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la IA, así como las principales barreras para su adopción  
en el ámbito educativo. El análisis reveló que los docentes  
valoran la IA principalmente por su capacidad de  
Artificial Intelligence in Education:  
Systematic Review of Perspectives,  
Benefits and Challenges in Teaching  
Practice  
personalizar el aprendizaje  
y
optimizar tareas  
administrativas, aunque también expresan preocupaciones  
éticas sobre privacidad y equidad, así como limitaciones  
técnicas relacionadas con infraestructura y capacitación  
insuficiente. Entre los desafíos más destacados se  
encuentran la falta de competencias técnicas y el  
escepticismo hacia la tecnología en algunos contextos  
educativos. Aunque la IA ofrece un potencial significativo  
para mejorar la educación, su éxito depende de una  
integración equilibrada que respete el rol del docente y  
promueva una implementación ética y equitativa. Esta  
revisión resalta la necesidad de políticas educativas que  
apoyen la formación continua y promuevan un acceso  
igualitario a las tecnologías de IA.  
Inteligencia artificial en la educación:  
Revisión sistemática de perspectivas,  
beneficios y desafíos en la práctica docente  
Ruth Peñafiel-Jurado1 , Nelly Márquez-Márquez1 , Isabel  
Guamán-Villa1  
1
Miguel Moreno Ordóñez Educational Unit, Cuenca canton, Azuay  
province, Ecuador.  
Palabras clave: inteligencia artificial, práctica  
docente, revisión sistemática, personalización del  
aprendizaje, desafíos éticos.  
Correspondence: Elias201penafiel@outlook.es  
Received: October 15, 2024 - Accepted: December 2, 2024 -  
Published: December 17, 2024.  
INTRODUCTION  
ABSTRACT  
Artificial intelligence (AI) has emerged over the past  
decade as a transformative technology across multiple  
sectors, including education. In the context of education,  
AI offers significant opportunities to improve teaching and  
learning by automating processes, personalizing  
instruction, and supporting pedagogical decision-making  
(Chounta et al., 2021; Kim and Kim, 2022). AI tools, such  
as intelligent tutoring systems, adaptive learning  
platforms, and educational data analytics applications,  
have begun to redefine the role of the teacher and  
pedagogical strategies in classrooms. However, the use of  
AI in education also poses significant challenges, ranging  
from ethical concerns and data privacy to teacher  
resistance to incorporating new technologies due to a lack  
of preparation and training (Celik, 2023; Pratama et al.,  
2023).  
The objective of this systematic review is to analyze  
teachers' perspectives, benefits, and perceived challenges  
in the implementation of artificial intelligence (AI) in  
education from 2020 to 2024. The PRISMA model was  
used to select a total of 53 studies that met the inclusion  
criteria. Data were extracted and evaluated to synthesize  
teachers' perceptions, the pedagogical and administrative  
advantages of AI, and the main barriers to its adoption in  
the educational field. The analysis revealed that teachers  
value AI primarily for its ability to personalize learning  
and optimize administrative tasks; however, they also  
express ethical concerns about privacy and equity, as well  
as technical limitations related to infrastructure and  
insufficient training. Among the most prominent  
challenges are the lack of technical skills and skepticism  
toward technology in certain educational contexts.  
Although AI offers significant potential to enhance  
education, its success depends on a balanced integration  
that respects the teacher's role and promotes ethical and  
equitable implementation. This review highlights the need  
for educational policies that support continuous training  
and promote equal access to AI technologies.  
This systematic review seeks to synthesize recent  
research on teachers’ perceptions, benefits, and challenges  
of using AI in education. The included studies cover the  
period 2020-2024,  
a
stage characterized by the  
massification of public-friendly AI platforms, such as  
ChatGPT, launched in November 2022, which marked a  
milestone in the accessibility of advanced chatbots and  
unleashed a global boom in their educational use. These  
platforms have facilitated teachers’ access to AI through  
simple interfaces, allowing them to implement this  
technology without the need for specialized knowledge in  
programming or data analysis.  
Keywords: artificial intelligence, teaching practice,  
systematic review, personalized learning, ethical  
challenges.  
