Distributed agency in second language learning and teaching through generative AI
- URL: http://arxiv.org/abs/2403.20216v4
- Date: Fri, 31 May 2024 14:17:17 GMT
- Title: Distributed agency in second language learning and teaching through generative AI
- Authors: Robert Godwin-Jones,
- Abstract summary: ChatGPT can provide informal second language practice through chats in written or voice forms.
Instructors can use AI to build learning and assessment materials in a variety of media.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- Generative AI, Pragmatics, and Authenticity in Second Language Learning [0.0]
There are obvious benefits to integrating generative AI (artificial intelligence) into language learning and teaching.
However, due to how AI systems under-stand human language, they lack the lived experience to be able to use language with the same social awareness as humans.
There are built-in linguistic and cultural biases based on their training data which is mostly in English and predominantly from Western sources.
arXiv Detail & Related papers (2024-10-18T11:58:03Z) - Policy Learning with a Language Bottleneck [65.99843627646018]
Policy Learning with a Language Bottleneck (PLLBB) is a framework enabling AI agents to generate linguistic rules.
PLLBB alternates between a rule generation step guided by language models, and an update step where agents learn new policies guided by rules.
In a two-player communication game, a maze solving task, and two image reconstruction tasks, we show thatPLLBB agents are not only able to learn more interpretable and generalizable behaviors, but can also share the learned rules with human users.
arXiv Detail & Related papers (2024-05-07T08:40:21Z) - Untangling Critical Interaction with AI in Students Written Assessment [2.8078480738404]
Key challenge exists in ensuring that humans are equipped with the required critical thinking and AI literacy skills.
This paper provides a first step toward conceptualizing the notion of critical learner interaction with AI.
Using both theoretical models and empirical data, our preliminary findings suggest a general lack of Deep interaction with AI during the writing process.
arXiv Detail & Related papers (2024-04-10T12:12:50Z) - Critical Appraisal of Artificial Intelligence-Mediated Communication [0.0]
This book explores the advantages and disadvantages of AI-mediated communication in language education.
It argues that it is crucial for language teachers to engage in CALL teacher education and professional development to keep up with the ever-evolving technology landscape and improve their teaching effectiveness.
arXiv Detail & Related papers (2023-05-15T02:35:40Z) - HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging
Face [85.25054021362232]
Large language models (LLMs) have exhibited exceptional abilities in language understanding, generation, interaction, and reasoning.
LLMs could act as a controller to manage existing AI models to solve complicated AI tasks.
We present HuggingGPT, an LLM-powered agent that connects various AI models in machine learning communities.
arXiv Detail & Related papers (2023-03-30T17:48:28Z) - Build-a-Bot: Teaching Conversational AI Using a Transformer-Based Intent
Recognition and Question Answering Architecture [15.19996462016215]
This paper proposes an interface for students to learn the principles of artificial intelligence by using a natural language pipeline to train a customized model to answer questions based on their own school curriculums.
The pipeline teaches students data collection, data augmentation, intent recognition, and question answering by having them work through each of these processes while creating their AI agent.
arXiv Detail & Related papers (2022-12-14T22:57:44Z) - Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations.
It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
arXiv Detail & Related papers (2022-01-26T18:53:09Z) - Building Human-like Communicative Intelligence: A Grounded Perspective [1.0152838128195465]
After making astounding progress in language learning, AI systems seem to approach the ceiling that does not reflect important aspects of human communicative capacities.
This paper suggests that the dominant cognitively-inspired AI directions, based on nativist and symbolic paradigms, lack necessary substantiation and concreteness to guide progress in modern AI.
I propose a list of concrete, implementable components for building "grounded" linguistic intelligence.
arXiv Detail & Related papers (2022-01-02T01:43:24Z) - A User-Centred Framework for Explainable Artificial Intelligence in
Human-Robot Interaction [70.11080854486953]
We propose a user-centred framework for XAI that focuses on its social-interactive aspect.
The framework aims to provide a structure for interactive XAI solutions thought for non-expert users.
arXiv Detail & Related papers (2021-09-27T09:56:23Z) - Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society.
A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests.
This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
arXiv Detail & Related papers (2021-06-25T22:31:55Z) - Future Trends for Human-AI Collaboration: A Comprehensive Taxonomy of
AI/AGI Using Multiple Intelligences and Learning Styles [95.58955174499371]
We describe various aspects of multiple human intelligences and learning styles, which may impact on a variety of AI problem domains.
Future AI systems will be able not only to communicate with human users and each other, but also to efficiently exchange knowledge and wisdom.
arXiv Detail & Related papers (2020-08-07T21:00:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.