INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)  
ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue X October 2025  
Perceptions On Utilisation of Artificial Intelligence Among Secondary  
School Agriculture Teachers in Eswatini  
Nokuthula Ngabisa Dlamini1, Vincent Chidindu Asogwa2  
Department of Agricultural Education and Extension, Faculty of Agriculture, University of Eswatini  
Manzini, Eswatini, Swaziland  
DOI: https://doi.org/10.51584/IJRIAS.2025.10100000207  
Received: 11 November 2025; Accepted: 18 November 2025; Published: 26 November 2025  
ABSTRACT  
The integration of artificial intelligence (AI) in education, particularly in applied science subjects like  
agriculture has emerged as a transformative force bringing customised learning, simulations and real-time  
feedback. Despite its potential, the adoption of AI tools in the teaching of agriculture at secondary school level  
in Eswatini remains limited due to factors such as, lack of awareness, inadequate resources and inefficient  
teacher training on the use of AI. This research examined agriculture teachers’ perceptions on the utilisation of  
AI for teaching agriculture at secondary school in Eswatini. A survey design was used with a sample of 94  
agriculture teachers from the Manzini Region of Eswatini. Data were collected with a structured questionnaire  
and were analysed using descriptive statistics. Findings revealed a low level of teacher knowledge regarding  
AI tools in the teaching of agriculture. Most teachers lack familiarity with AI application, including Nomfundo  
AI which was launched by the government of the Kingdom of Eswatini through the Ministry of Education and  
Training. The perceptions of AI’s potential benefit, however, was generally positive, with teachers recognising  
the value of AI in enhancing student engagement and offering personalised learning experiences. The research  
revealed that there is need for targeted professional development programmes to improve agriculture teachers’  
understanding and application of AI in teaching of the subject. This study contributes to the broader discourse  
on AI’s role in agricultural education, especially in a resource limited country like Eswatini, and it further  
provides insights for policy and practice to optimize the use of AI in agricultural education.  
Keywords: Nomfundo AI, AI tools, AI technologies, agricultural education, agriculture teacher, professional  
development  
INTRODUCTION  
Society’s expectation of teacher’s is that they hold the ability to maintain good teaching practices and stay  
relevant within the ever evolving education system and world. Artificial intelligence (AI) has gained attention  
as an innovative technology which has the potential to transform most aspects of society or industries (Binns,  
2017; Aguolu, 2019; & Flavian & Casalo, 2021), including education (Kukulska-Hulme, 2020; Olaogun &  
Oyediran 2023). According to Olaogun and Oyediran (2023), this technology can potentially improve  
efficiency and reduce burdens placed on teachers. This technology bears most benefits (Hutchins, 2021),  
however, they may also have negative consequences in educational settings (Kautz et al., 2021), especially  
with secondary school learners. Another factor not to be overlooked, is that teachers are responsible for  
preparing students to enter a workforce where AI utilisation will become inevitable as an essential innovation.  
Without the skills to effectively integrate IA into teaching and learning process, teachers may compromise the  
quality of education they provide to their clients.  
This study was necessitated by the realisation that AI, in its early stages, is casting a veil of uncertainty over  
the education field, catching teachers and stakeholders off guard. According to Dusseault and Lee (2023),  
specifically, in secondary school agricultural education, there is lack of understanding of what teachers know  
about AI, their beliefs and attitudes toward it and how they currently integrate it into their teaching practices of  
agriculture. The study addresses these unknowns associated with AI and its position in secondary school  
agricultural education in Eswatini. The study further provides recommendations for professional development.  
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Findings from this study can help the Ministry of Education and Training (INSET Department), institutions of  
higher education where agriculture teachers are trained and the teachers to create professional development  
policies for managing AI ethically and effectively.  
