arXiv:2606.13691v1 Announce Type: cross 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 examin
The proliferation of educational technologies leveraging natural language processing necessitates a review of their underlying incentive structures and stakeholder benefits.
Understanding the incentives within EdTech is crucial for ensuring equitable and effective educational outcomes, and for guiding future AI development in this sector.
This review provides a structured assessment of who benefits most from current AI-driven EdTech, potentially guiding policy and development toward more inclusive solutions.
- · Students (if incentives align)
- · Educators
- · NLP researchers focused on ethics
- · EdTech platforms with transparent models
- · Students (if incentives misalign)
- · EdTech platforms with opaque models
- · Traditional educational institutions
Identification of biases and misaligned incentives in current EdTech applications.
Increased pressure on EdTech developers and policymakers to align incentives with broader educational goals.
Potential for new EdTech paradigms that prioritize student and educator benefits more explicitly.
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