RESUMEN  
Seen in this way, the teacher is not directly  
confronted with AI, but with its practical applications,  
such as chatbots, which are presented as tools that simplify  
complex tasks. However, for the teacher, who is usually  
not a programmer or AI expert, the internal functioning of  
El objetivo de esta revisión sistemática es analizar las  
perspectivas, beneficios y desafíos percibidos por los  
docentes respecto al uso de la inteligencia artificial (IA) en  
la educación entre 2020 y 2024. Se utilizó el modelo  
PRISMA para seleccionar un total de 53 estudios que  
cumplen con los criterios de inclusión. Los datos fueron  
extraídos y evaluados para sintetizar las percepciones de  
los docentes, las ventajas pedagógicas y administrativas de  
these systems remains  
a
“black box”, generating  
automated responses whose underlying logic is opaque.  
This perception influences confidence and strategies for  
integrating AI into the classroom. Through a systematic  
literature review, this article examines how teachers  
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perceive these technologies, the specific benefits they see  
in their implementation, and the obstacles they face in  
adopting them.  
They evidence the literature a consensus on the  
positive impact of AI on learning personalization and  
administrative optimization. However, ethical, technical,  
and pedagogical limitations underscore the importance of  
continued research and integrative policies to ensure  
effective and equitable implementation in diverse  
educational contexts. This literature review constitutes a  
solid framework to contextualize the findings of this  
systematic review and its contribution to current  
knowledge at the intersection between AI and education.  
The relevance of this work lies in the need to have a  
clear and updated overview of the impact of AI on teaching  
practice. Understanding teachers’ perceptions and  
experiences is essential to design policies and training  
programs that allow them to maximize the benefits of AI  
and address its challenges. In turn, the findings of this  
review can guide educational AI researchers and  
developers in creating tools that respond to the specific  
needs of the classroom.  
Objective  
State of the Art  
The main objective of this review is to systematically  
analyse teachers' perspectives on the use of AI in  
education, the benefits they perceive in its implementation  
and the challenges they face. This review specifically  
addresses the following research questions:  
Research on artificial intelligence in education  
(
AIEd) has seen significant growth over the past two  
decades. This advancement reflects its potential to  
personalize learning, optimize administrative tasks, and  
transform pedagogical practices. According to the review  
by Chen et al. (2020), AI technologies, such as intelligent  
tutoring systems and chatbots, have been instrumental in  
facilitating personalized teaching and automating  
administrative functions, improving the quality of  
learning.  
What are teachers' perceptions and attitudes  
towards the use of AI in education?  
What benefits of AI are reported in teaching  
practice?  
What are the challenges in implementing AI in the  
educational field?  
A mapping of the evolution of knowledge in AIEd  
highlights the central role of technologies such as natural  
language processing, educational data mining, and deep  
learning, according to the literature review by Feng and  
Law (2021). These techniques have been extensively  
applied in personalized learning design, adaptive  
assessments, and intelligent tutoring systems, key areas in  
the transformation of education.  
Systematic reviews also underline the need to  
critically assess the ethical and practical challenges in  
implementing AI. Zawacki-Richter et al. (2019) point out  
that ethical risks, such as algorithmic bias and lack of  
transparency, limit the adoption of these technologies in  
educational contexts, especially in higher education.  
In the context of early education, Crescenzi-Lanna  
METHODS  
This systematic review was conducted using the  
PRISMA (Preferred Reporting Items for Systematic  
Reviews and Meta-Analyses) framework, which is  
recognized for its rigor in literature synthesis studies. To  
ensure the relevance and timeliness of the findings,  
inclusion criteria were established that restricted the  
selection to studies published between 2020 and 2024.  
This period was chosen due to the increase in publications  
on AI applied to education in recent years, which is  
considered a response to the accelerated advance of digital  
technologies and their integration into the educational  
environment. The studies had to directly address the  
implementation, benefits, challenges, or perspectives of  
teachers on the use of AI in educational contexts, from  
primary to higher levels, with an emphasis on pedagogical  
applications. Likewise, only research from indexed  
scientific journals was included, in order to ensure a high  
standard of quality and reliability. Studies published  
before 2020, as well as those that focused exclusively on  
the technical development of AI without a direct  
connection to its pedagogical use. The search strategy was  
rigorously designed to cover a wide variety of well-known  
academic databases in the educational and technological  
fields, including Scopus, ERIC, Web of Science, Scielo,  
and Redalyc. To ensure the exhaustiveness and accuracy  
of the results, structured search formulas were used that  
combined relevant key terms using Boolean operators.  