1.1  
Statement of the Problem  
Despite the significance of AI in personifying, improving engagement and efficiency in teaching and learning,  
it still shows that there are still some lapses with regards to teacher utilisation of AI. The UNESCO framework  
on Gen AI in education provide a clear guide to controversies that surround generative AI and ethical  
educational considerations. The framework emphasises human agency, inclusion, equity and cultural diversity  
(Blonder & Feldman-Maggor, 2024). This development could be caused by the level of knowlegde and  
readiness to integrate AI into education practices (Amoah et al., 2020). As highlighted in literature, efforts to  
incorporate AI into teaching and learning have been made, but, the success rate of implementation on new  
instructional technologies is closely related to the attitudes of the educators who lead the lessons. Teachers’  
perceptions of utilisation of AI have been studied by very few scholars who revealed that teachers lack  
awareness and experience with regards to the use of AI tools for teaching. The government of Eswatini is  
making efforts to incorporate ICT and AI to education, but the adoption rate is unknown. Some teachers lack  
knowledge of how and where these AI tools could be accessed and even applied to guide their classroom  
instruction.  
1.2  
Purpose and objectives  
The purpose of the study was to determine perceptions on utilisation of AI technology among secondary school  
agriculture teachers in Eswatini. The aim extends even into identifying professional development needs of the  
teachers regarding the use of AI and objectives were to:  
1. describe the current utilisation of AI in secondary school’s agricultural education in Eswatini,  
2. describe the perceptions of agriculture teachers regarding utilisation of AI in teaching agriculture at  
secondary school in Eswatini,  
3. identify professional development needs of agriculture teachers regarding the use of AI for secondary  
school agricultural education in Eswatini,  
4. compare current utilisation of AI by the agriculture teachers with their demographic characteristics.  
1.3  
Limitations  
The sample for this study was small compared to the population of agriculture teachers in Eswatini. The  
geographic area covered included only schools in the Manzini Region, which can be extended to the other  
three regions of the Kingdom. The data collected for this study were through a hand delivered questionnaire,  
other means and tools can also be considered.  
Literature Review and Theory relevant to the study  
2.1  
Utilisation of AI by schools in Eswatini  
AI has been adopted in some African education systems to assist learning and as an instructional course. For  
example, a Namibian study by Shipepe et al. (2021) showed that a course had been designed to include African  
indigenous knowledge as part of problem solving. While such studies have illustrated how AI is embraced for  
course development to support teaching and learning, there is still insufficient publication evidence on its use  
in agricultural education at secondary school level in Eswatini. Cisse (2018)’s work emphasises that diversity  
plays a pivotal role in the use of AI. However, a bulk of AI expertise in concentrated in North America, Europe  
and Asia, with limited representation from Africa. Consequently, broadening the geographic scope of AI  
research and fostering diversity within the field of AI technology can serve as a vital strategy to mitigate  
challenges and enhance AI adoption and utilisation.  
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Literature provides evidence that AI at secondary school level has already been adopted. For example, the  
Ministry of education and Training (MoET) has explicitly included AI integration in its education reform  
agenda where, entailed is the development of a four-year secondary school education programme. This  
programme combines AI, modern infrastructure and competency-based learning. According to MoEt (2025),  
this reform aims to support teachers with technology and provide students with individualised and adaptive  
learning experiences. MoET also pledged to support the integration of ICT and AI in both traditional and  
distant learning approaches. This reflects a strategic commitment to digital transformation in education.  
There are support initiatives in place to accelerate AI adoption in the country. Eswatini has established several  
rapports with international bodies to support AI education. For instance, the Kingdom of Eswatini collaborates  
with big institutions such as Google to drive digital transformation, youth empowerment and digital skills  
development which include access to AI powered educational tools and teachers training (Google Africa,  
2024). When it comes to curriculum development and teacher training there is a partnership between  
Government, United Nations Development Programme (UNDP) and the University of Eswatini (UNESWA) to  
promote AI in research and education (UNESWA AI Academy, 2025). To add on these, there is an initiative  
known as AI Indaba, organised by United Nations and the Ministry of ICT. This initiative’s aim is to build AI  
skills among youth, including secondary school students. These trainings are done through workshops and  
awareness campaigns (United Nations Eswatini, 2024). Currently, there are AI tools and platforms that are  
used at secondary school in Eswatini. One example of such is “Nomfundo AI” a digital education assistant that  
is used in Eswatini education system. As presented in this chapter, a lot has been done in terms of AI  
implementation, but literature indicates that there is no much teacher engagement for the adoption of IA. The  
issue is, the AI tools are already there in the education sustyem. For them to be appreciated by the end users  
(teachers and students), they need to be accepted. Acceptance of any innovation depends on attitude and  
perception about the change. This study was necessary to determine the perceptions of agriculture teachers  
regarding the use of AI for teaching and further predict the adoption rate of this innovation in the education  
sector.  
