While Systematic Literature Reviews (SLRs) are integral to research by synthesizing existing knowledge and guiding future inquiry, the exponential increase in academic publications presents significant challenges to traditional, manual review methods, notably regarding scalability, efficiency, and researcher workload. Recent advancements in Artificial Intelligence (AI), particularly Large Language Models (LLMs), offer promising avenues for augmenting the SLR process. Nonetheless, integrating AI into literature reviews introduces methodological complexities, including maintaining accuracy, minimizing biases, and preserving scholarly rigor. To address these challenges, this paper introduces a structured AI-augmented SLR framework, systematically integrating AI capabilities into Wolfswinkel et al.’s [37] established Grounded Theory Literature Review Method. Our framework incorporates AI-driven relevance assessments, automated selection processes, and thematic content analysis, underpinned by rigorous human oversight to ensure reliability and interpretative validity. We empirically illustrate and evaluate our framework through a comparative study, replicating and extending a previously published human SLR. The evaluation assesses AI performance using key metrics such as type I and type II error rates across varying confidence thresholds. Results demonstrate substantial efficiency gains and effective accuracy in AI-assisted selection, highlighting the importance of carefully calibrated thresholds and continued human oversight. Our study contributes practical guidelines for effectively balancing AI automation with human scholarly judgment, offering a replicable methodological approach for researchers seeking to leverage AI capabilities without compromising methodological quality or academic integrity.
In: Braun, T., Paaßen, B., Stolzenburg, F. (eds.): KI 2025: Advances in Artificial Intelligence, Lecture Notes in Computer Science, Vol. 15956, pp. 3-17, Springer,
2025