These formulas included specific combinations such as  
(
2022) highlights that, although AI applications have a  
positive impact on teaching, there are concerns about data  
privacy and the effects on human interaction in educational  
settings. This reinforces the need for ethical and  
pedagogical approaches in the design and implementation  
of AI systems in early childhood education.  
On the other hand, Talan (2021) conducted a  
bibliometric study that identifies trends in AIed,  
highlighting that most research is concentrated in countries  
such as the United States, the United Kingdom, and  
Taiwan. This analysis highlights the importance of  
interdisciplinary approaches that include pedagogical,  
ethical, and technological perspectives to address  
limitations in access and adoption of these tools.  
In the field of 21st century skills education,  
Trisnawati et al. (2023) argue that AI has the potential to  
enhance competencies such as critical thinking and  
collaboration. However, they warn that its excessive use  
could reduce students' ability to think independently,  
underlining the need for a balance between human-  
machine interaction.  
“
artificial intelligence in education” AND “teachers’  
perspectives on AI” OR “AI challenges in education.” In  
addition, variations in the key terms were used to address  
synonyms and related concepts, such as “AI in teaching”  
OR “educational impacts of AI,” depending on the search  
fields available in each database.  
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Each search formula was adjusted to fit the specific  
functionalities of the different databases, respecting their  
syntax and order. For example, in databases such as  
Scopus and Web of Science, the use of connectors such as  
AND, OR and parentheses was prioritized to structure  
complex queries, while in regional databases such as  
Scielo or Redalyc, searches were simplified with exact  
phrases to capture relevant publications in Spanish. Field  
filters such as “title,” “abstract,” and “keywords” were also  
included to optimize the relevance of the results.  
validity and applicability of qualitative and quantitative  
research. Each study was assessed for the clarity and  
consistency of its design, the transparency of the data  
collection and analysis process, and the appropriateness of  
the conclusions based on the data presented. In addition,  
special attention was paid to the possible presence of  
biases, both in the selection of the sample and in the  
interpretation of the results.  
RESULTS  
The initial search yielded a total of 418 potential  
articles. To ensure thematic relevance, additional language  
filters were applied, limiting the results to publications in  
English and Spanish, which represented the languages  
handled by the review team. This multilingual approach  
allowed research from various regions to be captured,  
broadening the geographic scope, but ensuring linguistic  
consistency for the interpretation and detailed analysis of  
the texts.  
Finally, the search formulas and specific criteria were  
fully documented to ensure replicability and transparency  
in the methodology, allowing future researchers to  
reproduce the process or adapt it to new research  
approaches.  
Overview of Studies  
The final sample of 53 studies on the  
implementation of AI in education spans a period  
between 2020 and 2024, covering a notable geographic  
diversity, with research coming from North America,  
Europe, Asia, and Latin America. The most represented  
countries in these studies include the United States,  
China, and European countries, such as Germany and  
the United Kingdom (Table 1). However, contributions  
from emerging regions in the field of educational AI,  
such as the Middle East (United Arab Emirates) and  
Africa (Nigeria), also stand out, suggesting a global  
interest in the adoption and study of AI in different  
educational and socioeconomic contexts.  
The majority of studies (39%) use quantitative  
methodologies, such as structured surveys and advanced  
statistical analysis, to measure factors such as  
acceptance, willingness, and self-efficacy in the use of  
artificial intelligence (AI) in education (Table 2).  
Qualitative approaches (35%) employ interviews and  
focus groups to explore subjective experiences and  
perceptions, including ethical and pedagogical  
challenges.  
Eighteen percent opt for mixed methodologies that  
combine quantitative and qualitative data, while 8% rely  
on iterative designs such as design-based research  
(DBR) to develop AI-related curricula. This  
methodological diversity reflects an interest in  
integrating objective and subjective analyses to address  
both the practical impacts and contextual challenges of  
AI in educational settings.  
In terms of objectives, most studies explore two  
main lines: (1) teachers’ perceptions and attitudes  
towards AI as an educational tool and (2) the factors that  
facilitate or hinder its implementation in the classroom.  
Some studies stand out for focusing on specific  
contexts, such as STEM education (science, technology,  
engineering and mathematics) and language teaching,  
where AI is used to personalize and enhance the learning  
of technical and linguistic skills. Particular approaches  
to equity and ethics in the use of AI are also identified,  
especially in studies from Europe and the United States,  
indicating a concern about the social and ethical impacts  
of AI in education.  
