Improving elementary school students’ morality with AI

This study focuses on exploring moral education strategies for elementary school students through the use of conversational artificial intelligence (AI), with the aim of strengthening democratic citizenship competencies required in the digital society. The research investigates the social impact of conversational AI, the concerns it raises in elementary education, and proposes approaches to fostering moral judgment and critical thinking as solutions. Emphasis is placed on the importance of discussion-based learning using moral dilemma situations, while examining the potential of conversational AI as a supportive educational tool. To address issues such as biased data learning and the uncritical acceptance of information, the study highlights approaches including the cultivation of conscience-based morality, the presentation of age-appropriate moral dilemmas, and the promotion of moral reasoning through discussion-oriented learning. In particular, the study explores the application of conversational AI in the classroom to compare ethical standards across cultures and eras, facilitate discussions, provide feedback, and generate role-play scenarios. Furthermore, it proposes that combining teachers’ active guidance with AI’s supportive functions can help elementary students establish their own criteria for judgment and engage actively in moral discourse. This study suggests a new educational approach to strengthening morality and critical thinking through conversational AI, and discusses the future direction of democratic citizenship education in the digital learning environment.

Publication Details

Journal
The Korean Society for Artificial Intelligence Ethics
Published
2025-08-31
DOI
https://doi.org/10.59728/jaie.2025.4.2.52
Primary Topic
Anesthesia and Sedative Agents
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Improving elementary school students’ morality with AI

The Korean Society for Artificial Intelligence Ethics
Anesthesia and Sedative Agents
article

Improving elementary school students’ morality with AI

article en

Abstract

This study focuses on exploring moral education strategies for elementary school students through the use of conversational artificial intelligence (AI), with the aim of strengthening democratic citizenship competencies required in the digital society. The research investigates the social impact of conversational AI, the concerns it raises in elementary education, and proposes approaches to fostering moral judgment and critical thinking as solutions. Emphasis is placed on the importance of discussion-based learning using moral dilemma situations, while examining the potential of conversational AI as a supportive educational tool. To address issues such as biased data learning and the uncritical acceptance of information, the study highlights approaches including the cultivation of conscience-based morality, the presentation of age-appropriate moral dilemmas, and the promotion of moral reasoning through discussion-oriented learning. In particular, the study explores the application of conversational AI in the classroom to compare ethical standards across cultures and eras, facilitate discussions, provide feedback, and generate role-play scenarios. Furthermore, it proposes that combining teachers’ active guidance with AI’s supportive functions can help elementary students establish their own criteria for judgment and engage actively in moral discourse. This study suggests a new educational approach to strengthening morality and critical thinking through conversational AI, and discusses the future direction of democratic citizenship education in the digital learning environment.

The Korean Society for Artificial Intelligence EthicsVol. 4(2)
Quality Education
Openalex Percentile: Top 33%
Anesthesia and Sedative Agents
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.