Encouraging Responsible Use of Generative AI in Education: A Reward-Based Learning Approach
- URL: http://arxiv.org/abs/2407.15022v1
- Date: Wed, 26 Jun 2024 14:27:24 GMT
- Title: Encouraging Responsible Use of Generative AI in Education: A Reward-Based Learning Approach
- Authors: Aditi Singh, Abul Ehtesham, Saket Kumar, Gaurav Kumar Gupta, Tala Talaei Khoei,
- Abstract summary: This research introduces an innovative mathematical learning approach that integrates generative AI to cultivate a structured learning rather than quick solution.
The goal is to transition students from seeking quick fixes to engaging actively in a comprehensive learning experience.
- Score: 0.7889270818022226
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This research introduces an innovative mathematical learning approach that integrates generative AI to cultivate a structured learning rather than quick solution. Our method combines chatbot capabilities and generative AI to offer interactive problem-solving exercises, enhancing learning through a stepby-step approach for varied problems, advocating for the responsible use of AI in education. Our approach emphasizes that immediate answers from ChatGPT can impede real learning. We introduce a reward-based system that requires students to solve mathematical problems effectively to receive the final answer. This encourages a progressive learning path from basic to complex problems, rewarding mastery with final solutions. The goal is to transition students from seeking quick fixes to engaging actively in a comprehensive learning experience.
Related papers
- BloomWise: Enhancing Problem-Solving capabilities of Large Language Models using Bloom's-Taxonomy-Inspired Prompts [59.83547898874152]
We introduce BloomWise, a new prompting technique, inspired by Bloom's taxonomy, to improve the performance of Large Language Models (LLMs)
The decision regarding the need to employ more sophisticated cognitive skills is based on self-evaluation performed by the LLM.
In extensive experiments across 4 popular math reasoning datasets, we have demonstrated the effectiveness of our proposed approach.
arXiv Detail & Related papers (2024-10-05T09:27:52Z) - Integrating Cognitive AI with Generative Models for Enhanced Question Answering in Skill-based Learning [3.187381965457262]
This paper proposes a novel approach that merges Cognitive AI and Generative AI to address these challenges.
We employ a structured knowledge representation, the TMK (Task-Method-Knowledge) model, to encode skills taught in an online Knowledge-based AI course.
arXiv Detail & Related papers (2024-07-28T04:21:22Z) - YODA: Teacher-Student Progressive Learning for Language Models [82.0172215948963]
This paper introduces YODA, a teacher-student progressive learning framework.
It emulates the teacher-student education process to improve the efficacy of model fine-tuning.
Experiments show that training LLaMA2 with data from YODA improves SFT with significant performance gain.
arXiv Detail & Related papers (2024-01-28T14:32:15Z) - Causal Reinforcement Learning: A Survey [57.368108154871]
Reinforcement learning is an essential paradigm for solving sequential decision problems under uncertainty.
One of the main obstacles is that reinforcement learning agents lack a fundamental understanding of the world.
Causality offers a notable advantage as it can formalize knowledge in a systematic manner.
arXiv Detail & Related papers (2023-07-04T03:00:43Z) - Enhancing Chemistry Learning with ChatGPT and Bing Chat as Agents to
Think With: A Comparative Case Study [0.0]
This study explores the potential of Generative AI chatbots (GenAIbots) such as ChatGPT and Bing Chat, in Chemistry education.
It highlights the ability of ChatGPT and Bing Chat to act as 'agents-to-think-with', fostering critical thinking, problem-solving, concept comprehension, creativity, and personalised learning experiences.
It underlines the need for comprehensive educator training to effectively integrate these tools into classrooms.
arXiv Detail & Related papers (2023-05-12T09:27:58Z) - End-to-End Evaluation of a Spoken Dialogue System for Learning Basic
Mathematics [8.819665252533104]
This work presents a task-oriented Spoken Dialogue System (SDS) built to support play-based learning of basic math concepts for early childhood education.
The system has been evaluated via real-world deployments at school while the students are practicing early math concepts with multimodal interactions.
arXiv Detail & Related papers (2022-11-07T12:58:24Z) - Teacher-student curriculum learning for reinforcement learning [1.7259824817932292]
Reinforcement learning (rl) is a popular paradigm for sequential decision making problems.
The sample inefficiency of deep reinforcement learning methods is a significant obstacle when applying rl to real-world problems.
We propose a teacher-student curriculum learning setting where we simultaneously train a teacher that selects tasks for the student while the student learns how to solve the selected task.
arXiv Detail & Related papers (2022-10-31T14:45:39Z) - Human Decision Makings on Curriculum Reinforcement Learning with
Difficulty Adjustment [52.07473934146584]
We guide the curriculum reinforcement learning results towards a preferred performance level that is neither too hard nor too easy via learning from the human decision process.
Our system is highly parallelizable, making it possible for a human to train large-scale reinforcement learning applications.
It shows reinforcement learning performance can successfully adjust in sync with the human desired difficulty level.
arXiv Detail & Related papers (2022-08-04T23:53:51Z) - Teachable Reinforcement Learning via Advice Distillation [161.43457947665073]
We propose a new supervision paradigm for interactive learning based on "teachable" decision-making systems that learn from structured advice provided by an external teacher.
We show that agents that learn from advice can acquire new skills with significantly less human supervision than standard reinforcement learning algorithms.
arXiv Detail & Related papers (2022-03-19T03:22:57Z) - Reset-Free Reinforcement Learning via Multi-Task Learning: Learning
Dexterous Manipulation Behaviors without Human Intervention [67.1936055742498]
We show that multi-task learning can effectively scale reset-free learning schemes to much more complex problems.
This work shows the ability to learn dexterous manipulation behaviors in the real world with RL without any human intervention.
arXiv Detail & Related papers (2021-04-22T17:38:27Z)
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.