The study selection process followed the four stages  
established by the PRISMA framework: identification,  
screening, eligibility and inclusion. In the first stage, 418  
potential studies were identified using the search terms  
previously mentioned. Subsequently, 145 duplicate studies  
were eliminated and the remaining 273 were screened,  
reviewing their titles and abstracts to determine their  
alignment with the review objectives. This phase allowed  
us to discard studies that did not specifically address AI in  
education, systematic reviews and meta-analyses, or that,  
although they mentioned the topic, focused on areas  
outside of teaching practice, such as the design of  
algorithms or the analysis of large volumes of educational  
data without a practical application in the classroom. At  
the end of the screening, 113 studies were obtained that  
advanced to the eligibility stage. In this third phase, the  
full texts of the studies were reviewed to verify that they  
met the inclusion criteria in an exhaustive manner, leaving  
a total of 53 studies for final inclusion and in-depth  
analysis of their findings.  
Data extraction was carried out using a structured  
form that allowed the key information of each selected  
study to be captured and organized. This form included  
details such as the author and year of publication, the main  
objective of the research, the methodology used (whether  
qualitative, quantitative or mixed), and the main findings  
in relation to the implementation of AI in educational  
contexts. Attention was also paid to the limitations  
recognized by the authors themselves, such as the sample  
size, the geographical context, and the generalization of  
the results, which facilitated a critical interpretation of the  
findings. This systematic extraction stage was carried out  
thoroughly, allowing the data to be organized in a  
homogeneous manner and providing a solid basis for the  
comparative analysis of the different investigations.  
To assess the methodological quality of the studies,  
the Critical Appraisal Skills Programme (CASP) guide was  
used, which offers a rigorous approach to analyzing the  
This exhibition provides  
a
comprehensive  
overview of current trends in the use of AI in education,  
highlighting both the predominant methodological  
approaches and the thematic and regional areas of  
greatest interest over the past five years.  
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Table 1. Countries presenting research on the use and perceptions  
Table 2. Designs, methods, instruments and samples of scientific  
of AI among teachers  
literature  
Number  
of Studies  
Design Methods  
Instrument  
Sample  
%
Country  
Authors  
-
Descriptive  
-Online surveys  
-Likert scales  
-Questionnaires  
Kim y Kim (2022), Antonenko y  
3
3
–
164  
Quanti  
Quali  
Mixed  
DBR  
-Experimental  
39%  
Abramowitz (2023), Lee & Perret (2022),  
Williams et al. (2021), Wood et al. (2021),  
Ottenbreit-Leftwich et al. (2023), Kaplan-  
Rakowski et al. (2023)  
-Correlational  
EE.UU.  
7
-Thematic  
analysis  
-Discourse  
analysis  
-Interviews  
-Open-ended  
questions  
5 – 276  
8 – 800  
35%  
18%  
Yau et al. (2023), Chiu et al. (2022), Chiu  
Hong Kong  
Spain  
4
4
(2021), Wang & Cheng (2021)  
-Focus groups  
-
-
Structured surveys  
Semi-structured  
Leoste et al. (2021), Dúo et al. (2023), De  
Vega-Martín et al. (2022), Delgado de  
Frutos et al. (2024)  
-Sequential  
-Explanatory  
-Exploratory  
interviews  
-
Pre/Post tests  
-Observations  
Participant  
feedback  
Tobar et al. (2024), Morocho Cevallos et  
al. (2023), Apolo et al. (2024), Jara (2024)  
-Iterative testing  
-Curriculum  
development  
Ecuador  
4
~18  
8%  
-
An et al. (2023), Qin et al. (2020), Wang  
et al. (2023)  
China  
3
3
Teachers' perspectives on AI  
Gocen & Aydemir (2020), Sabuncuoglu  
Türkiye  
(2020), Hopcan et al. (2024)  
Analysis of teachers’ perceptions reveals a wide  
range of attitudes towards AI in education, marked by  
diversity in pedagogical beliefs, level of familiarity  
with the technology, and context of application (Table  
Lindner & Berges (2023), Zhang et al.  
2023)  
Germany  
India  
2
2
(
3
). Overall, teachers recognize the potential of AI to  
Joshi et al. (2021), Kashive et al. (2020)  
Al Darayseh (2023), ElSayary (2023)  
Sumakul et al. (2022), Pratama et al.  
improve teaching and facilitate personalized learning.  
