Incentives Of EdTech: A Systematic Review Of EduNLP Research
For the NLP and EdTech research community, this paper identifies systemic biases in stakeholder inclusion and research priorities, calling for more responsible practices.
This systematic review of 204 EduNLP papers (2024-2025) reveals that teachers are underrepresented as beneficiaries (33.3%), real-world deployment is rare (9.8%), and ethical engagement is superficial, highlighting a tension between private-sector incentives and educational infrastructure needs.
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.