Engaging learners in dialogue around controversial issues is essential for examining diverse values and perspectives in pluralistic societies. While prior research has identified productive discussion moves mainly in STEM-oriented contexts, less is known about what constitutes productive discussion in ethical and value-laden discussions. This study investigates productive discussion in AI ethics dilemmas using a dialogue-centric learning analytics approach. We analyze small-group discussions among undergraduate students through a hybrid method that integrates expert-informed coding with data-driven topic modeling. This process identifies 14 discussion moves across five categories, including Elaborating Ideas, Position Taking, Reasoning & Justifications, Emotional Expression, and Discussion Management. We then examine how these moves relate to discussion quality and analyze sequential interaction patterns using Ordered Network Analysis. Results indicate that emotive and experiential arguments and explicit acknowledgment of ambiguity are strong positive predictors of discussion quality, whereas building on ideas is negatively associated. Ordered Network Analysis further reveals that productive discussions are characterized by interactional patterns that connect emotional expressions to evidence-based reasoning. These findings suggest that productive ethical discussion is grounded not only in reasoning and justification but also in the constructive integration of emotional expression.

翻译：引导学习者围绕争议性议题展开对话，对于审视多元社会中的多样化价值观与视角至关重要。尽管先前研究主要在STEM导向的语境中识别了有效的讨论策略，但对于伦理及价值负载型讨论中何谓有效讨论仍知之甚少。本研究采用以对话为中心的学习分析方法，探讨了在人工智能伦理困境中的有效讨论。我们通过一种混合方法分析了本科生的小组讨论，该方法融合了专家指导的编码与数据驱动的主题建模。此过程识别出跨越五个类别的14种讨论策略，包括观点阐述、立场表达、推理与论证、情感表达以及讨论管理。随后，我们考察了这些策略与讨论质量的关系，并利用有序网络分析（Ordered Network Analysis）分析了序列交互模式。结果表明，情感与经验性论证以及对模糊性的明确承认是讨论质量的强正向预测因子，而基于他人观点构建则呈负相关。有序网络分析进一步揭示，有效讨论的交互模式特征在于将情感表达与基于证据的推理相连接。这些发现表明，有效的伦理讨论不仅植根于推理与论证，还依赖于情感表达的建构性整合。