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ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025

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A Quantitative Study on the Influence of Readiness and Perceived

Usefulness on Malaysian Primary ESL Teachers’ Intention to

Integrate AI in Language Learning and Teaching

Chiew Fung Ling

, Melor Md Yunus, Hanita Hanim Ismail

Faculty of Education, University Kebangsaan Malaysia

Corresponding Author

DOI: https://dx.doi.org/10.47772/IJRISS.2025.91100242

Received: 22 November 2025; Accepted: 28 November 2025; Published: 06 December 2025

ABSTRACT

Artificial Intelligence (AI) is increasingly transforming English Language Teaching (ELT), yet limited empirical

evidence exists on how Malaysian primary ESL teachers perceive and intend to use AI tools in classroom

practice. This study investigates teachers’ readiness, perceived usefulness and behavioural intention to integrate

AI, addressing a gap in empirical research within the Malaysian primary school context. A quantitative survey

design was employed, involving 80 primary ESL teachers who completed a structured questionnaire adapted

from the Technology Acceptance Model (TAM). Descriptive statistics indicated moderately high levels of

readiness, strong perceptions of usefulness and high intention to adopt AI. Pearson correlation analysis revealed

strong, positive and statistically significant relationships among the three constructs. Multiple regression results

further showed that perceived usefulness was the strongest predictor of intention, followed by teacher readiness.

Openended responses provided supplementary insights, highlighting teachers’ need for hands-on training,

practical examples and continuous professional development. Overall, the study offers timely empirical evidence

on AI adoption in Malaysian primary ESL classrooms and underscores the importance of enhancing teachers’

digital competence and pedagogical capacity for sustainable and meaningful AI integration.

Keywords: Artificial Intelligence (AI), teacher readiness, perceived usefulness, Technology Acceptance Model

(TAM), English Language Teaching (ELT)

INTRODUCTION

The integration of Artificial Intelligence (AI) into education has become one of the most significant developments

shaping teaching and learning in the twenty-first century. In the field of English Language Teaching (ELT), AI

applications such as ChatGPT, Grammarly, ELSA Speak and Mondly AR have redefined how teachers plan

lessons, deliver instruction and assess learning outcomes. These intelligent tools provide personalized feedback,

adaptive content and interactive learning environments that align with the global demand for digital competence

and 21st-century learning. In Malaysia, the Ministry of Education has taken progressive steps to encourage

technology adoption through the Digital Education Policy (2024–2030) (Ministry of Education Malaysia, 2024),

which aims to promote innovation, creativity and digital literacy among educators and students alike. This

initiative complements the aspirations of the Malaysia Education Blueprint (2013–2025) (Ministry of Education

Malaysia, 2013), which emphasizes leveraging technology to enhance teaching quality and student engagement.

Despite these policy efforts, the successful implementation of AI in English language classrooms depends largely

on teachers’ readiness and perceived usefulness of such technologies. Teacher readiness reflects the degree to

which educators feel prepared, confident and equipped with the necessary technological skills and resources to

incorporate AI tools in their teaching practice (Chan & Tang, 2024). Perceived usefulness, on the other hand,

represents teachers’ beliefs about the extent to which AI can improve teaching efficiency, foster meaningful

learning and enhance students’ language acquisition (Guan et al., 2025). Together, these two constructs determine

teachers’ behavioural intention to integrate AI in their pedagogical activities. Research suggests that even when

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ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025

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teachers have access to digital tools, their adoption is often limited if they do not perceive them as useful or if

they lack the readiness to utilize them effectively (Wulandari & Purnamaningwulan, 2024).

However, while global studies have explored these factors conceptually, there remains limited empirical evidence

within the Malaysian English as a Second Language (ESL) context. Most previous research has focused broadly

on teachers’ attitudes toward educational technology or general digital readiness, without isolating the influence

of AI-specific readiness and perceived usefulness on teachers’ behavioural intention to use AI in language

education (Metwally & Bin-Hady, 2025; An et al., 2023). In addition, much of the existing literature has been

conducted in higher education or in developed countries where digital infrastructure and professional training

are more established (Kohnke & Ulla, 2024). In contrast, Malaysian ESL teachers—particularly those in primary

and secondary schools—operate within diverse linguistic environments and resource constraints, which may

influence how they perceive and adopt AI tools in classroom practice. As a result, the integration of AI in

language learning and teaching in Malaysia remains uneven and largely exploratory. This study therefore

addresses a clear research gap by providing quantitative, context-specific evidence on how readiness and

perceived usefulness predict ESL teachers’ intention to integrate AI into their pedagogical practices—an area that

has not been empirically examined in the Malaysian school context.