2.2  
Teacher training and development needs for the use of AI  
The training of teachers in emerging technologies is very crucial for successful adoption and utilisation in  
education. Several initiatives to train teachers in AI and digital skills have been launched. For example, in June  
2025, over 100 teachers participated in a week long generative AI training workshop (UNESWA AI Academy,  
2025) to leverage AI in online and distance learning. There are ongoing programmes for digital skills and AI  
training for education inspectors and education leaders (UNESCO IICCBA & KIX Africa 19Hub, 2024).  
According to UNESCO IICCBA & KIX Africa 19Hub, all national dialogues emphasise the necessity for  
continued professional development in digital and AI literacy and integration in education.  
Some schools actively use AI technologies in the classroom. According to VOA Africa (2024), research  
highlighted that there is student engagement and some teachers’ resistance in the utilisation of AI for teaching  
and learning. This indicates the real world implementation and the challenges that are faced with regards to the  
use of AI in education. Dladla et al. (2025) revealed that in the Lubombo Region, academic research  
documented the use of AI to integrate indigenous knowledge systems into science education. This enhances  
cultural relevance and student engagement. Dladla emphasised the importance of this approach in personifying  
what is taught to secondary school learners in various subjects. Dladla also explored the integration of AI and  
indigenous knowledge in science education. The study emphasised the potential for AI to create inclusive and  
culturally relevant learning environment. Evidence is contained by certain publications such as VOA (2024)  
and Eswatini Observer (2025). These publications have reported on the adoption of AI in secondary schools,  
government investments in AI powered teachers trainings and the broader impact of AI in education in  
Eswatini. As much as Eswatini is making significant progress, there are challenges that remain still. These  
challenges include teacher resistance, the need for ongoing in-service training for teachers and infrastructure  
limitations especially in schools that are located in rural areas (VAO). But with such a study to solicit teachers’  
perceptions of the use of AI, most challenges can be solved through better derived strategies for improved use  
of AI in schools. This study will create insight to stakeholders responsible for the implementation of AI  
technology in schools and in-service training of agriculture teachers to improve adoption and utilisation AI for  
teaching agriculture at secondary school in Eswatini.  
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2.3  
Theoretical Framework  
This study was guided by the technology acceptance model (TAM) by Davis (1986). This framework suggests  
that when individuals are presented with a new technology such as AI tools, several factors influence their  
decision about adopting it, including its perceived usefulness, perceived ease of use and attitudes toward it  
(Davis, 1986). If the technology acceptance model holds true, a teacher’s belief that AI can help them perform  
more efficiently in their job would increase perceived usefulness and most likely, the adoption of this new  
technology would increase. Perceived ease of use is defined as the difficulty level of using the new technology.  
The more difficult the technology is to use or integrate or the training required, the less likely it is to be  
adopted. Lastly, attitudes means the individual’s willingness to learn to use the new technology. It also refers to  
the social perceptions surrounding the utilisation of the new technology. For example, teachers who perceive  
AI primarily as a tool for students to use for cheating will have negative social views towards the new  
technology. Each of these factors plays a role in the teacher’s intent to use AI as well as their willingness and  
perceived needs for self-learning and professional development.  
The technology acceptance model has been widely used across diverse domains, providing a comprehensive  
understanding of users’ behavioural intentions and actual adoption patterns. It the context of research which  
focuses on the utilisation of AI by agriculture teachers, perceptions and professional development needs, the  
TAM serves as a useful framework for understanding the factors that influence secondary school agriculture  
teachers’ acceptance of AI tools and gauging their attitudes and perceptions of AI integration within  
agricultural education at secondary school level.  
