AI 中文总结
本研究提出负责任AI系统RIACT，结合结构化学习记录与混合AI架构，为大学生检测早期倦怠信号并生成个性化学习建议，嵌入可审计规则等负责任AI原则，还提出了倦怠信号评估框架。
AI 中文摘要
学生倦怠在高等教育中极为普遍，报告比例介于12%至70%以上，且始终超过在职人群的倦怠率——然而该问题通常仅在学业成绩下降后才被回溯识别。造成这一情况的一个因素是学生对自身学习行为缺乏结构化的认知，而现有的生产力工具仅记录活动却不进行解读。本文提出RIACT（即Record记录、Insight洞察、Analyze分析、Coach指导、Track追踪），这是一款基于网络的应用程序，它将结构化学习会话记录与混合AI架构相结合，以呈现个性化洞察与早期倦怠信号。学生按地点和时间记录学习会话；系统通过扣除休息时间计算净专注时长，利用透明的确定性规则（基于逐周行为对比）检测倦怠信号，并使用受固定输出模式约束的大语言模型对模式进行情境化分析，生成个性化建议。该设计全程嵌入负责任AI原则：警告由可审计规则而非模型判断控制，所有输出均被表述为观察结果而非诊断，数据收集仅限于自记录的行为字段。我们阐述了该系统的设计原理，将其置于学生倦怠与教育领域可解释AI的文献背景中，并提出了一个评估框架，用于对照已建立的倦怠工具验证其行为信号。
英文摘要
Student burnout is highly prevalent in higher education, with reported rates ranging from 12% to over 70% and consistently exceeding those of the working population - yet it is typically identified only retrospectively, after academic decline has already occurred. A contributing factor is that students have little structured visibility into their own study behaviour, and existing productivity tools record activity without interpreting it. This paper presents RIACT (Record, Insight, Analyze, Coach, Track), a web-based application that combines structured study session logging with a hybrid AI architecture to surface personalized insights and early burnout signals. Students log sessions by location and time; the system computes net focus time by accounting for breaks, detects burnout signals through transparent, deterministic rules operating on week-over-week behavioural comparisons, and uses a large language model - constrained to a fixed output schema - to contextualize patterns and generate personalized recommendations. The design embeds responsible AI principles throughout: warnings are governed by auditable rules rather than model judgement, all output is framed as an observation rather than a diagnosis and data collection is limited to self-logged behavioural fields. We describe the system's design rationale, situate it within the literature on student burnout and explainable AI in education and propose an evaluation framework for validating its behavioural signals against established burnout instruments.
Comments11 pages. Also available on EdArXiv: https://doi.org/10.35542/osf.io/94hr5_v1
