# Distributed agency in second language learning and teaching through generative AI

arXiv: https://arxiv.org/abs/2403.20216 · HTML: https://mlsanity.com/paper/2403.20216/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 135 references that are in the map.
- First posted on arXiv 29 Mar 2024.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Evaluating Large Language Models in Theory of Mind Tasks](https://mlsanity.com/paper/2302.02083/) (2023)
- [GPT detectors are biased against non-native English writers](https://mlsanity.com/paper/2304.02819/) (2023)
- [Levels of AGI for Operationalizing Progress on the Path to AGI](https://mlsanity.com/paper/2311.02462/) (2023)
- [Assessing Multilingual Fairness in Pre-trained Multimodal Representations](https://mlsanity.com/paper/2106.06683/) (2021)
- [Fairness in Language Models Beyond English: Gaps and Challenges](https://mlsanity.com/paper/2302.12578/) (2023)
- [Exploring AI-Generated Text in Student Writing: How Does AI Help?](https://mlsanity.com/paper/2304.02478/) (2023)
- [Emergent AI-Assisted Discourse: Case Study of a Second Language Writer Authoring with ChatGPT](https://mlsanity.com/paper/2310.10903/) (2023)

## Built on by (most cited first, 4 of 4)

- [LLMs and Childhood Safety: Identifying Risks and Proposing a Protection Framework for Safe Child-LLM Interaction](https://mlsanity.com/paper/2502.11242/) (2025)
- [Safe-Child-LLM: A Developmental Benchmark for Evaluating LLM Safety in Child-LLM Interactions](https://mlsanity.com/paper/2506.13510/) (2025)
- [From Test-taking to Cognitive Scaffolding: A Pedagogical Diagnostic Benchmark for LLMs on English Standardized Tests](https://mlsanity.com/paper/2505.17056/) (2025)
- [Analyzing Information-Seeking Behaviors in a Hakka AI Chatbot: A Cognitive-Pragmatic Study](https://mlsanity.com/paper/2509.11591/) (2025)

## Abstract (arXiv)

Generative AI offers significant opportunities for language learning. Tools like ChatGPT can provide informal second language practice through chats in written or voice forms, with the learner specifying through prompts conversational parameters such as proficiency level, language register, and discussion topics. AI can be instructed to give corrective feedback, create practice exercises, or develop an extended study plan. Instructors can use AI to build learning and assessment materials in a variety of media. AI is likely to make immersive technologies more powerful and versatile, moving away from scripted interactions. For both learners and teachers, it is important to understand the limitations of AI systems that arise from their purely statistical model of human language, which limits their ability to deal with nuanced social and cultural aspects of language use. Additionally, there are ethical concerns over how AI systems are created as well as practical constraints in their use, especially for less privileged populations. The power and versatility of AI tools are likely to turn them into valuable and constant companions in many peoples lives (akin to smartphones), creating a close connection that goes beyond simple tool use. Ecological theories such as sociomaterialism are helpful in examining the shared agency that develops through close user-AI interactions, as are the perspectives on human-object relations from Indigenous cultures.