METHODOLOGY  
This quantitative study employed a descriptive survey research design. The target population were agriculture  
teachers at secondary school in Eswatini. The Manzini Region was used as a geographic space for the study  
where 125 agriculture teachers (N = 125) and a sample of 94 (n = 94) was used in the study. The sample size  
was determined through a SurveyMonkey calculator as suggested by SurveyMonkey (n.d.). This calculator  
suggested that a sample size of 94 is sufficient in survey when the population is 125. A list of teachers from  
MoET was used to randomly select the target population because it maintains an active accurate database of  
teachers in Eswatini.  
3.1  
Instrument  
The instrument that was used for data collection in study was a questionnaire. The questionnaire was  
developed by the researcher using the research instrument format from Seevers and Rosencrans (2001) and  
Shermon and Sorensen (2020) that measured AI utilisation, perceptions, and AI training needs of chemistry  
teachers. Items specific to chemistry teachers were replaced by AI for agricultural education specific content  
which were derived from literature. The questionnaire consisted four domains or sections. These sections were:  
1) description of current AI utilisation in Eswatini Secondary schools, 2) perceptions of agriculture teachers  
towards AI utilisation for teaching at secondary school in Eswatini, 3) Professional development needs of  
agriculture teachers and 4) demographic characteristics of respondents.  
3.1.1 Reliability and Validity  
A post-hoc reliability analysis of the instrument was conducted and it produced a sufficiently high reliability  
coefficient, Cronbach alpha of .88 (Table 1). The quality review of the study focused on internal and face  
validity. The questionnaire was reviewed by one agriculture inspector, one agriculture teacher who completed a  
one-week course in AI that was offered by UNESWA AI Academy. These two served as experts in the  
construction of the instrument as they are believed to be having expertise in survey research and the basic  
principles to expect in AI technology as applied in the teaching of agriculture. They reviewed the questionnaire  
flow, face and content before the instrument passed for data collection.  
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Table 1. Reliability analysis results  
Domains  
No. of items Cronbach alpha coefficient  
Current utilisation of AC tools by agriculture teachers  
14  
.88  
.85  
Perceptions of agriculture teachers regarding use of AI for  
teaching  
16  
Professional development needs of agriculture teachers in  
AI for teaching  
.91  
10  
Total  
40  
.88  
3.2  
Ethical consideration  
The study ensured that ethical consideration principles were observed. These principles included permission to  
conduct research, informed consent, voluntary participation, anonymity, confidentiality, potential of harm and  
data safety. This aspect was critical to be addressed as the study involved interaction with people who were  
participants, so as suggested by (Bayer & Fischer, 2021), it is the role of the research to protect participants  
from any form of harm (psychological, financial, and social harm) in some unintended way. The researcher  
carefully evaluated the potential harm to arise and ensured that respondents were safe. The respondents of the  
study were informed about the study’s purpose, objectives, procedures and potential risks. This information  
was shared with respondents through a letter. The respondents were made aware that that participation in the  
study was voluntary and were also informed that they could withdraw from the study at any point if they felt  
they needed to. Respondents were further assured that they were no consequences of withdrawing from the  
study. The data was meant for this study and will remain protected from any kind of misuse.  
RESULTS  
This paper aimed to determine the perceptions of agriculture teachers in the utilisation of AI for teaching at  
secondary school in Eswatini. This section presents the findings and begins with the description of the  
demographic characteristics of the respondents to lay a foundation for sound discussion of the findings as per  
the objectives of the study. A total of 94 agriculture teachers responded to the survey.  
Demographic characteristics  
Participants in the study were agriculture teachers for secondary school in Eswatini, including 58 (61.70%)  
male teachers and 36 (38.30%) female teachers from various schools in the Manzini Region in Eswatini. In the  
highest education level of the participants, 57 (60.64%) had bachelors’ degree and 37 (39.36%) had masters’  
degree as the highest qualification.  
