The Multidimensional Impact of AI in the Field of Education:

Applications, Limitations and Future Development

Ziyue Zhang

School of Computer and Network Security, Chengdu University of Technology, Chengdu, Sichuan, China

Keywords: Artificial Intelligence, Intelligent Tutoring System, Learning Engagement, Privacy Security, Educational

Equality.

Abstract: The rapid development of artificial intelligence (AI) technology has brought profound changes to the field of

education. This study analysed the application of AI technology in practical educational scenarios through

literature analysis and review. Research has found that AI technology significantly improves teachers' teaching

efficiency and students' learning outcomes by designing personalized learning plans for students and

optimizing classroom teaching resource allocation. At the same time, research has also raised the challenges

and risks that AI applications in the field of education will face, such as causing students to become dependent

on technology, data security risks, and unequal resource allocation. This experiment analyses teaching

scenarios such as science education, college English, and middle school biology, and points out the advantages

and limitations of AI technology. Finally, the article explores the possible development directions of AI

technology in the field of education in the future, and points out the shortcomings of this study.

1 INTRODUCTION

Artificial intelligence (AI) is an emerging computer

technology that simulates human behaviour. It

enables computer systems to perform tasks that

typically require human intelligence, such as

learning, reasoning, language understanding, and

problem-solving. AI can also automatically learn

patterns and rules from data and networks through

technologies such as machine learning and deep

learning, thereby completing more demanding tasks

(Wang et al., 2024).

Currently, with the increasing demand of data

handling and feedback capabilities, AI technology

has been updated and iterated to a very high level. AI

technology has already had a very profound impact

on all walks of life including education, healthcare

and industry in today's society (Saffira et al., 2024).

The application of artificial intelligence (AI) in the

field of education has undergone a transition from

traditional computer-assisted instruction to web-

based intelligent education systems, and further to

embedded systems (such as robots and chatbots).

Nowadays, AI has been widely applied in various

aspects of education management, teaching, and

learning (Chen et al., 2020). AI can enhance students'

personalized learning through intelligent

recommendation systems, thereby addressing the

issue of differentiated knowledge levels among

students. Moreover, it can assist teachers in planning

teaching programs and resources through intelligent

classroom management systems and analysis and

prediction technologies. Akhtar. N et al. pointed out

in 2024 that when learning highly conceptual biology,

middle school students often find biological

knowledge such as genetics and cell structure obscure

and cumbersome (Akhtar et al, 2024). In 2024,

Almasri. F pointed out that AI has many benefits in

science education. For example, AI can analyse each

student's learning habits and develop personalized

learning plans based on their abilities and needs.

Another advantage of artificial intelligence in science

education is its ability to create a virtual laboratory

environment for students to learn and explore in

different experimental scenarios, thereby deepening

their understanding of scientific disciplines.

Although AI has brought many conveniences to

the education industry, there are still some limitations

in the current use of AI technology in the field of

education. For example, artificial intelligence cannot

completely replace the complex teaching tasks of

human teachers, especially in scenarios that require

emotional interaction. In addition, excessive reliance

Zhang, Z.

The Multidimensional Impact of AI in the Field of Education: Applications, Limitations and Future Development.

DOI: 10.5220/0014355800004718

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

In Proceedings of the 2nd International Conference on Engineering Management, Information Technology and Intelligence (EMITI 2025), pages 371-375

ISBN: 978-989-758-792-4

371

on artificial intelligence may lead to a decline in

students' self-directed learning ability and even

induce them to engage in academic misconduct.

When AI processes student data, students' personal

privacy issues cannot be fully guaranteed. Due to the

uneven distribution of social teaching resources, the

use of AI assisted teaching may exacerbate

educational inequality (Saffira et al., 2024).

This article will review existing research findings,

aiming to analyse the impact of AI in different

educational scenarios on student learning outcomes,

student engagement, teacher-student interaction,

educational assessment, academic integrity, and

cheating detection. It will also examine the challenges

and potential shortcomings currently faced by AI

applications in the field of education. Finally, the

article will explore the future development directions

and trends of AI in education.

2 REVIEW STRATEGY

2.1 Methods and Materials

This study aims to analyse the impact of AI

technology applications in the field of education, and

therefore uses reverse analysis to summarize existing

research and literature. In fact, Snyder pointed out in

2019 that analysing existing research results through

systematic or semi systematic literature reviews can

provide a more comprehensive understanding of

various aspects of research phenomena (Synder,

2019).

This study adopts a qualitative research method,

with literature review as the main research design. By

systematically reviewing and analysing existing

research literature, identify, analyse, and synthesize

the application and impact of AI in education. This

method can provide a deep understanding of the

research phenomenon and provide a theoretical basis

for subsequent empirical research.

