Language learners typically exhibit first language (L1) influence in their written second language (L2) production. We investigate whether similar patterns emerge in L2 language models (L2LMs), which are typically assessed on task-based benchmarks rather than on language use. We evaluate the use of Native Language Identification (NLI) as a method for detecting whether L2LMs exhibit human-like L1 influence. Using existing learner corpora and our novel L2 English dataset, we identify the conditions that yield the highest NLI accuracy, and show that text length but not proficiency affects performance. We then apply NLI to L2LM-generated text under various instruction-tuning and prompting conditions. We find that instruction tuning on human learner essays yields high NLI accuracy (~90%) and is necessary for detectable L1 influence. Whilst NLI accuracy is similar for L2LM and human essays, human evaluation shows that LM-generated L1 influence remains distinguishable from human writing.

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.