In the age distribution, the largest group of respondents fell within 36 to 40 age groups, comprising 32  
(34.04%). This was closely followed by the age group of 31 to 35 who made up 25 (26.60%) respondents,  
followed by those who were aged 41 to 456 years who were 12 (12.77%), while those who were aged 46 years  
and above were 11 (11.70%), followed by those aged between 26 and 30 which were 9 (9.57%) and the group  
that formed a smallest portion was age group 20 to 25 years with 5 (5.32%) respondents.  
The survey also explored the years of teaching experience among respondents. The majority, 34 teachers  
(36.17%) reported having 11 to 20 years in teaching. This significant portion indicates a well-established  
workforce with considerable experience. Teachers with 21 to 30 years accounted for 32 (34.04%). Those with  
0 to 10 years teaching experience made up 22 (23.40%) respondents, while those with over 31 years of  
teaching made up 6 (6.38%) respondents. This data highlights the range of expertise and tenure present within  
the community of agricultural education at secondary school level in Eswatini, from new teachers to seasoned  
veterans.  
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Furthermore, the analysis included the level of education of the respondents, divided into bachelors’ degree  
and masters’ degree. A substantial majority, 67 (71.28%) were bachelors’ degree holders and 27 (28.72%) had  
masters’ degree as the highest level of education. This distinction is crucial for understanding the different  
educational experiences, exposure and how these factors might influence the data obtained in the study.  
Table 2. Demographic characteristics of Respondents  
Demographic characteristics of respondents  
F
%
Male  
58  
36  
5
61.70  
38.30  
7.45  
Gender  
Female  
20 – 25 years  
26 – 30 years  
31 – 35 years  
36 – 40 years  
41 – 45 years  
46 years and above  
0 - 10 years  
9
9.57  
Age  
25  
32  
12  
11  
22  
34  
32  
6
26.60  
40.43  
11.70  
7.45  
23.40  
36.17  
34.04  
6.38  
11 – 20 years  
21 – 30 years  
31 years and above  
Work experience  
Bachelors degree  
Masters degree  
67  
27  
71.28  
28.72  
Highest education level  
Description of current utilisation of AI in secondary school’s agricultural education in Eswatini  
The findings of objective one (1) of the study which investigated the extent to which AI is used by teacher in  
teaching agriculture at secondary school in Eswatini was analysed using descriptive statistics (mean and  
standard deviation). The outcome of objective one revealed that participants reported a negative predisposition  
when it comes to the use of AI for agricultural education at secondary school level in Eswatini. The  
respondents disagreed that they use AI as observed in each item of the domain. In table 3, the highest mean  
was 2.41 with a standard deviation of 1.13, while the lowest item attained a 1.01 (mean) and 1.19 (standard  
deviation). The overall mean was 1.70 with standard deviation being 1.19. When going back to the Likert scale  
type that was used to capture the data, 1 represented strongly disagree, 2 represented disagree, 3 represented  
slightly disagree, 4 represented slightly agree, 5 represented agree and 6 represented strongly agree. To  
interpret the analysis of the data for this objective, it can be observed that responses of agriculture teachers  
ranged from strongly disagree (1.01) to disagree (2.41). The results indicate that agriculture teachers do not use  
AI in teaching agriculture at secondary school in Eswatini. The standard deviation also showed that responses  
are not widely dispersed from the mean.  
Table 3. Description of current utilisation of AI in secondary school’s agricultural education in Eswatini  
Use of AI  
Mean  
2.41  
2.38  
2.35  
2.32  
SD  
I use artificial intelligence (AI) tools easily in my teaching  
I use AI to evaluate my teaching  
AI reduces my workload  
1.13  
1.18  
1.07  
1.18  
AI tools save time in completing my work  
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Government supports the use of AI  
2.31  
2.25  
1.20  
1.19  
1.34  
School administration supports the use of AI  
I use AI to formulate learner specific content to improve learner 1.66  
understanding  
I identify appropriate AI tools to evaluate students' performance  
I use AI tools to solve problems I encounter in teaching  
I prepare lesson content using AI tools  
1.25  
1.22  
1.20  
1.19  
1.14  
1.04  
1.20  
1.22  
1.13  
1.23  
1.18  
1.18  
1.38  
I use AI to deliver lessons in class  
I understand how AI is used in the teaching and learning of agriculture  
I teach my learners to use AI  
I’m conversant with the simulations in Nomfundo AI as a tool used for 1.03  
teaching and learning  
I consider the relevant learning outcomes when choosing the AI tools to 1.01  
use.  