This perception is especially found in studies analyzing  
its impact in STEM areas and language teaching, where  
AI supports teachers in personalizing tasks and  
scaffolding complex content (Kim and Kim, 2022; An  
et al., 2023).  
However, a significant proportion of teachers  
express concerns about ethics and equity in the use of  
AI. These concerns focus on the risk of bias in  
algorithms and technological dependence, which could  
shift the teaching role towards more technical tasks.  
This tension between enthusiasm for AI and ethical  
concerns is particularly evident in studies conducted in  
higher education contexts and in countries with  
advanced technological regulations, such as the United  
States and Europe (McGrath et al., 2023; Nazaretsky et  
al., 2022).  
United Arab  
Emirates  
2
Indonesia  
2
2
2
2
(2023)  
United  
Kingdom  
Cukurova et al. (2019), Kaplan-Rakowski  
et al. (2023)  
South Korea  
Israel  
Choi et al. (2022), Yang (2022)  
Nazaretsky et al. (2022), Nazaretsky et al.  
(2021)  
Bulgaria,  
Greece, Italy,  
Romania  
1
Polak et al. (2022)  
Differences in AI adoption and perception also  
reflect teachers’ pedagogical beliefs. Those with  
constructivist approaches show a greater willingness to  
integrate AI as a pedagogical tool, while teachers with  
more transmissive views show resistance due to the  
perception of AI as a replacement for their role in the  
classroom (Choi et al., 2022). Additionally, AI  
acceptance levels are closely linked to factors such as  
self-efficacy and technological familiarity, which  
underlines the need for specific professional  
development programs that improve teachers’  
technological competencies (Al Darayseh, 2023;  
Antonenko and Abramowitz, 2023).  
Estonia  
Sweden  
Nigeria  
Belgium  
Thailand  
Mexico  
Africa  
1
1
1
1
1
1
1
1
1
1
Chounta et al. (2021)  
McGrath et al. (2023)  
Ayanwale et al. (2022)  
Henry et al. (2021)  
Boonmoh et al. (2021)  
Salas-Rueda et al. (2022)  
Sanusi et al. (2022)  
Palestine  
Grecia  
Abdelmoneim et al. (2024)  
Mystakidis & Christopoulos (2022)  
Sosa et al. (2024)  
Paraguay  
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teaching, where AI tools can offer expert models and  
real-time feedback to improve students’ skills (Kim  
and Kim, 2022; Sumakul et al., 2022). This  
personalization helps maintain motivation and  
promotes more meaningful and effective learning by  
adjusting the pace and level of complexity of the  
content to each student.  
Second, AI has been shown to be an ally in  
performing administrative tasks. Tools such as AI-  
Grader allow teachers to automate assessment tasks,  
freeing up time for them to focus on more complex  
pedagogical activities (Nazaretsky et al., 2022). This  
automation also contributes to reducing the margin of  
error and improving assessment accuracy, which is a  
key benefit in contexts with high volumes of students  
or assignments. In addition, AI can facilitate lesson  
planning and organization, helping teachers design  
programs and activities that respond more effectively  
to classroom needs (ElSayary, 2023).  
Finally, scaffolding in complex content is another  
of the highlighted benefits. AI provides reasoning  
models and examples that allow students to approach  
difficult topics with greater support, especially in areas  
such as scientific writing and the development of  
critical thinking skills (Kim and Kim, 2022; McGrath  
et al., 2023). This support contributes to strengthening  
the understanding of advanced topics and developing  
higher cognitive skills in students. However, some  
teachers highlight the need to balance this automated  
support with human guidance to avoid excessive  
dependence on technological tools.  
Table 3. Teachers' perspectives on AI in education  
Category  
Characteristics  
Studies  
Teachers see AI as a tool that  
facilitates personalized  
learning and administrative  
support.  
Kim and Kim  
Optimism and  
Potential  
(2022); Sumakul et  
al. (2022)  
Ethical and  
Equity  
Concerns  
Fears about biases,  
technological dependency,  
and loss of the teacher's role.  
McGrath et al.  
(2023); Nazaretsky  
et al. (2022)  
Teachers with constructivist  
approaches show greater  
acceptance, while those with  
transmissive approaches are  
more skeptical.  
Influence of  
Pedagogical  
Beliefs  
Choi et al. (2022);  
Yau et al. (2023)  
Al Darayseh (2023);  
Antonenko and  
Abramowitz (2023);  
Lindner and Berges  
The lack of digital skills  
limits AI adoption; the need  
for continuous training is  
highlighted.  
