality. Through a survey-based comparative analysis, preliminary findings indicate that human-generated and AI-generated design principles generally achieve high levels of accessibility, importance, novelty, explanatory power, and conciseness. However, subtle differences in actionability, effectiveness, and practical applicability suggest distinct nuances in their conceptual constructions. Our study contributes to the understanding of the interplay between human cognitive processes and gAI capabilities. It highlights implications for integrating gAI into the conceptual phase of knowledge generation in IS and design research.

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