Artificial Intelligence in Education and Value Education

Shweta Bhatnagar and Rashmi Agrawal

School of Computer Applications, Manav Rachna International Institute of Research and Studies, Delhi Suraj Kund Road,

Sector 43, Faridabad, 121004 Haryana, India

Keywords: AI in Education, AI Applications in Education, Learner Analytics, Value‑Based Education.

Abstract: Delivering moral education requires a unique teaching methodology that differs from traditional learning.

Educational technology offers a platform for providing personalized education. In this paper, we present an

extensive review of recent advancements in Artificial Intelligence (AI) applications in education. The study

aims to answer two fundamental questions: how AI technologies are used in education, and how they can be

integrated into value, moral, and character education. The review focuses on AI-related educational research

carried out in the last five years and reveals a prevalent focus on understanding learners to facilitate

personalization, customization, and recommendations.

1 INTRODUCTION

The integration of Artificial Intelligence (AI) into

education has undergone a transformative journey,

progressing from computer technology to web-based

intelligent systems and even extending to humanoid

robots taking on teaching roles and chatbots assisting

educators (

L. Chen et al., 2020). This evolution has

entailed collaborative efforts from a diverse range of

professionals, including data scientists, statisticians,

psychologists, linguists, and education experts, who

have collectively engineered intelligent systems

capable of decision-making. These systems not only

support educators in their tasks but also empower

them to navigate an ever-changing landscape,

enhancing their knowledge and competencies (

Pokrivcakova., 2019)

Against this backdrop of technological

advancement, this paper delves into the recent strides

made in education technology within the past five

years, with a particular focus on applications of

Artificial Intelligence in moral education. Through a

systematic literature review, this study seeks to

address pivotal questions driving the evolution of AI-

based education:

• Which domains and AI-driven technologies

have been explored in the context of

education?

• To what extent has research been undertaken

to leverage AI for value education?

The paper is structured into six distinct sections.

In Section II, the methodology employed for

conducting the comprehensive literature review is

expounded upon. Moving to Section III, a detailed

exploration of AI-based research within mainstream

education is presented. Subsequently, Section IV is

dedicated to scrutinizing AI's impact on value

education. Section V visualizes the findings. The

concluding Section VI briefly summarizes the

findings and delineates prospects for future research.

2 METHODOLOGY

Literature selection for this review followed a

systematic and comprehensive approach to ensure the

inclusion of relevant and recent research findings on

applications of AI in education. Only studies

published within the last five years, from 2018 to

2023, were considered eligible for inclusion. As

evident from Figure 1, the number of papers

published on Artificial Intelligence applications in

education has been increasing since 2018, although

many studies have been conducted from 2008 to

2014.

Papers were included if they contributed to the

application of Artificial Intelligence in education. The

relevance of each study was assessed based on the

title, abstract, and keywords. Only peer-reviewed

journals were considered for inclusion, to maintain

the quality and reliability of the included studies.

Bhatnagar, S. and Agrawal, R.

Artiﬁcial Intelligence in Education and Value Education.

DOI: 10.5220/0013942800004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 5, pages

727-734

ISBN: 978-989-758-777-1

727

Conference proceedings, books, and other non-peer-

reviewed sources were excluded.

The IEEE and Science Direct databases were

searched. Keyword ‘applications of Artificial

Intelligence’ and search terms ‘Education

technology,’ ‘Moral Education’, ‘Value Education,’

and ‘Ethics Education’ were used to retrieve relevant

articles. Boolean operators (AND, OR) were

employed to effectively combine search terms.

Figure 1: Number of publications on Artificial Intelligence

applications in Education in IEEE over the years.

A total of 152 results were generated, of which

127 papers were weeded out based on topics, use of

technologies other than AI, and abstracts. 25 relevant

papers were identified.

These 25 papers were grouped based on their

areas of application in education. The six areas of AI

application in education are smart campuses, library

systems, intelligent tutoring, Examinations,

Laboratories, and Learner Analytics. The search for

moral education resulted in 46 open-access papers

published in Computer Science Journals.

Studies addressing the social science aspects of

ethics were excluded. Of the 46 shortlisted papers, 3

good quality papers with real-life complete

applications matched the criteria. The criteria for

inclusion in the literature review were an AI-based

application for value/moral/character education.

AI-Powered Applications and Technologies in

Education: Educational institutions perform multiple

functions such as academics, governance, student

guidance, examinations, etc. daily for which the

faculty has to spend a lot of time.

These activities are getting automated to an

extent, to facilitate faculty with some right tools for

their work. Technology allows management to get a

holistic view of the organization.

This not only empowers the faculty but also gives

them the necessary tools for 21st-century teaching.

Considerable Work has been done in the past decade,

especially in the last 5 years on developing such tools.

The summary of the applications of Artificial

Intelligence in the field of education is given in Table

1 and the technologies are summarized in Table 2.

Table 1: AI applications in areas of education.

