AI 中文总结
该研究针对高等教育场景开展AI生成互动小说的试点用户评估，发现其叙事清晰度和长度接受度较好但连贯性较弱，据此得出相关设计启示以指导后续研究。
AI 中文摘要
生成式人工智能（AI）能够大规模生成教育内容，包括互动式和叙事性学习体验，但仅靠技术生成是不够的：存在困惑、叙事不一致或缺乏吸引力的场景在实践中不太可能有用。本文针对用于高等教育的AI生成互动小说（IF）开展了以用户为中心的试点评估。我们使用先前描述的领域无关流程和共享STEM内容库，生成了一组受控场景，并邀请22名高等教育STEM领域的参与者玩一个生成的片段，对其叙事清晰度、故事内容连贯性、参与度和长度接受度进行评分。我们还通过自由文本提示收集了开放式反馈。结果显示，叙事清晰度和长度接受度的评分呈积极态势，参与度接近量表的中立中点，而故事内容连贯性是明显最弱的维度。定性反馈指出，测验整合是瓶颈，存在小说内测验提示的动机不足、场景突然变化的问题，还提到答错后缺少故事层面的后果。基于这些观察，我们得出了可用于后续更大规模研究（包括后续学习效果研究）的具体设计启示。
英文摘要
Generative artificial intelligence (AI) can produce educational content at scale, including interactive and narrative learning experiences, but technical generation alone is not sufficient: scenarios that are confusing, narratively inconsistent, or unengaging are unlikely to be useful in practice. This paper presents a pilot user-centred evaluation of AI-generated interactive fiction (IF) for educational use in higher education. Using a previously described domain-agnostic pipeline and a shared STEM content base, we generated a controlled pool of scenarios and asked participants (N = 22, STEM higher-education) to play one generated episode and rate it on narrative clarity, story-content coherence, engagement, and length acceptance. A free-text prompt captured open feedback. Narrative clarity and length acceptance were rated positively, engagement sat near the neutral mid-point of the scale, and story-content coherence was the weakest dimension by a clear margin. Qualitative feedback points to quiz integration as the bottleneck. Artificial in-fiction motivation for quiz prompts and abrupt setting changes were reported. Feedback also pointed to missing story-level consequences for wrong answers. From these observations, we derive concrete design implications that can inform larger follow-up studies, including later work on learning effectiveness.
Comments8 pages, 1 figure, 2 tables. Published in the EDULEARN26 proceedings
Journal refEDULEARN26 Proceedings, Article 1075 (2026)
DOI：10.21125/edulearn.2026.1075
