The exponential growth of scholarly publications in recent years has presented a daunting challenge for researchers to keep track of relevant articles within their research field. To address this issue, we examined the capabilities of InfraNodus, an AI-Powered text network analysis platform. InfraNodus promises to provide insights into any discourse, uncover blind spots, and enhance a scholar's perspective by representing text as a network graph with relevant topical clusters and their relations. To understand the tools' effectiveness in analyzing scholarly articles, we used a set of 15 abstracts and 15 full papers. Our findings revealed that InfraNodus could indeed create topical clusters and meaningful patterns from abstracts, but its generated questions and summaries lacked relevance and coherence with the content. A deeper understanding of how the AI operates within the tool would benefit researchers seeking to optimize their literature review processes. [For the full proceedings, see ED636095.]