Application

Technological

Advancement

Research

Publications

Smart

Campus

Networked

infrastructure, cloud

storage, IoT for

wearables and smart

gadgets, surveillance,

biometric and smart

attendance

[3], [4], [5], [6],

[7]

Library

AI based search

suggestions, identifying

hot topics for research

[8], [9], [10],

[11], [12]

Intelligent

tutoring

Personalized learning,

dashboards for teachers,

AI-based question

classification on

BLOOM’s taxonomy,

AI-based curriculum

builder, chat-bots for

learning resource

suggestions, teaching

robots using NLP,

English writing

intelligent tools

[13], [14], [15],

[16], [17], [18],

[19], [20], [21],

[22], [23], [24],

[25], [26], [27],

Examination

e-proctoring, safe exam

browsers, AI-based

auto-evaluation, AI-

based formative

assessments

[28], [29],

Laboratory

Virtual laboratories

based on 3D modeling

[30], [31], [32],

[33], [34]

Learner

Analytics

Learner Profiling,

performance prediction,

career path

recommendation,

Mapping of skills with

course outcomes,

identification of open

educational resources for

skills, mapping learners

to skills

[35], [36], [37],

[38], [39], [40],

[41], [42], [43],

[44]

Generative

applications

Language translation,

personalized responses,

Tutoring support

material generation,

[45], [46], [47]

100

150

200

250

2000

2002

2004

2006

2008

2010

2012

2014

2016

2018

2020

Number of publications on Artificial

Intelligence applications in

Education over the years

Total

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Table 2: Technologies used in areas of education.

Area of

Application

Technologies used Ref.

Examination

Machine learning techniques to help understand the learner

learning style, such as KNN-means for regression and Deep

Neural Networks for improving machine learning on the basis of

weights. Data mining processing model SEMMA

[29]

Recurrent Neural Networks are used for creating the prediction

model for the learner performance.

[35]

Decision Trees, Support Vector Machine (SVM), Naïve Bayes

(NB), K-Nearest Neighbour (kNN), Logistic Regression (LR) and

Random Forest (RF). Synthetic Minority Oversampling

Technique (SMOTE)

[36]

DNN to train machine on facial images; CLAHE algorithm for

light normalization; multi-level, multi-modal and multi-task

learning for face recognition

RNN based on LSTM for voice recognition (identification of d-

vector)

CNN+RNN for t

reco

nition

[28]

Random forest, K-Nearest Neighbour, Large-margin nearest

neighbour regression.

Creation of numeric features for the social media activities as

data set.

Regression models were applied in Weka tool

[37]

Improved conditional generative adversarial network based deep

support vector machine (ICGAN-DSVM) algorithm

Comparison between ICGAN and CGAN, while DSVM is

compared with SVM

Heuristic based multiple kernel learning (MKL) is use

[38]

Research area

Identification

and library

DeepWalk was used to extract keywords from single paper.

Graph Convolutional Network (GCN) was used to extract

relevant keywords from multiple papers.

TextRank, SurfKE, S-DWKE, M-GCKE are used for ranking the

extracted keywords for hot topic.

Googlenews-vecctors-negative300.bin, with 300-dimensional

news corpus pre-trained by Google with word2vec, and

newsblogbbs.vec a pre-trained Chinese word vector were used for

testin

hot to

ics

[10]

Unbalanced multistage Heat Conduction and Mass Diffusion

(UHM) algorithm

[9]

Laboratory

MATLAB/Simulink environment

3DS Max for 3D modelling

PD control algorithm for movement control

[32]

Robotic ‘Equilibrium Player’

Ultimatum Game

[33]

Parts of Speech (POS) for breaking word problem into keywords

Object oriented analysis and design (OOAD) to classify the

keywords into numbers and operators

Machine Learning algorithms for classifying keywords into

categories

[34]

Learner

Analytics

Random walk strategy for creating the traversal graph

Precision, recall and F1 score to evaluate the recommender

system

Graph embedding methods like DeepWalk, Node2vec, LINE,

Graph Factorization, SDNE

[42]

Python for creation of Quiz Making Language and LMS

automato

[40]

Artiﬁcial Intelligence in Education and Value Education

729

latent Dirichlet allocation (LDA) statistical method

Sentiment Analysis

[41]

W3C standards, such as RDFs and OWL

Linked-data ontologies

Protégé LOV plugin

OOPS tool for validation

[27]

Fuzzy logic based on the Mamdani inference [43]

Smart Campus

Support Vector Machine (SVM), Decision Tree (DT), Random

Forest (RF), and K-nearest neighbor (KNN) for identifying the

uest burst

[6]

Monte Carlo Tree Search (MCTS) and Location Verification

(LV) algorithms are used

[7]

A brief discussion on technologies like google glass, fitbit, oculus

rift, emotive insight, Samsung gear, apple watch, Empatica,

sensewear, etextiles, tactile vest, hip disk, motiv GI, leg mounted

RFID, Go Pro 4 and fitbit iconic, head and pen motion modules

was made

[4]

Teaching/

Intelligent

Tutoring

Machine Learning

Sentiment Analysis

[14]

LSTM model

Wiki Word Vectors pre-trained word embedding

[22]

Speech Act Classifier

SVM algorithm

Google’s Dialog-Flow technology

[17]

LSTM-CRF algorithm

brat rapid annotation tool

[18]

Input channel addition algorithm

Event bundle encoding algorithm

Alternative template learning algorithm

[13]

Table 3: Applications and technologies used in Moral/ ethics education.