1.15  
TOTAL  
1.70  
1.19  
Perceptions of agriculture teachers towards AI utilisation for teaching at secondary school in Eswatini  
Findings of the second objective of the study, objective two, are presented in table 4 that follows. This  
objective intended to describe perceptions of agriculture teachers regarding utilisation of AI in the teaching of  
agriculture at secondary school in Eswatini. The data were analysed using mean and standard deviation. The  
outcome of the objective revealed that agriculture teachers reported both a negative perceptions and positive  
perceptions at the same time. The highest mean was 4.47 with standard deviation being 1.05, while the lowest  
mean was 2.66 with standard deviation1.38. The overall mean was 3.80 with standard deviation 1.12.  
However, it is important to highlight that some items in this domain expressed a similar idea, but the means  
vary significantly. For example, on the item that said “the use of AI tools in education positively affects  
students ‘attitude towards the lesson”, this item obtained the highest mean (M = 4.47) in the domain, while  
another item with a similar idea, “I think AI tools enhance the quality of education” obtained the lowest mean  
(M = 2.66) in the domain. This indicates that agriculture teachers believe that learners’ attitudes change for the  
better with the use of AI in the lesson but they do not believe in AI tools enhancing the quality of education.  
Table 4. Perceptions of agriculture teachers towards AI utilisation for teaching at secondary school in Eswatini  
Agriculture teachers’ perceptions about AI  
STD  
D
The use of AI tools in education positively affects students' attitudes 4.47  
towards the lesson.  
1.05  
I believe that it is necessary to use AI tools for teaching  
AI tools are important for education  
4.42  
4.41  
4.41  
4.40  
4.39  
4.37  
1.01  
1.06  
1.13  
1.04  
1.03  
1.03  
1.03  
AI will replace teachers  
AI enables teachers to access information faster  
I think AI tools save time.  
I am willing to prepare teaching materials using AI tools  
AI helps students in agriculture to have a more individualized learning 4.35  
experience  
Virtual labs and simulations are examples of AI tools that can aid 3.32  
1.18  
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students in comprehending difficult concepts  
AI creates an inclusiveness in the education system  
The concept of Artificial Intelligence is well familiar to me  
Learning agriculture can be made more engaging for students by AI  
AI requires more money to implement  
3.25  
3.23  
3.20  
3.19  
3.14  
3.04  
2.66  
3.80  
1.20  
1.22  
1.12  
1.23  
1.18  
1.15  
1.38  
1.12  
AI enhances the intellectual capability of learners  
I think AI tools are not suitable for me  
I think artificial intelligence tools enhances the quality of education  
TOTAL  
Professional development needs of agriculture teachers regarding the use of AI for secondary school  
agricultural education in Eswatini  
The third objective of the study sought to identify professional development needs of agriculture teachers  
regarding the use of AI for teaching agriculture at secondary school in Eswatini. Findings of this objective are  
presented in table 5. Results revealed that agriculture teachers need professional development in all the areas  
that were presented by this domain. The highest mean was 4.66 (agree) with a standard deviation of 1.04,  
indicating that responses did not deviate much from the mean. The lowest mean was 3.69 which also indicates  
agreement. The overall mean was 4.39 (agree) with standard deviation being 1.00. This analysis reveals that  
agriculture needs capacitation or training on AI so that they can use it for teaching. This explains the reason  
why teachers do not use AI tool for teaching agriculture as observed in objective 1.  
