The Future of AI and Education: Chat GPT on Learning and Teaching Behaviours

Topic Information

Dear Colleagues,

We are pleased to announce a topic of MDPI publications that will explore the impact of Chat GPT on teaching and learning behaviors. Introduction of AI in education was supported with anticipation referring to teacher’s roles and competences and students’ roles as autonomous, self-directed learners (Istenic, 2019). The launch of Chat GPT, or generative pre-trained transformer, has dramatically captured teachers’ and learners’ attention and raised concern since it generates human-like behaviors such as writing, drawing and programming. Potential positives of Chat GPT use include understanding each student's unique learning style and adapting to their needs accordingly. With the ability to generate human-like text and carry on conversations, Chat GPT can answer questions, provide feedback, and guide students through complex topics. There are an important ethical considerations that need to be addressed when using Chat GPT in education. However, several negative concerns have also emerged. For one part, the inappropriate use of Chat GPT leads to a lack of critical thinking, plagiarism behaviors, bullying, and a complete dependence on technology. In this Special Issue, we both focus on opportunities and challenges associated with the use of Chat GPT in teaching and learning behavior and how the teacher’s role will be defined and which teaching skills will be needed in the future.

The submissions we are calling for are including, but are not limited to, the following topics:

1. How Chat GPT is utilized for personalized teaching and learning behaviors.
2. How Chat GPT promote student–teacher, student–student, and teacher–teacher interactions.
3. The AI algorithm development in Chat GPT for education.
4. How Chat GPT reforms the current education model.
5. The ethical considerations of using chat GPT in education.
6. The limitations in maintaining teachers’/students’ roles and engagement of Chat GPT.

We encourage research articles, case studies and literature reviews from scholars and educators across all disciplines. Our goal is to provide readers with a comprehensive understanding of how Chat GPT is transforming education and its potential to shape the future of learning. Submissions will be accepted until May 30 2024 for consideration in the topic.

References:

ISTENIČ, Andreja. (2019). Human learning and learning analytics in the age of artificial intelligence. British journal of educational technology. 50(6), 2974–2976. DOI: 10.1111/bjet.12879.

Thank you for your interest in this exciting area of research.

Dr. Xuesong Zhai
Prof. Dr. Andreja Istenic Starcic
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	3.1	7.2	2020	18.9 Days	CHF 1600
Behavioral Sciences behavsci	2.5	2.6	2011	28.7 Days	CHF 2200
European Journal of Investigation in Health, Psychology and Education ejihpe	3.0	4.4	2011	26.9 Days	CHF 1400

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All AI Behavioral Sciences EJIHPE

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23 pages, 13442 KiB  

Open AccessArticle

From Play to Understanding: Large Language Models in Logic and Spatial Reasoning Coloring Activities for Children

by Sebastián Tapia-Mandiola and Roberto Araya

AI 2024, 5(4), 1870-1892; https://doi.org/10.3390/ai5040093 - 11 Oct 2024

Cited by 1 | Viewed by 1479

Abstract

Visual thinking leverages spatial mechanisms in animals for navigation and reasoning. Therefore, given the challenge of abstract mathematics and logic, spatial reasoning-based teaching strategies can be highly effective. Our previous research verified that innovative box-and-ball coloring activities help teach elementary school students complex [...] Read more.

Visual thinking leverages spatial mechanisms in animals for navigation and reasoning. Therefore, given the challenge of abstract mathematics and logic, spatial reasoning-based teaching strategies can be highly effective. Our previous research verified that innovative box-and-ball coloring activities help teach elementary school students complex notions like quantifiers, logical connectors, and dynamic systems. However, given the richness of the activities, correction is slow, error-prone, and demands high attention and cognitive load from the teacher. Moreover, feedback to the teacher should be immediate. Thus, we propose to provide the teacher with real-time help with LLMs. We explored various prompting techniques with and without context—Zero-Shot, Few-Shot, Chain of Thought, Visualization of Thought, Self-Consistency, logicLM, and emotional —to test GPT-4o’s visual, logical, and correction capabilities. We obtained that Visualization of Thought and Self-Consistency techniques enabled GPT-4o to correctly evaluate 90% of the logical–spatial problems that we tested. Additionally, we propose a novel prompt combining some of these techniques that achieved 100% accuracy on a testing sample, excelling in spatial problems and enhancing logical reasoning. Full article

(This article belongs to the Topic The Future of AI and Education: Chat GPT on Learning and Teaching Behaviours)

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