The Design and Evaluation of the Collaboration between Researchers and Generative AI for Systematic Literature Reviews
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Date
2025-01-07
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4682
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Abstract
Literature reviews are crucial in academic research to identify prior coverage. Conventionally, researchers navigate through repositories to select relevant papers and conduct analysis and synthesis, which is time-consuming. With the emergence of generative AI (GAI) technologies, researchers anticipate that GAI will reduce the cognitive load and time spent completing a literature review. However, a researcher's exact trajectory while cooperating with GAI is not fully exposed. This research aims to make the interactive process between a researcher and GAI technology transparent and specify the marginal benefits and limitations for a researcher by cooperating with GAI to complete the literature review tasks. The findings demonstrate GAI's effectiveness in analysis and synthesis while revealing researcher challenges in content validation and nuanced discourse with GAI. The insights obtained from the GAI collaborative literature review could contribute to the interaction design for GAI apps for collaborative literature review.
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AI Assistants and Generative AI for Knowledge Creation, Retention, and Use, design science., generative ai, human-centered ai cooperation, resilience research, systematic literature review
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10
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Proceedings of the 58th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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