We investigate the role of large language models (LLMs) in promoting gender-inclusive language by evaluating their ability to rewrite biased text and generate counterfactual narratives across multiple languages. We introduce a shared task with two subtasks: gender-inclusive rewriting and counterfactual generation. The task covers five languages English, German, Spanish, Tamil, and Kannada reflecting diverse grammatical gender systems and sociocultural contexts. We release curated word-level and sentence-level datasets to support controlled inclusive generation. A total of 50 teams registered for the shared task, and around 8 teams submitted results. Submissions are evaluated using a hybrid framework combining rubric-based automatic scoring with expert human judgment. Finally, we provide an overview of participating systems and discuss key findings and challenges observed across languages.