2.2 Search Strategy

This study will use a series of keywords related to AI

and education for literature search. These keywords

include "Artificial intelligence," "education,"

"Machine learning," "Deep learning," "Educational

management," "limitations," "challenges," "future

trends," and so on. The search will cover multiple

academic databases, such as CNKI (China National

Knowledge Infrastructure), VIP Journal, Web of

Science, Google Scholar, etc. These databases cover

a wide range of academic literature and can provide

abundant resources for research. Meanwhile, to

ensure the timeliness and relevance of the research,

articles published after 2010 will be selected. AI

technology is developing rapidly, and recent research

can better reflect the current level of technology and

application.

When screening literature, two aspects will be

considered: research type and research content.

Firstly, prioritize qualitative or quantitative research

articles, especially situational research articles that

incorporate the use of AI in actual teaching processes.

These studies are usually more scientific and credible.

Secondly, ensure that the content of the article is

closely related to the research topic, with a focus on

the specific applications, advantages, limitations, and

future trends of AI in education.

3 TECHNICAL APPLICATION

AND LITERATURE ANALYSIS

3.1 Overview of AI Technology

3.1.1 AI Intelligent Recommendation

System

AI recommendation systems can integrate

information from different sources, such as users’

data, content descriptions, social network

information, etc., to provide more accurate

recommendations. At the same time, they can capture

the dynamic changes in user preferences in real time

and adjust recommendation strategies based on user

feedback through reinforcement learning and other

technologies. In the field of education, AI

recommendation systems can recommend

personalized learning paths and resources for students

based on their learning progress, knowledge mastery,

and learning styles, helping them learn more

efficiently. On educational social platforms, AI

recommendation systems can recommend learning

communities and discussion groups that match

students' interests and learning goals, promoting

communication and collaboration among students

(Zhang et al., 2021).

3.1.2 AI Optimization Algorithm

Artificial intelligence optimization algorithms are

computational methods inspired by natural processes

and intelligent behaviors to solve complex

optimization problems. The role that AI optimization

algorithms can play in the field of education is to

improve the rationality and effectiveness of teaching

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management. AI can monitor in real-time the

resources available in the teaching environment, such

as classrooms and the use of teaching equipment, and

allocate teaching resources reasonably according to

the needs of teaching tasks and time. AI optimization

algorithms have solved problems that may arise in

practical teaching situations such as course conflicts

and unreasonable time arrangements, while also

improving the utilization of teaching resources. AI

optimization algorithms can also help improve the

quality of teaching in classrooms. Teachers can not

only subjectively judge and evaluate students'

learning situations, but also provide more

comprehensive teaching evaluations through AI

integrated data such as student exam scores and

classroom participation (Xu, 2025).

3.1.3 AI Predictive Analysis Technology

AI predictive analysis technology utilizes advanced

machine learning techniques to provide high-

precision predictions by processing large amounts of

complex data models. This technology can provide

teachers with macro level predictions of students'

learning status, knowledge understanding, and other

learning situations by analyzing the learning

situations of different students. At the same time, it

can help teachers provide different teaching plans for

students to meet the needs of students with different

abilities (Yuan et al., 2025).

3.2 Application Scenarios of AI

Technology in Education

3.2.1 Science Teaching Scenario

The application of AI technology in scientific

teaching environments can significantly enhance

students' learning engagement and academic

performance. For example, in academic tests,

students who use AI tools have an average score 15%

to 20% higher than those who only participate in

traditional teaching.

AI can also provide real-time and personalized

feedback based on students' learning progress and

performance, helping them improve their learning

methods, with an average feedback accuracy rate of

90%. At the same time, AI tools analyse students'

learning behaviour and historical data to predict their

performance, providing timely intervention and

support, with prediction accuracy reaching 85% to

90%. For teachers, AI tools can help them design

science units, grading standards, and tests,

significantly improving work efficiency and saving

an average of 30% of time. These applications are

mainly based on technologies such as machine

learning, natural language processing, generative AI,

and data mining, such as support vector machines

(SVM), decision trees, Transformer architectures,

and GPT series. Although AI has shown great

potential in science education, it also faces some

challenges, such as limited understanding of specific

subject content, insufficient ability to adapt to

different educational environments, and performance

differences between different AI models (Almasri,

2024).

3.2.2 College English Teaching Scenario

The application of AI technology in college English

teaching has also improved students' learning

situation. Experimental data shows that students have

improved their language abilities through AI learning

platforms. For example, their reading comprehension

score increased from 65 to 80, while at the same time,

their vocabulary increased by 20%. During the

experiment, the intervention of AI mobile learning

platform significantly increased students' interest in

learning, and the average time per person

participating in English learning per week increased

from 2 hours to 4 hours. The error feedback

mechanism of the AI mobile learning platform has

helped students reduce their error rate by 30% in

writing and speaking practice. In addition, AI mobile

learning platforms provide strong teaching support

for teachers, enabling them to design courses more

effectively and evaluate student performance, and

reducing teachers' preparation time (Guo et al., 2025).

