# Teach Me How to Improve My Argumentation Skills: A Survey on Feedback in Argumentation

arXiv: https://arxiv.org/abs/2307.15341 · HTML: https://mlsanity.com/paper/2307.15341/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 85 references that are in the map.
- First posted on arXiv 28 Jul 2023.

## Builds on (most cited first, 26 of 26)

- [CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities](https://mlsanity.com/paper/2201.06796/) (2022)
- [A Survey of the State of Explainable AI for Natural Language Processing](https://mlsanity.com/paper/2010.00711/) (2020)
- [Argumentative XAI: A Survey](https://mlsanity.com/paper/2105.11266/) (2021)
- [ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning](https://mlsanity.com/paper/2104.07644/) (2021)
- [The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants](https://mlsanity.com/paper/1708.01425/) (2017)
- [VISAR: A Human-AI Argumentative Writing Assistant with Visual Programming and Rapid Draft Prototyping](https://mlsanity.com/paper/2304.07810/) (2023)
- [Automatic Generation of Socratic Subquestions for Teaching Math Word Problems](https://mlsanity.com/paper/2211.12835/) (2022)
- [Explainable Artificial Intelligence Approaches: A Survey](https://mlsanity.com/paper/2101.09429/) (2021)
- [One Chatbot Per Person: Creating Personalized Chatbots based on Implicit User Profiles](https://mlsanity.com/paper/2108.09355/) (2021)
- [Argotario: Computational Argumentation Meets Serious Games](https://mlsanity.com/paper/1707.06002/) (2017)
- [Pchatbot: A Large-Scale Dataset for Personalized Chatbot](https://mlsanity.com/paper/2009.13284/) (2020)
- [Logical Fallacy Detection](https://mlsanity.com/paper/2202.13758/) (2022)
- [Argument Undermining: Counter-Argument Generation by Attacking Weak Premises](https://mlsanity.com/paper/2105.11752/) (2021)
- [Socratic Pretraining: Question-Driven Pretraining for Controllable Summarization](https://mlsanity.com/paper/2212.10449/) (2022)
- [A survey on improving NLP models with human explanations](https://mlsanity.com/paper/2204.08892/) (2022)
- [Effective Interfaces for Student-Driven Revision Sessions for Argumentative Writing](https://mlsanity.com/paper/2107.07018/) (2021)
- [Strategize Before Teaching: A Conversational Tutoring System with Pedagogy Self-Distillation](https://mlsanity.com/paper/2302.13496/) (2023)
- [Case-Based Reasoning with Language Models for Classification of Logical Fallacies](https://mlsanity.com/paper/2301.11879/) (2023)
- [Persua: A Visual Interactive System to Enhance the Persuasiveness of Arguments in Online Discussion](https://mlsanity.com/paper/2204.07741/) (2022)
- [Knowledge-Enhanced Evidence Retrieval for Counterargument Generation](https://mlsanity.com/paper/2109.09057/) (2021)
- ["Mama Always Had a Way of Explaining Things So I Could Understand'': A Dialogue Corpus for Learning to Construct Explanations](https://mlsanity.com/paper/2209.02508/) (2022)
- [Discussion Tracker: Supporting Teacher Learning about Students' Collaborative Argumentation in High School Classrooms](https://mlsanity.com/paper/2102.10293/) (2021)
- [TYPIC: A Corpus of Template-Based Diagnostic Comments on Argumentation](https://mlsanity.com/paper/2201.06674/) (2022)
- [Automated Evaluation for Student Argumentative Writing: A Survey](https://mlsanity.com/paper/2205.04083/) (2022)
- [LPAttack: A Feasible Annotation Scheme for Capturing Logic Pattern of Attacks in Arguments](https://mlsanity.com/paper/2204.01512/) (2022)
- [Extracting Implicitly Asserted Propositions in Argumentation](https://mlsanity.com/paper/2010.02654/) (2020)

## Abstract (arXiv)

The use of argumentation in education has been shown to improve critical thinking skills for end-users such as students, and computational models for argumentation have been developed to assist in this process. Although these models are useful for evaluating the quality of an argument, they oftentimes cannot explain why a particular argument is considered poor or not, which makes it difficult to provide constructive feedback to users to strengthen their critical thinking skills. In this survey, we aim to explore the different dimensions of feedback (Richness, Visualization, Interactivity, and Personalization) provided by the current computational models for argumentation, and the possibility of enhancing the power of explanations of such models, ultimately helping learners improve their critical thinking skills.