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Proceedings of the 21st Conference on Computer Science and Intelligence Systems (FedCSIS)

Annals of Computer Science and Information Systems, Volume 47

Human–AI Collaboration in Maturity Model Development: Evidence from Big Data Analytics Assessment Criteria

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DOI: http://dx.doi.org/10.15439/2026F5552

Citation: Marija Ðukić, ,

Full text

Abstract. The development of maturity models is a knowledgeintensive process requiring literature synthesis, domain knowledge and practical validation. In this paper, we evaluate assessment criteria for a Big Data Analytics maturity model (BDAMM) and investigate the potential of generative artificial intelligence (GenAI) as a cognitive amplifier in this context. The criteria proposed in previous work were independently evaluated by domain experts and GenAI tools. The results show agreement between human and GenAI evaluations while highlighting complementary strengths. Human experts primarily contributed to practical relevance, while GenAI proved effective in knowledge synthesis. The resulting criteria hierarchy establishes the basis for an assessment instrument based on Logic Scoring of Preference supporting explainable BDA maturity assessment. The findings demonstrate that the effectiveness of GenAI depends on iterative human--AI collaboration, domain expertise, and continuous critical evaluation. With this research, we contribute assessment criteria for BDAMM development and provide empirical evidence on the role of GenAI as a cognitive amplifier in knowledgeintensive research.

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