Southern Cross University研究生成式人工智能环境下的学习评估

Southern Cross University的研究跟踪了36名工程学和职前教师教育学生在二年级物理课程三项评估中使用GenAI的情况。

What did the study examine? Researchers tracked how 36 engineering and pre-service teaching students used GenAI across three assessments in a second-year physics unit.

学生逐渐使用GenAI检查作业、寻求反馈、澄清概念并检视自己的推理；研究未发现报告使用GenAI与未使用者之间的结果存在统计学显著差异。

How were students using AI? Students increasingly used GenAI to check their work, seek feedback, clarify concepts and question their reasoning, rather than simply complete assessment tasks. Did using AI improve student marks? The study found no statistically significant difference in outcomes between students who reported using GenAI and those who did not.

研究指出，物理课程研究样本较小，且依据学生自我报告的人工智能使用情况；研究人员认为还需要开展覆盖不同学科的更大规模研究。

The physics study was small and based on students' self-reported AI use, and the researchers say larger studies across different disciplines are needed.

Southern Cross University的评估使用清晰说明规定人工智能的可用方式；Assessment Adaptation Model – GenAI用于帮助教师判断现有任务在生成式人工智能可用时能否继续提供有意义的学习证据。

Assessments now use clear descriptors to establish how AI may be used, while the University's Assessment Adaptation Model – GenAI helps academics examine whether an existing task still provides meaningful evidence of learning when generative AI is available.

文中介绍的多模态反馈做法结合个性化视频评语、评估量规和人工智能生成的书面摘要；教师负责评分与原始反馈，所有人工智能生成的摘要在返回学生前均经过审阅。

In a separate practice report, Dr Quince trialled a multimodal feedback approach combining personalised video commentary, assessment rubrics and AI-generated written summaries. The academic still determined the grade and provided the original feedback. GenAI was used only to help convert that feedback into an additional concise format, with every AI-generated summary reviewed before it was returned to the student.