Although AI integration is gaining momentum, there remains limited empirical evidence on how teacher

readiness and perceived usefulness predict Malaysian ESL teachers’ intention to adopt AI. Recognising this gap,

the present study examines the influence of these two constructs on teachers’ intention to integrate AI in language

learning and teaching. Specifically, it seeks to identify teachers’ levels of readiness, their perceptions of AI’s

usefulness, and the extent to which these factors predict their intention to use AI in instructional contexts. To

fulfil this aim, the objectives of the study are as follows:

1. to examine the levels of ESL teachers’ readiness, perceived usefulness and intention to integrate AI in

language learning and teaching

2. to determine the relationship between readiness, perceived usefulness and intention

By examining these relationships, this study contributes to a deeper understanding of the psychological and

pedagogical factors that shape teachers’ adoption of AI in English language education. The findings are expected

to provide valuable insights for policymakers, school leaders and teacher educators in promoting effective and

sustainable AI integration. Furthermore, the study offers empirical evidence to support Malaysia’s Digital

Education Policy (2024–2030) by highlighting the importance of teacher preparedness and attitudes in driving

technological innovation in schools. It also extends the theoretical foundation of the Technology Acceptance

Model (TAM) by validating how readiness and perceived usefulness influence behavioural intention within the

Malaysian ESL context (An et al., 2023; Runge et al., 2025). Ultimately, this study seeks to promote a balanced

and human-centred approach to AI adoption, positioning technology as a collaborative pedagogical tool that

empowers both teachers and learners in the English language classroom.

LITERATURE REVIEW

Teacher Perceptions of AI-Enhanced Language Teaching

The integration of Artificial Intelligence (AI) into English Language Teaching (ELT) has influenced teachers’

professional beliefs and classroom practices, shaping their perceptions across both pedagogical and

psychological dimensions. Pedagogically, teachers increasingly recognise AI as a valuable instructional tool that

enhances lesson delivery, supports differentiated instruction and streamlines assessment processes. Empirical

studies show that tools such as ChatGPT, Grammarly, ELSA Speak and Mondly AR provide instant feedback,

personalise learning pathways and reduce teachers’ workload in preparing materials and assessing student work

(Mustroph & Steinbock, 2024; Xiaofan & Annamalai, 2025). Similar findings were reported in a recent study,

where EFL teachers perceived AI-powered grading tools such as CoGrader as useful for enhancing feedback

quality and supporting writing assessment (Alsalem, 2024). In the Malaysian context, ESL teachers similarly

report that AI promotes student engagement and creativity, particularly when learners require varying levels of

linguistic scaffolding (Zainuddin et al., 2024). Recent Malaysian findings also echo this pattern, where teachers

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reported generally positive perceptions of ChatGPT for enhancing lesson preparation, feedback quality and

student engagement (Tan et al., 2025; Sivanganam et al., 2025). Similarly, primary ESL teachers recognise its

usefulness for planning and personalised tasks despite ongoing challenges with infrastructure, training and the

adaptation of AI-generated content for younger learners (Hisham & Yunus, 2025). These findings explain why

teachers generally hold favourable pedagogical perceptions of AI integration.

However, teachers’ attitudes are also shaped by psychological considerations, which influence how they evaluate

AI’s role in language teaching. While educators acknowledge AI’s instructional benefits, many remain cautious

about its limitations in offering human-like interaction, cultural sensitivity and emotional support—elements

considered essential for effective language learning (Tafazoli, 2024; Metwally & Bin-Hady, 2025). Concerns

about the reliability, neutrality and contextual appropriateness of AI-generated responses reflect deeper

psychological constructs such as trust, perceived risk and anxiety toward automation. These reservations align

with sociocultural and constructivist theories, which emphasise the importance of human mediation and guided

interaction in learning processes (Vygotsky, 1978; Piaget, 1970). Consequently, although teachers value AI as a

pedagogical aid, psychological uncertainties contribute to cautious and selective adoption.

Perceived Usefulness and Teacher Readiness

Perceived usefulness (PU) and teacher readiness are central constructs influencing teachers’ acceptance of AI and

can be understood through intertwined pedagogical and psychological dimensions. PU, a core construct of the

Technology Acceptance Model, refers to teachers’ belief that AI enhances their instructional effectiveness and

professional performance (Davis, 1989). Teachers who perceive AI as improving feedback accuracy, increasing

teaching efficiency or promoting learner autonomy are more likely to integrate it into their practice (Granström

& Oppi, 2025; Guan et al., 2025). PU therefore represents a psychologically grounded judgement of AI’s value,

framed within teachers’ pedagogical goals.