Need for  
Training and  
Support  
(2023)  
Disparities in access to  
technology and institutional  
support limit AI integration  
in certain contexts.  
Regional  
Inequalities  
Sanusi et al. (2022);  
Apolo et al. (2024)  
Benefits of AI in teaching practice  
The implementation of AI in the educational field  
has been perceived as valuable tool for the  
a
personalization and efficiency of teaching and learning  
processes. In the reviewed studies (Table 4), the  
benefits of AI in education are grouped into three main  
areas: personalized learning, support in administrative  
tasks, and scaffolding in complex content.  
Challenges in AI implementation  
Implementing AI in education faces several  
challenges, ranging from a lack of teacher training to  
ethical concerns and technical limitations. These  
obstacles reflect both the level of teacher preparation  
and the complexity of the technological and regulatory  
environments in which AI is embedded (Table 5).  
Table 4. Benefits of AI in teaching practice  
Category  
Description  
Studies  
Table 5. Challenges in implementing AI in education  
AI allows the adaptation of  
content and methods to the  
individual profile of each  
Kim and Kim  
(2022); Sumakul  
et al. (2022);  
Personalized  
Learning  
student, improving motivation Pratama et al.  
Challenge  
Description  
Studies  
and learning effectiveness.  
(2023)  
Teachers lack technical and  
pedagogical skills to use AI,  
limiting its effective  
integration.  
Chounta et al.  
Lack of  
Training  
AI tools automate tasks such  
as assessment and planning,  
freeing up time for  
(2021); Polak et  
Support for  
Administrative  
Tasks  
Nazaretsky et al.  
(2022); ElSayary  
(2023)  
al. (2022)  
pedagogical activities.  
Concerns about privacy, equity, McGrath et al.  
Ethical  
Concerns  
and bias in AI algorithms,  
which could perpetuate  
inequalities.  
(2023); Akgun  
and Greenhow  
(2022)  
AI facilitates access to  
advanced knowledge through  
reasoning models and  
Scaffolding in  
Complex  
Content  
Kim and Kim  
(2022); McGrath  
et al. (2023)  
individualized examples.  
Lack of infrastructure,  
resources, and connectivity in  
certain regions limits AI use in  
education.  
Morocho Cevallos  
et al. (2023);  
Sanusi et al.  
(2022)  
Technical  
Limitations  
First, one of the most frequently mentioned  
benefits is AI’s ability to personalize learning,  
adapting content and methods to each student’s  
individual needs. AI facilitates the creation of  
differentiated learning paths, which is particularly  
useful in STEM education contexts and language  
Skeptical attitudes towards AI,  
Joshi et al. (2021);  
Delgado de Frutos  
et al. (2024)  
Resistance to based on perceptions of threats  
Change  
to teaching and  
dehumanization.  
https://doi.org/10.5281/zenodo.14509895  
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applicability of findings to other educational contexts,  
as noted by Choi et al. (2022) and Al Darayseh (2023).  
This limitation is especially problematic in research  
where teachers come from a single discipline, which  
reduces the possibility of applying the results to  
teachers from other areas.  
One of the most common challenges is the lack of  
teacher training. Most teachers lack the technical skills  
necessary to use AI tools in the classroom, which limits  
their ability to effectively integrate this technology  
(
Chounta et al., 2021; Polak et al., 2022). This is  
directly related to the scarcity of specific AI training  
programs, which not only develop technical skills but  
also address pedagogical and ethical aspects associated  
with the technology.  
In addition, there are serious ethical concerns  
among teachers, mainly related to data privacy and  
equity in the use of AI. Many teachers fear that AI may  
compromise student privacy and increase inequalities  
by favoring those with better access to technology  
Table 6. Limitations of studies on AI in education  
Type of  
Limitation  
Description  
Studies  
Small samples limit the  
generalization of  
findings to larger  
populations.  
Chounta et al.  
(2021);  
Nazaretsky et  
al. (2022)  
Small Sample  
Size  
(
McGrath et al., 2023; Akgun and Greenhow, 2022).  
These concerns also include the risk of bias in AI  
algorithms, which could perpetuate stereotypes and  
unfair practices in the classroom. Another prominent  
challenge is technical limitations, such as a lack of  
infrastructure and resources in many educational  
centers. In regions with less access to technology, such  
as certain areas of Latin America and Africa, the lack of  
connectivity and adequate equipment makes it difficult  
to implement AI tools (Morocho-Cevallos et al., 2023;  
Studies limited to a  
specific region or  
Representativity discipline restrict the  
applicability of results.  
