面向编程教育的风险自适应与证据约束生成式人工智能反馈框架

中文摘要 AI 辅助

生成式人工智能可以将学习分析转化为个性化支持，但反馈系统必须决定何时干预、使用哪些证据以及提供多少帮助。我们针对入门编程课程开发了一个风险自适应、证据约束的框架，使用了来自215名学生的2993个失败提交状态。学生不相交模型预测了持续失败及相关结果；为136个案例生成了四种匹配的反馈条件；校准后的风险为容量受限的干预策略提供了依据。验证选定的逻辑回归模型在测试集上实现了精确率-召回率曲线下面积0.550和受试者工作特征曲线下面积0.681。更广泛的学生历史记录改善了对未修改重新提交的预测。经过标准化修复和证据门控后，544条新生成消息中有519条包含所有必需组件。固定阈值顺序策略选择了17.8%的合格测试状态，并捕获了25.2%的观察到的持续失败。这些发现支持一种证据门控的渐进式辅助策略：校准风险指导干预时机，记录证据约束反馈内容，辅助从自我检查逐步升级到局部提示（在适当情况下）。该框架将预测、决策制定和基于证据的生成连接起来，同时保持其评估结果的独立性。

英文摘要

Generative artificial intelligence can turn learning analytics into personalized support, but feedback systems must decide when to intervene, which evidence to use, and how much assistance to provide. We developed a risk-adaptive, evidence-constrained framework for introductory programming using 2993 failed-submission states from 215 students. Student-disjoint models predicted persistent failure and related outcomes; four matched feedback conditions were generated for 136 cases; and calibrated risk informed capacity-limited intervention policies. The validation-selected logistic regression model achieved a test precision-recall area under the curve of 0.550 and a receiver operating characteristic area under the curve of 0.681. Broader student histories improved prediction of unmodified resubmission. After standardized repair and evidence gating, 519 of 544 newly generated messages contained all required components. A fixed-threshold sequential policy selected 17.8% of eligible test states and captured 25.2% of observed persistent failures. These findings support an evidence-gated progressive assistance strategy: calibrated risk guides intervention timing, recorded evidence constrains feedback content, and assistance progresses from self-checks to localized hints when warranted. The framework connects prediction, decision-making, and grounded generation while keeping their evaluation outcomes distinct.

发表机构

• University College London（伦敦大学学院）

机构由 AI 辅助整理，请以论文原文为准。