4 SUGGESTIONS AND FUTURE

DEVELOPMENT

4.1 Teacher Training and Development

Systematic AI technology training for practitioners in

the education field is key to promoting the application

of AI technology in the education sector. Training can

start from the technical characteristics, ethics, and

usage norms of AI, and design targeted courses and

seminars according to different disciplines. Teachers

can better understand the functions and usage norms

of AI technology through training, thereby effectively

integrating it into practical teaching scenarios.

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4.2 Data Privacy and Ethics

Privacy and security issues have always been people's

biggest concerns about AI technology. Ensuring the

privacy and security of students and teachers is

crucial in the field of education, as the data involved

is highly confidential. In the future, if educators want

to use AI technology on a large scale, they need to

develop clear and strict protection policies for privacy

and security. And it is necessary to popularize

relevant moral and ethical knowledge for teachers and

students, so that they can use AI technology

reasonably while preventing the risks brought by AI.

4.3 Educational Equity

There are differences in technological levels among

different regions and countries, which may lead to

unequal development of artificial intelligence

technology and exacerbate the digital divide. In the

education industry, in order to avoid educational

inequality caused by the digital divide, it is necessary

to develop fair and just education policies to ensure

that every student and teacher has equal access to

artificial intelligence resources. In addition, it may be

necessary to provide additional technical and

financial assistance to resource scarce and

underdeveloped areas and schools, such as building

signal stations and regularly updating equipment.

5 CONCLUSION

This study explores the multidimensional impact of

artificial intelligence (AI) in the field of education by

analyzing the application of AI technology and

practical teaching scenarios. It involves the

application, advantages, and limitations of AI

technology in different teaching scenarios, and also

analyzes the future development direction of AI. The

research results indicate that AI technology has

enormous potential for application in educational

management, assisting teachers in teaching, and

aiding students in learning. Simply put, AI

technology can make learning more personalized,

allowing teachers to develop more detailed and

appropriate teaching plans based on each student's

different learning situation. At the same time, AI can

also provide students with a better teaching

experience, allowing them to understand complex and

in-depth knowledge, thereby enhancing their learning

interest and knowledge level.

However, the application of AI technology in the

field of education also comes with challenges. AI

technology does not have the ability for emotional

communication, which makes it difficult for AI

technology to fully play its role in complex teaching

environments. Moreover, excessive use of AI for

teaching or learning may lead to students' excessive

reliance on AI technology, which can result in a

decline in their self-directed learning ability and even

give rise to academic integrity issues. In the process

of using AI technology for learning and teaching,

both teachers and students may face privacy and

security issues. The application of AI technology may

also exacerbate the digital divide, as not all students

can benefit equally from these technologies.

Although this study conducted a contextual

analysis of the application of AI technology in the

field of education, there are still many shortcomings

in the research. Firstly, this study only reviewed

existing articles and did not conduct quantitative

research, so the conclusion lacks practical data

support to some extent. Secondly, the scope of this

research review includes research papers from the

past 10 years and does not involve early papers on the

principles of AI technology, which may lack

authority in explaining AI technology

REFERENCES

Akhtar, N., Matloob, R., & Nawaz, R. (2024). Exploring

the impact of artificial intelligence in teaching of

biology at secondary level. International Research

Journal of Management and Social Sciences, 5(3), 427–

440.

Almasri, F. (2024). Exploring the impact of artificial

intelligence in teaching and learning of science: A

systematic review of empirical research. Research in

Science Education, 54(3), 977–997.

Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence

in education: A review. IEEE Access, 8, 75264-75276.

Guo, S., Abdul Halim, H. B., & Mohd Saad, M. R. B.

(2025). Leveraging AI-enabled mobile learning

platforms to enhance the effectiveness of English

teaching in universities. Scientific Reports, 15(1),

15873.

Saffira, M. F., Lubis, M., Kusumasari, T. F., & Putri, D. P.

(2024). Advancements in artificial intelligence and data

science: Models, applications, and

challenges. Procedia Computer Science, 234, 381–

388.

Snyder, H. (2019). Literature review as a research

methodology: An overview and guidelines. Journal of

Business Research, 104, 333-339.

Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z.

(2024). Artificial intelligence in education: A

systematic literature review. Expert Systems With

Applications, 252, 124167.

EMITI 2025 - International Conference on Engineering Management, Information Technology and Intelligence

374

Xu, X. (2025). AI optimization algorithms enhance higher

education management and personalized teaching

through empirical analysis. Scientific Reports, 15,

10157.

Yuan, L., Hou, B., Feng, D., Abdul Rahman, S. S., & Abdul

Razak, R. (2025). Predictive analytics in education:

Leveraging AI for personalizing student learning

trajectories. In R. Koul et al. (Eds.), Proceedings of the

2nd International Conference on Educational

Development and Social Sciences (EDSS 2025) (pp.

309–314). Atlantis Press.

Zhang, Q., Lu, J., & Jin, Y. (2021). Artificial intelligence in

recommender systems. Complex & Intelligent Systems,

7(3), 439–457.

The Multidimensional Impact of AI in the Field of Education: Applications, Limitations and Future Development

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