This paper presents Daleel 2026, the first shared task on argument mining in Arabic news editorials and competitive debates. Daleel includes two tasks: Task 1 identifies which of six argumentative discourse unit (ADU) categories are present in a paragraph, while Task 2 locates the units with their labels and exact text spans. The tasks are performed using a new corpus of 30 documents, 1,042 paragraphs, and about 76k words. Twenty-nine teams registered, 12 participated, and 10 submitted system papers. Both tasks have Closed and Open tracks. The best results for the Closed track were 0.7117 macro-F1 in Task 1 and 0.7547 modified overlap F1 in Task 2. The results show different insights: the best Closed-track score of each team was higher on debates than on editorials. A broad range of approaches was used in Task 1 submissions, including Arabic encoders, large language models, rules, and ensembles. Task 2 systems, in particular the top-ranked ones, were more similar and mainly used CRF or Viterbi decoding with combinations of models or training runs. The rare ADU categories remained difficult in both tasks, and identifying exact span boundaries was an additional challenge in Task 2.

Despite the growing importance of online discourse, Arabic-speaking communities lack platforms that support structured, culturally grounded debate. Mainstream social media rarely fosters constructive engagement, often leading to polarization and superficial exchanges. This paper proposes the development of a culturally aware debate platform tailored to the values and traditions of Arabic-speaking users, with a focus on leveraging advances in natural language processing (NLP). We present findings from a user survey that explores experiences with existing debate tools and expectations for future platforms. Besides, we analyze 30,000 English-language debate topics using large language models (LLMs) to assess their cultural relevance and appropriateness for Arab audiences. We further examine the ability of LLMs to generate new culturally resonant debate topics, contributing to the emerging tasks of culture-aware topic assessment and generation. Finally, we propose a theoretical and technical framework for building an NLP-supported Arabic debate platform. Our work highlights the urgent need for culturally sensitive NLP resources that foster critical thinking, digital literacy, and meaningful deliberation in Arabic.