分析204篇教育NLP论文，发现研究多为商业利益服务，教师被忽视，落地难。
Incentives Of EdTech: A Systematic Review Of EduNLP Research
- 系统梳理204篇教育NLP论文，对比ACL语料库验证结果
- 教师仅33.3%被列为受益者，真实部署率不足10%
- 呼吁建立更负责任的研究范式，关注教育本质需求
尽管自然语言处理领域投入大量资源发展教育技术（EdTech），但教育各利益相关方中谁真正受益仍不明确。本文对2024至2025年计算语言学协会教育应用特别兴趣组（ACL SIGEDU）发表的204篇论文进行系统综述，并与更广泛的ACL语料库中的EdTech论文进行比对。通过分析利益相关方参与度和研究任务优先级，发现存在私有企业激励与教育基础设施根本需求之间的关键张力：教师作为最受影响群体，其作为受益者的代表性严重不足（仅33.3%），真实世界部署极为罕见（9.8%），伦理考量多停留于声明而非实际行动。基于文集中的典范研究，本文提出更具责任感的EduNLP研究实践建议。
原文摘要 · Abstract (English)
While the Natural Language Processing community has dedicated significant resources in developing educational technologies (EdTech) that support this shift, it remains unclear whose interests are being best served among the stakeholders of education. In this paper, we present a systematic literature review of 204 papers published in venues of the Association for Computational Linguistics' Special Interest Group on Building Educational Applications in 2024 and 2025, and validate these against EdTech papers from the wider ACL Anthology. By examining stakeholder inclusion and the prioritisation of research tasks, our findings reveal a critical tension: a push and pull between private-sector incentives and the foundational needs of educational infrastructure. Our analysis reveals that teachers are systematically under-represented as beneficiaries of research (33.3%) despite being the most affected, that real-world deployment remains rare (9.8%), and that ethical engagement tends toward acknowledgement rather than action. Drawing on exemplary papers in our corpus, we offer concrete recommendations for more responsible EduNLP research practices.
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