AI 中文总结
该研究借鉴多领域技术提出精准教育范式，以学生数字孪生为核心，旨在推动高等教育从被动转向预防性，助力学生学业成功与职业适配，同时明确从预测转向因果干预的核心挑战。
AI 中文摘要
高等教育在学业成功方面的方法仍以被动反应为主。高校往往仅在学生挂科、学业进度落后、负债过高或未获得学位就离校后才发现学业问题。数十年前，医疗保健领域面临类似挑战，其应对方式是转向由预测模型、风险分层、电子健康记录及人工智能（AI）驱动的预防性医疗。本文认为，高等教育正处于类似的转折点。基于学习分析、教育数据挖掘、机器学习、劳动力分析及数字孪生技术的进展，我们提出了一种称为精准教育的范式。在该框架下，AI持续分析学生的学业、行为、财务及职业数据，以识别新兴风险、推荐个性化干预措施、优化学术路径并使学术决策与长期职业成功相适配。该模型的核心是学生数字孪生（Student Digital Twin），这是一种持续更新的学习者表征，可模拟多种教育未来及干预场景。我们以普渡大学的Course Signals项目和佐治亚州立大学的GPS Advising项目等早期部署的证据为基础支撑该愿景。我们还认为，仅靠预测是不够的，核心方法论挑战在于从预测转向可执行的因果干预。本文提出了一个概念框架，研究了支撑技术，综述了实证记录及其局限性，分析了伦理与治理影响，并勾勒了未来十年AI赋能高等教育的研究议程。
英文摘要
Higher education remains largely reactive in its approach to student success. Institutions frequently identify academic problems only after students have failed courses, fallen behind in degree progression, accumulated excessive debt, or departed without a credential. Healthcare faced a similar challenge decades ago. It responded by shifting from reactive treatment to preventive care powered by predictive models, risk stratification, electronic health records, and artificial intelligence (AI). This paper argues that higher education stands at an analogous inflection point. Drawing on advances in learning analytics, educational data mining, machine learning, workforce analytics, and digital twin technologies, we propose a paradigm we call Precision Education. Under this framework, AI continuously analyzes academic, behavioral, financial, and career data to identify emerging risks, recommend personalized interventions, optimize educational pathways, and align academic decisions with long-term career success. Central to the model is the Student Digital Twin, a continuously updated representation of a learner that can simulate multiple educational futures and intervention scenarios. We ground the vision in evidence from early deployments such as Course Signals at Purdue and GPS Advising at Georgia State University. We also argue that prediction alone is insufficient. The central methodological challenge is the move from prediction to causal, actionable intervention. The paper presents a conceptual framework, examines enabling technologies, reviews the empirical record and its limits, analyzes ethical and governance implications, and outlines a research agenda for the next decade of AI-enabled higher education.