Ref. Technologies use

[48]

Thematic analysis of literature on CAI

Typically, CAI systems include an Automatic Speech Recognition (ASR), Natural

Language Understanding (NLU), Dialogue Management (DM), Natural Language

Generation (NLG), and Text to Speech (TTS) modules, which together constitute

the hi

h-level architecture of CAI.

[52]

Normative reasoning and deontic logics

The enabling technology are higher-order theorem proving systems via the Shallow

Semantical Embedding technique. Interactive theorem proving (ITP) and

automated theorem proving (ATP) for HOL, and also the overall coalescence of

heterogeneous theorem proving systems, as witnessed, in particular, by

Isabelle/HOL, LEO-II and Leo-III, which fruitfully integrate other specialist ATP

systems

[49]

Five separate Binary Logistic Regression models were run. Each model focused on

a different predictor detecting profanity or offensive words. Six separate Ordinary

Least Squares (OLS) Multiple Regression models were run, analyzing how

different factors influence chatbot responses

[53]

Value Group Classifier for identifying stakeholders in the ethical decision-making

tool designed using SVM for educational purpose

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Research in Artificial Intelligence Assisted Value

Education

The importance of ethics in education is emphasized

in the National Education Policy 2021 of India

through inclusion of ethical concepts in holistic

education, such as service to society, love, peace

duty, non-violence and scientific temperament.

Applications like interactive storytelling (

N. Park et al.,

2021)

using Conversational Artificial Intelligence can

help young children understand the impacts of their

decisions. A

chat-bot that predicts the use of profane

languageduring interaction and replies in a human-

like manner to avoid such interactions was developed

in (

P. E. Torres et al., 2021)

The rules for detecting the use of profanity was

based on the participant’s profile. Physical-digital

play applications are helping shape the personalities

and values of young children (

R. A. Shweta Bhatnagar.,

2025)

by including strategies of motivation,

collaboration, problem-solving, decision-making and

physical activity.

The research suggests techniques of action

regulation, social expectations, goal-tool-action

alignment and technical feature creation for

improving the behavior of children. Also, the

numerous bio-wearables are being developed for the

children. The data collected by these can have a lot of

impact on the behavior of children (

B. Coppin., 2004)

Ethico-Legal reasoning is exemplified using HOL for

technology applications, as demonstrated in LogiKEy

(

R. H. Hassan et al., 2021)

. The court rulings on ethical

matters were encoded in HOL and are now available

as open reference for reuse by all. These examples

show the existing research in the field of moral

education.

Table 3: Applications and technologies used in

moral/ethics education

3 RESULTS AND DISCUSSION

Literature for the past 5 years was studied to analyze

the first research question on areas of application of

AI and the technologies mostly used for education.

The second research question about the applications

in the field of moral education was searched to

understand the depth of research in this area. Out of

the papers studied on applications of AI in education,

the maximum was in the category of intelligent

tutoring and examination. 20% of the applications in

education focused on understanding the learner or

improving the content delivery for the learner These

three are the core activities of the educational

institutions. Figure2 explains the breakup of the

literature review.

Figure 2: Distribution of research papers on applications in

AI on education.

The technology-wise grouping of applications is

given in Figure 3. Most of the research in this field

has happened using Machine learning algorithms.

Apart from the wearables, laboratories requiring

visualizations, and chatbots requiring NLP; most of

the applications reviewed involved prediction,

recommendation, and classification of information.

Generative AI tools can develop a safe environment

for learners, where their exposure to unwanted

content is reduced, although there is limited research

on building personalized learning environments using

Generative AI.

Figure 3: AI techniques used in education.

The second question about research in moral

education discusses applications with moral

implications. Ethical education has always been a part

of national policies and recommendations of

committees formed for evaluating the education

system. The use of Artificial Intelligence in

addressing value education is limited and has scope

for further research.

Artiﬁcial Intelligence in Education and Value Education

731

4 CONCLUSIONS

A comprehensive literature review was undertaken to

identify applications centered around Artificial

Intelligence in education. The research aimed to

address two key inquiries: the domains of application

of AI in education, and specific instances of

application in the realm of value education. The

outcomes show that Artificial Intelligence aids in

establishing a personalized and tailored connection

with learners. Machine learning algorithms and tools

process data for data-driven functions such as

recommendations, classification, and predictions.

Popular applications primarily cater to mainstream

educational contexts, with a lack of representation in

the domain of value education.

In future, the identified gap in the application of

technological solutions within the sphere of moral,

ethical, and value education will be addressed by noel

methods. This approach advocates for the

incorporation of existing digital tools into this

particular domain. By adapting and integrating these

tools, the proposed research seeks to bridge the

technological gap in the field and enhance the

delivery of moral, ethical, and value education in

future.

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