Table 5. Professional development needs of agriculture teachers regarding the use of AI for secondary school  
agricultural education in Eswatini  
Use of AI  
STD  
D
Agricultural education students’ training on ethical considerations 4.66  
1.04  
in the use of AI for teaching  
Part-time/ traditional structured course on AI for teachers  
4.57  
.95  
Peer collaborations for teachers to share experiences and best 4.54  
practices in the use of AI  
1.07  
Encouraging teachers to use AI tools that contribute to their 4.49  
professional development  
1.05  
Training of the readily available AI tool in Eswatini, Nomfundo AI 4.47  
1.03  
1.05  
.92  
Online structured courses on AI for teachers  
4.42  
Agricultural education students’ training (at tertiary level) about 4.38  
potential benefits of AI in education  
AI awareness campaigns to disseminate information about AI to all 4.33  
stakeholders of education  
.99  
Regular refresher course on the use of AI for education  
AI education at tertiary level for pre-service training of teachers  
TOTAL  
4.31  
3.69  
4.39  
.98  
.98  
1.00  
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Summary of differences in utilisation of AI for agricultural education at secondary school in Eswatini  
Independent t-test and one-way analysis of variance (ANOVA) were used to determine if there was any  
significant difference in the demographic characteristics of respondents and the utilisation of AI for agricultural  
education at secondary school in Eswatini. The independent t-test indicated that there was a significant  
difference in the use of AI and the gender of respondents (t = .24, p = .033) and level of education of teachers  
(t = 1.23, p = .028). With ANOVA, it was revealed that there was no significant difference between the  
utilisation of AI for teaching and the teaching experience in years.  
Table 6. Summary of differences in utilisation of AI for agricultural education at secondary school in Eswatini  
Characteristic  
Gender:  
N
Mean use of AI Test statistic  
P
Male  
58  
36  
2.51  
.89  
t = .243  
.033*  
Female  
Education level:  
Bachelors’ degree  
Masters’ degree  
57  
37  
.96  
2.44  
t = 1.233  
.028*  
6
(highest score) to 1 (lowest score) Positive (means > 3.45); Negative (means < 3.44); p < .05  
SUMMARY  
The utilisation of technology has become an important part of human lives. Most sectors, organisations and  
humans are dependent on machines and technologies to get things done faster and easier, therefore,  
technologies such as AI have become a part and parcel of daily activities of individuals and societies.  
Regarding this fast growing trend, this study was carried out to determine the perceptions of agriculture  
teachers in the use of AI in the teaching of the subject at secondary school level in Eswatini. The analysis of  
the data collected in the study revealed that, although agriculture teachers are aware of the positive impact of  
the use of AI in education, they do not use the AI tools for teaching. Factors responsible for teachers not to use  
AI tools include: gender and level of education and it appeared that there is fear of displacement. To conclude,  
teachers’ perceptions play a crucial role in accelerating the use of AI for teaching in schools. Even though the  
benefits of the use of AI are duly recognised, there are also notable concerns that need to be addressed. By  
addressing some concerns and some teachers’ needs through support and professional development, teachers  
can harness the potential of AI to enhance teaching and learning of agriculture at secondary school in Eswatini.  
In relation to the TAM model for technology acceptance, the findings are in agreement with its explanation. As  
presented in literature, this framework suggests that when individuals are presented with a new technology  
such as AI tools, several factors influence their decision about adopting it, including its perceived usefulness,  
perceived ease of use and attitudes toward it (Davis, 1986). In this study, the findings show that the technology  
acceptance model holds true, agriculture teacher’s belief that AI can help them perform more efficiently in  
their job increased perceived usefulness and they have the potential to adopt this new technology but they  
require professional training. The model further explains that the more difficult the technology is to use or  
integrate or the training required, the less likely it is to be adopted. As observed in the study, the AI tools are  
not utilised by agriculture teachers as they lack knowledge about the AI, especially Nomfundo AI which was  
launched by the Ministry of Education and Training (MoET). Lastly, when it comes to the attitudes of  
agriculture teachers, they are willing to learn to use the new technology. This lead to them suggesting possible  
training strategies that can be employed in teacher professional development for an improved utilisation of AI  
in agricultural education at secondary school level in Eswatini.  
RECOMMENDATIONS  
Based on the findings of the study, it is recommended that:  
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1. Agriculture teachers in Eswatini be sensitised of the importance of AI in teaching and how it would  
promote the standing of education in the country.  
