人工智能与社会情感学习交叉研究中的政策缺失
The Policy Deficit in AI x Social-Emotional Learning Research
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中文总结 AI 辅助
本研究通过综述65篇AI与SEL交叉的论文，发现该领域存在显著政策缺失，提出将政策启示作为研究方法的转向，并为相关人员提供弥合AI创新与教育治理差距的可操作指南。
中文摘要 AI 辅助
随着人工智能（AI）日益融入社会情感学习（SEL）项目，基于证据的政策需求变得至关重要。我们系统综述了65篇探讨AI与SEL交叉领域的同行评审论文，以研究这些研究如何阐述政策启示。分析发现，当前AI×SEL文献中存在显著的“政策缺失”：近四分之三的研究根本未提及政策启示。我们采用“WH-问题”框架（谁、什么、为什么、何时/何地、如何），梳理文献中现有的政策启示叙事，发现它们往往缺乏有效循证决策所需的具体性和面向行动者的指导。我们还发现发表场所与政策参与度之间存在显著关联，表明当前学术激励结构可能优先考虑技术创新和教学可行性，而非明确参与治理与监管。本研究识别出一种“技术解决方案主义”陷阱，即过度强调技术潜力，却未充分明确负责任实施的制度条件。最后，我们提出应从“将启示作为事后补充”转向“将启示作为研究方法”，并为研究人员、编辑、审稿人和政策制定者提供一套可操作的指南，以弥合AI创新与教育治理之间的差距。我们主张，AI-SEL研究不应将政策视为泛泛的伦理边界，而应系统明确谁应采取行动、推荐哪些行动、为何需要这些行动、行动适用的时间和地点，以及行动的框架强度，从而加强AI×SEL创新向教育政策与实践的转化。
英文摘要
As artificial intelligence (AI) is increasingly integrated into social-emotional learning (SEL) initiatives, the need for evidence-based policy has become paramount. We systematically reviewed 65 peer-reviewed papers that examine the intersection of AI and SEL to investigate how these studies articulate policy implications. Our analysis revealed a substantial "policy deficit" in the current AI x SEL literature: nearly three-quarters of the studies did not mention policy implications at all. Using the "WH-question" framework (Who, What, Why, When/Where, and How), we map the policy implications narratives present in the literature and show that they often lack the specificity and actor-oriented guidance required for effective evidence-informed policymaking. We find a significant association between publication venue and policy engagement, suggesting that current academic incentive structures may prioritize technical innovation and pedagogical feasibility over explicit engagement with governance and regulation. This study identifies a "techno-solutionist" trap, where technical potential is foregrounded while the institutional conditions for responsible implementation remain under-specified. We conclude by proposing a shift from "implication-as-afterthought" to "implication-as-methodology" and offer a set of actionable guidelines for researchers, editors, reviewers, and policymakers to bridge the gap between AI innovation and educational governance. Rather than presenting policy as a generic ethical horizon, we argue that AI-SEL studies should systematically specify Who should act, What actions are recommended, Why these actions are needed, When and Where they apply, and How strongly they are framed, thereby strengthening the translation of AI x SEL innovation into educational policy and practice.
发表机构
- University of Copenhagen（哥本哈根大学）
- University of Szeged（塞格德大学）
- University of Social Sciences and Humanities, Vietnam National University, Ho Chi Minh City（胡志明市越南国家大学社会科学与人文大学）
- Vietnam National University, Ho Chi Minh City（胡志明市越南国家大学）
机构由 AI 辅助整理，请以论文原文为准。