Choi et al.  
(2022); Al  
Darayseh  
(2023)  
Lack of  
Use of self-reported  
Jara (2024);  
Apolo et al.  
Limited  
Generalization  
methods and online  
surveys can introduce  
biases into results.  
(
2024)  
Focusing on a single AI  
tool limits understanding  
of its use in different  
Sanusi et al., 2022). This creates  
a
significant  
Nazaretsky et  
al. (2022);  
Celik (2023)  
Tool Diversity  
technological gap that limits the potential of AI to  
contribute to inclusive education.  
contexts and disciplines.  
Finally, resistance to change is a relevant obstacle.  
Some teachers, especially those with more traditional  
pedagogical approaches, show a skeptical attitude  
towards AI, seeing it as a threat to their role in the  
classroom. This resistance is based on the belief that  
technology can dehumanize teaching and replace direct  
interaction between teachers and students (Joshi et al.,  
Limited generalizability is also affected by the use  
of self-reported methods and online surveys, which,  
although useful for collecting data quickly, can  
introduce significant biases into the results. Studies  
such as Jara’s (2024) rely on self-reported data, which  
can influence the accuracy of the recorded perceptions,  
especially in relation to the acceptance and  
understanding of AI.  
Finally, there is a lack of diversity in the AI tools  
investigated. Some studies focus on a single tool or a  
single type of technology, limiting the understanding of  
AI as a whole in education. For example, Nazaretsky et  
al. (2022) exclusively study the AI-Grader in the context  
of biology, which restricts the applicability of the  
results to other areas or types of AI.  
2
021; Delgado de Frutos et al., 2024). These challenges  
underscore the need for a comprehensive approach to AI  
adoption in education, including ongoing training,  
technical support, security and transparency measures,  
and strategies to address ethical and cultural concerns.  
Limitations of the Studies  
The review of studies on the implementation of AI  
in education reveals several limitations that affect the  
representativeness and generalizability of the results  
These limitations highlight the need for future  
research  
with  
more  
representative  
samples,  
(
Table 6). First, the small sample size is a common  
methodologies that include geographic and disciplinary  
diversity, and the study of a broader range of AI tools  
to obtain more robust and generalizable conclusions  
about their impact in education.  
limitation in multiple studies. Research such as Chounta  
et al. (2021) and Nazaretsky et al. (2022) present small  
samples in relation to the total number of teachers,  
which restricts the ability to extrapolate their findings  
to broader populations.  
This problem is exacerbated in studies carried out  
in specific contexts, such as in Turkey or the United  
Arab Emirates, where the sample size is insufficient to  
represent the diversity of educational experiences in  
these countries.  
Another obstacle is the lack of geographical and  
disciplinary representativeness. Many studies focus on  
very particular educational regions or contexts, such as  
certain disciplines (e.g., STEM) or specific geographic  
areas (e.g., South Korea, Nigeria). This limits the  
DISCUSSION  
Analysis of teachers’ perceptions of artificial  
intelligence (AI) in education reveals a generally  
positive assessment of its potential, especially in terms  
of learning personalization and administrative support.  
These findings are consistent with previous research,  
such as Chen et al. (2020), which documented how  
intelligent systems in education enable real-time content  
adaptation, optimizing teaching-learning processes and  
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improving knowledge retention in key areas such as  
science and mathematics. Furthermore, Polak et al.  
et al. (2019) highlighted a persistent disconnect between  
the development of AI technologies and the pedagogical  
preparation of teachers to use them, reinforcing the need  
to design training programs that integrate both technical  
and ethical aspects. These programs could not only  
address current limitations, but also strengthen  
teachers’ trust in AI as an educational tool.  
(
2022) highlighted a favorable attitude among teachers  
toward teaching digital skills using AI tools, although  
they also pointed out limitations in advanced technical  
skills, underlining the need for additional training.  
However,  
alongside  
perceived  
benefits,  
fundamental ethical dilemmas arise that require further  
attention. Teachers express concerns about inherent bias  
in AI algorithms, privacy of student data, and  
technological dependency. These dilemmas not only  
affect the acceptance of AI, but could delimit what is  
socially permissible and ethically accepted within the  
educational field. Ahmad et al. (2021) highlighted that  
AI algorithms can perpetuate inequalities if not  
managed properly, posing serious risks to educational  
equity. This type of bias could discourage teachers and  
educational communities from adopting AI, especially  
in contexts where justice and equality are central values.  
