This study investigates the use of ChatGPT for the development of inductive (data-driven) codebooks from qualitative datasets in underrepresented languages (Azerbaijani and Finnish). Although prior work has leveraged GPT- 4o as a “co-researcher” that can support more efficient and comprehensive inductive codebook development, further work is needed to understand how consistent results are across languages and for translated text. The study found GPT- 4o to be useful for identifying relevant codes, but also found limitations, particularly in terms of the quality of example sentences generated for less-resourced languages. Social moderation by humans and construct evaluations were applied to refine the generated codebooks to ensure clarity and reduce redundancy. The results demonstrated that, while GPT-4o significantly aids in multilingual qualitative analysis, human intervention remains essential to validate and enhance the accuracy of the outputs. This research is particularly significant for the learning analytics field as it demonstrates scalable methods for multilingual qualitative analysis, a critical step in expanding the inclusivity and applicability of learning analytics across educational contexts.