Teacher readiness further shapes acceptance by encompassing both psychological confidence and pedagogical

competence. Psychologically, readiness refers to teachers’ digital self-efficacy, openness to innovation and

comfort with technological change (Chan & Tang, 2024; Wulandari & Purnamaningwulan, 2024). Teachers who

feel competent using digital tools tend to view AI more positively. Pedagogically, readiness involves the ability

to integrate AI meaningfully with curriculum demands, instructional strategies and diverse learner needs.

Research indicates that insufficient training or institutional support can lead teachers to perceive AI as

burdensome or disruptive, whereas teachers with strong readiness are more likely to see AI as a pedagogically

advantageous resource (Jackman et al., 2025; Riggs, 2025).

Recent studies demonstrate a reciprocal relationship between readiness and PU: higher readiness enhances

teachers’ perceptions of AI’s usefulness and stronger perceptions of usefulness further strengthen intention to

adopt AI tools (Granström & Oppi, 2025). This synergy highlights the need to address both psychological

preparedness and pedagogical capacity when examining ESL teachers’ acceptance of AI.

Technology Acceptance Model (TAM)

The Technology Acceptance Model (TAM) provides a coherent framework for understanding teachers’

behavioural intentions to integrate AI into language classrooms, capturing both psychological factors and

pedagogical considerations. TAM posits that two key psychological constructs—perceived usefulness (PU) and

perceived ease of use (PEOU)—shape individuals’ evaluations of technology and influence adoption decisions

(Davis, 1989). In educational contexts, TAM has been expanded to incorporate variables such as teacher

readiness, digital competence and training experience, providing a more comprehensive understanding of how

educators respond to AI technologies (Kavitha & Joshith, 2024; Runge et al., 2025).

Within the ELT context, TAM assumes relevance across both dimensions. Psychologically, teachers assess AI

based on their confidence in using technology and their beliefs about its practicality and compatibility with their

teaching capabilities. Pedagogically, teachers evaluate AI according to how well it supports instructional

objectives, promotes student engagement and enhances language-learning processes. Empirical evidence shows

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that teacher readiness serves as an antecedent to both PU and behavioural intention as teachers with stronger

digital self-efficacy and pedagogical understanding of AI demonstrate higher acceptance (Jackman et al., 2025;

Wulandari & Purnamaningwulan, 2024).

Drawing on these insights, the present study therefore adopts an extended TAM framework in which teacher

readiness influences PU, and both constructs collectively predict ESL teachers’ intention to integrate AI into

teaching and learning. This model maintains coherence by linking psychological beliefs with pedagogical

considerations, offering a comprehensive explanation of teachers’ acceptance of AI within Malaysian ESL

classrooms.

Collectively, the reviewed studies highlight the importance of examining both psychological and pedagogical

factors that influence teachers’ acceptance of AI. While international research has demonstrated the roles of

readiness and perceived usefulness, evidence within the Malaysian primary ESL context remains limited. This

underscores the need for a context-specific investigation into how these constructs shape teachers’ behavioural

intention, which the present study addresses through an extended TAM framework.

METHODOLOGY

Research Design

The study employed a quantitative survey design to examine primary school ESL teachers’ readiness, perceived

usefulness and intention to integrate Artificial Intelligence (AI) in language learning and teaching. The survey

method was selected as it allows systematic collection of numerical data and provides an efficient means of

capturing participants’ beliefs and perceptions, making it appropriate for investigating teachers’ views on AI

integration (Creswell, 2012). Although primarily quantitative, the instrument incorporated four open-ended

questions to obtain supplementary qualitative insights. The study was underpinned by the Technology

Acceptance Model (TAM) (Davis, 1989), which posits that users’ perceptions of a technology, particularly its

usefulness, influence behavioural intention to adopt it.

Sampling Method and Participants

Convenience sampling was employed to obtain participants for the study. In convenience sampling, individuals

are selected based on ease of access and willingness to participate (Noor et al., 2022; Wang & Cheng, 2020). In

this research, the questionnaire link was disseminated through WhatsApp and Telegram teacher groups as well

as selected Facebook communities, which facilitated voluntary participation from practising primary ESL

teachers across different regions in Malaysia. To enhance visibility and improve response rates, the Google Form

link was uploaded three times across these platforms, resulting in 80 respondents in the main study. Prior to this,

a pilot study involving 21 primary ESL teachers was conducted to examine the clarity, relevance and reliability

of the instrument. The main data collection period was subsequently carried out over one month.

