@inproceedings{gaudeau-etal-2026-incentives,
    title = "Incentives Of {E}d{T}ech: A Systematic Review Of {E}du{NLP} Research",
    author = "Gaudeau, Gabrielle  and
      O{'}Driscoll, Aoife  and
      Degraeuwe, Jasper  and
      Caines, Andrew  and
      Rooein, Donya  and
      Talat, Zeerak",
    editor = "Kochmar, Ekaterina  and
      Alhafni, Bashar  and
      Bann{\`o}, Stefano  and
      Bexte, Marie  and
      Burstein, Jill  and
      Horbach, Andrea  and
      Laarmann-Quante, Ronja  and
      Tack, Anais  and
      Yaneva, Victoria  and
      Yuan, Zheng",
    booktitle = "Proceedings of the 21st Workshop on Innovative Use of {NLP} for Building Educational Applications ({BEA} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.bea-1.50/",
    doi = "10.18653/v1/2026.bea-1.50",
    pages = "715--750",
    ISBN = "979-8-89176-409-5",
    abstract = "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."
}