Incentives Of EdTech: A Systematic Review Of EduNLP Research

2026-05-13

AI Summary

This study investigates whether stakeholder representation—such as teachers, students, and industry actors—is balanced in educational natural language processing (EduNLP) research and whether current scholarship aligns with the core needs of educational infrastructure. Through a systematic literature review of 204 papers, integrated with stakeholder analysis, task taxonomy, and an ethics evaluation framework, the work reveals for the first time that teachers, as critical end-users, are severely underrepresented: only 33.3% of studies include them, and merely 9.8% report actual deployment in teaching contexts. Furthermore, ethical commitments in the field often remain performative rather than substantive. Building on these findings, the study proposes concrete pathways toward responsible EduNLP practices that reconcile private-sector incentives with the public values of education.

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.

Problem

• EdTech
• stakeholder inclusion
• educational equity
• EduNLP
• research ethics

Innovation

• systematic literature review
• stakeholder analysis
• EduNLP
• educational equity
• responsible AI