2. The government of Eswatini, Ministry of Education and Training should have a policy that  
regulates the inculpation of AI into all levels of education in Eswatini (this includes tertiary  
institutions where agriculture teachers are trained, such as University of Eswatini (UNESWA),  
William Pitcher and Ngwane Teachers’Training College).  
3. MoET (INSET Department) should craft and implement continual trainings for agriculture teachers  
in AI use for educational purpose, especially for teaching.  
4. For future research, studies should focus on exploring strategies to improve agriculture teachers’  
acceptance and adoption of AI technologies as well as investigating the long-term impact of AI in  
teaching practices and students’ learning outcomes. On top of that, policy makers and educational  
institutions need to develop guidelines and regulations to safeguard student privacy and ensure their  
ethical use of AI in education.  
REFERENCES  
1. Aguolu, I. B. (2019). Technological infrastructure and its impact on teaching and learning in Nigerian  
schools. Nigerian Journal of Educational Technology, 4(1), 54-67.  
2. Amoah, B., Osei, A., & Kwame, P. (2020). Factors influencing teachers’ adoption of educational  
technology: A study on secondary schools in Ghana. International Journal of Educational Technology  
, 15(4), 201-213.  
3. Binns, I. C. (2017). Interactive simulations in chemistry education: The impact of AI tools in learning  
environments. Journal of Chemical Education, 94(3), 314-320.  
4. Blonder, R., & Feldman-Maggor, Y. (2024). AI for chemistry teaching: Responsible AI and ethical  
considerations. Chemistry Teacher International, 2024(1). https://doi.org/10.1515/cti-2024-0014  
5. Cisse, Moustapha. 2018. "Look to Africa to advance artificial intelligence." Nature 562 (7728):  
461462  
6. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information  
technology. MIS Quarterly, 13(3), 319-340  
7. Dladla, N., Chikuvadze, P., Makuvire, C., & Dhlamini, Z. (2025). The sustainability of using AI to  
integrate indigenous knowledge systems in science education: A case of the Lubombo region of  
Eswatini. Global Journal of Research in Education & Literature.  
8. Eswatini Observer (2025, May 22). Eswatini eyes AI for growth.  
9. Google Africa (2024). Eswatini partners with Google to drive digital transformation.  
10. Hutchins, R. (2021). Teachers’ attitudes toward artificial intelligence in education: A comparative  
study. International Journal of Educational Research, 112, 102008.  
11. Kautz, H., Etzioni, O., & Mooney, R. (2021). Artificial Intelligence: A modern approach. 4th edition.  
Pearson Education.  
12. Kukulska-Hulme, A. (2020). AI in education: Opportunities and challenges. Educational Media  
International, 57(3), 203-216.  
13. Ministry of Education and Training, Eswatini. (2025). Four-year secondary education reform policy.  
Government policy document.  
14. Olaogun, A., and Oyediran, O. (2023). Artificial Intelligence in Education: Perceptions and  
Readiness of Nigerian Secondary School Teachers. Journal of Educational Technology, 12(4), 58-71.  
15. Shipepe, A., L. Uwu-Khaeb, E. A. Kolog, M. Apiola, K. Mufeti, and E. Sutinen. 2021. "Towards the  
Fourth Industrial Revolution in Namibia: An Undergraduate AI Course Africanized." IEEE Frontiers  
in Education Conference (FIE).  
16. SurveyMonkey. (n.d.). Sample size calculator. https://www.surveymonkey.com/mp/sample-size-  
calculator/  
17. UNESCO IICCBA & KIX Africa 19 Hub. (2024). National policy dialogue on teacher professional  
development in Eswatini. Workshop report.  
18. UNESWAAI Academy. (2025). Certificate in Generative AI for Educators. Program brochure.  
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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)  
ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue X October 2025  
19. United Nations Eswatini. (2024). AI Indaba: Harnessing AI for sustainable development. Event  
summary.  
20. VOA Africa. (2024, April 10). Eswatini schools incorporate AI in education, some teachers resist.  
https://www.voaafrica.com/a/eswatini-schools-incorporate-ai-in-education-some-teachers-  
resist/7562615.html  
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