In this sense, ethics can constitute both a regulatory  
boundary and a starting point for new prospective  
research. The inclusion of clear ethical frameworks in  
the implementation of AI can help build trust among  
teachers, guiding them towards responsible and  
sustainable uses. These frameworks should address key  
questions: What type of data is acceptable to collect?  
How is transparency guaranteed in AI decision-making  
processes? And, above all, what mechanisms can be  
implemented to avoid the amplification of existing  
inequalities? This forward-looking approach, in  
addition to strengthening trust in technology, could  
serve as a model for other areas where AI is being  
integrated.  
On the other hand, the diversity in teachers'  
pedagogical beliefs also significantly influences their  
willingness to adopt these technologies. Teachers with  
constructivist orientations see AI as a complementary  
tool that enables the co-creation of knowledge and  
encourages critical thinking. Goksel and Bozkurt (2019)  
emphasize that this constructivist perception facilitates  
the integration of AI, while teachers with more  
traditional approaches may see it as a threat to the  
human role in the classroom. This contrast underlines  
the importance of addressing these differences through  
professional development programs that, in addition to  
teaching technical skills, contextualize AI within  
inclusive pedagogical frameworks.  
Although teachers’ perceptions towards AI are  
mostly positive, the ethical dilemmas and practical  
challenges associated with its adoption cannot be  
ignored. Ethics should not only be seen as a limit that  
restricts the use of AI, but as a guide for its responsible  
implementation. This approach could set the foundation  
for a more equitable and effective adoption of AI in  
education, turning current dilemmas into opportunities  
to strengthen both teaching practice and trust in these  
emerging technologies.  
Limitations of the study  
This literature review, although exhaustive in its  
analysis of recent studies on AI in education, presents  
some limitations inherent to the selection process and  
the scope of the included articles. First, the review was  
restricted to studies published between 2020 and 2024,  
which could have excluded previous relevant research  
that provides background or long-term trends in the use  
of educational AI. Furthermore, although a rigorous  
search strategy was used, some articles may not have  
been considered if they were not indexed in the  
reviewed databases. Another limitation is the reliance  
on studies with varied methodologies and, in some  
cases, small samples and geographically specific  
contexts, which limits the representativeness of the  
findings. Finally, this review is subject to the  
interpretation of the results according to the established  
categories, which could introduce a bias in the synthesis  
of findings due to the subjectivity in the classification  
and grouping of research.  
CONCLUSION  
This systematic review on the use of AI in  
education allows us to synthesize the main perspectives,  
benefits, and challenges perceived by teachers in the  
implementation of this technology. The findings  
highlight a growing interest in AI as an educational tool  
that facilitates personalized learning, optimizes the  
management of administrative tasks, and provides  
support in the learning of complex content. However,  
significant challenges persist, such as a lack of teacher  
training, ethical concerns about privacy and equity,  
technological infrastructure limitations in some regions,  
and resistance to change by some educators.  
This review underlines the need to develop  
comprehensive educational policies that facilitate  
access to AI technologies and address ethical and equity  
aspects to create inclusive and sustainable learning  
environments. Likewise, the importance of continuous  
and specific training for teachers is fundamental to  
maximize the potential of AI in education and ensure its  
effective and responsible implementation.  
The administrative benefits of AI are also  
highlighted, especially in the automation of tasks such  
as assessment and planning, allowing teachers to spend  
more time on strategic pedagogical activities. Ahmad et  
al. (2022) noted that tools such as learning analytics and  
automated assessment  
systems optimize time  
management, while contributing to improving  
educational quality by offering more accurate and real-  
time  
feedback.  
However,  
this  
administrative  
optimization must be balanced with the need to maintain  
the centrality of teachers in pedagogical decision-  
making.  
Despite these advantages, challenges related to  
teacher training and technical limitations in specific  
contexts represent significant barriers. Zawacki-Richter  
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Although this review provides an updated analysis,  
future research should expand the diversity of contexts  
and AI tools studied, as well as consider more  
representative and longitudinal samples to strengthen  
the validity of the findings. Ultimately, the success of  
AI in education will depend on a balanced integration  
that respects and complements the irreplaceable role of  
the teacher in the teaching-learning process.  
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