RIACT: A Responsible AI System for Personalized Study Habit Tracking and Early Burnout Signal Detection in University Students

RIACT：一个用于大学生个性化学习习惯追踪与早期倦怠信号检测的负责任人工智能系统

Abstract: 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. 摘要： 学生倦怠在高等教育中非常普遍，据报道其发生率在 12% 到 70% 以上，且持续高于在职人群。然而，这种倦怠通常只有在学业表现已经下滑后，才能通过回顾性分析被识别出来。

A contributing factor is that students have little structured visibility into their own study behaviour, and existing productivity tools record activity without interpreting it. 导致这一问题的一个因素是，学生对自己学习行为的结构化认知不足，而现有的生产力工具往往只记录活动，却无法对其进行解读。

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. 本文介绍了 RIACT（记录、洞察、分析、辅导、追踪），这是一个基于 Web 的应用程序，它将结构化的学习时段记录与混合人工智能架构相结合，旨在呈现个性化的洞察和早期的倦怠信号。

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. 学生按地点和时间记录学习时段；系统通过扣除休息时间来计算净专注时长，通过基于周际行为对比的透明、确定性规则来检测倦怠信号，并利用受限于固定输出模式的大语言模型，对行为模式进行情境化分析并生成个性化建议。

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. 该设计自始至终贯彻了负责任人工智能的原则：预警由可审计的规则而非模型判断来控制，所有输出都被表述为观察结果而非诊断结论，且数据收集仅限于学生自述的行为字段。

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. 我们描述了该系统的设计原理，将其置于学生倦怠和教育领域可解释人工智能的文献背景中，并提出了一个评估框架，用于对照现有的倦怠评估工具来验证其行为信号的有效性。