Low-code/no-code (LCNC) development platforms are reshaping the IT landscape by enabling individuals with minimal programming expertise to build applications. While LCNC has the potential to diversify the IT workforce, recruitment practices may still reflect gender biases that hinder inclusivity. This research investigates gendered language in LCNC job advertisements and compares them to traditional IT roles to assess whether LCNC offers a more inclusive entry point or perpetuates existing biases. A pre-study of 75 U.S.-based job postings revealed minimal explicit gender bias but a notable overrepresentation of masculine-coded language in qualification requirements, which may subtly discourage women from applying. Building on these findings, the planned study will leverage Natural Language Processing to analyze a larger dataset, systematically identifying linguistic patterns that shape hiring practices. By uncovering implicit biases in job postings, this research contributes to the broader discourse on equitable access to IT careers and LCNC’s role in fostering workforce diversity.