Research Instrument

Data were gathered using a structured Google Forms questionnaire comprising five sections. Section A elicited

demographic information while Sections B, C and D consisted of five-point Likert-scale items measuring Teacher

Readiness, Perceived Usefulness and Intention to Integrate AI respectively. All Likert-scale items were rated on

a five-point scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree), allowing respondents to indicate the

extent of their agreement with each statement. Section E included four open-ended questions designed to capture

teachers’ reflections on readiness, usefulness perceptions, integration decisions and support needs.

The questionnaire was developed by adapting established constructs from the Technology Acceptance Model and

recent empirical studies on AI integration in education. The Perceived Usefulness and Intention constructs were

adapted from TAM (Davis, 1989; Venkatesh & Davis, 2000), with items recontextualised for AI use in English

language teaching. The Teacher Readiness construct was informed by recent empirical studies (Chan & Tang,

2024; Kavitha & Joshith, 2024; Guan et al., 2025), which conceptualise readiness in terms of digital competence,

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technical confidence, infrastructure support, institutional encouragement and teacher preparedness. Although

these sources provide the conceptual foundation, all questionnaire items were newly written and contextualised

for the Malaysian primary ESL environment. The four open-ended items were self-developed to enrich the

quantitative findings by eliciting respondents’ deeper perspectives on AI integration.

Instrument validity was ensured through expert review. The questionnaire was reviewed by three experts in

English, comprising the Head of Panel for English and two experienced English teachers, who evaluated the

clarity, content appropriateness and suitability of the items for Malaysian primary ESL classroom contexts. Their

feedback informed minor revisions to the wording and structure of several items.

A pilot study was subsequently conducted with 21 primary ESL teachers to assess the internal consistency of the

instrument. Cronbach’s alpha values for all constructs exceeded .80, indicating good to very good internal

consistency. This interpretation is consistent with established reliability benchmarks, which hold that values

above .80 demonstrate very good reliability (Daud et al., 2018), values between .80 and .89 indicate good

reliability (Ahmad et al., 2024), and coefficients of .80 or higher are recommended for instruments used in applied

educational research (Nunnally & Bernstein, 1994). The pilot study results are presented in Table 1.

Table 1: Internal Consistency in the Questionnaire for Pilot Study

Construct

Cronbach’s Alpha

Cronbach’s Alpha (Standardised)

Number of items

Teacher Readiness

.915

.917

Perceived Usefulness

.936

.942

Intention to Integrate AI

.905

.909

Total scales

.960

.965

Following the pilot test, the instrument was administered to the full sample (N = 80). A subsequent reliability

analysis using the main study dataset yielded a Cronbach’s alpha of .961 for all 28 items combined, indicating

excellent overall reliability. The main-study reliability results are summarised in Table 2. Table 2: Overall

Reliability Statistics for the Main Study

Cronbach’s Alpha

Cronbach’s Alpha (Standardised)

Number of items

.961

.964

Data Analysis

After establishing the reliability of the instrument, the data were analysed using SPSS Version 31.0. Descriptive

statistics (frequencies, percentages, means and standard deviations) were generated to summarise the

demographic characteristics of the respondents and to determine the levels of teacher readiness, perceived

usefulness and intention to integrate AI. Pearson correlation analysis was conducted to examine the strength and

direction of the relationships among these continuous variables, while multiple regression analysis was employed

to identify the predictive effects of teacher readiness and perceived usefulness on teachers’ intention. These

analytical procedures were selected because they correspond directly to the research questions and are

theoretically aligned with the assumptions of the Technology Acceptance Model (TAM), which posits that users’

perceptions and readiness influence behavioural intention. Although the questionnaire contained four openended

items, the responses were summarised briefly to support the quantitative results rather than subjected to

qualitative analysis.

RESULTS

Demographic Information

A total of 80 Malaysian primary ESL teachers participated in this study, representing a broad and diverse range

of professional and personal backgrounds. The respondents varied in age from 21 years old to above 50 and

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possessed differing academic qualifications, with the majority holding a Bachelor’s degree, followed by teachers

with Master’s degrees, Diplomas and a small number with doctoral qualifications. Their teaching experience

ranged from 1–5 years to more than 15 years, providing a balanced distribution of novice, mid-career and highly

experienced educators. Participants were drawn from both urban and rural school contexts across several states,

including Selangor, Johor, Perak, Kedah and Sabah, offering representation from varied educational

environments. In addition, their exposure to technology and AI-related professional development differed, as

some had attended AI-focused training while others had not. This diversity in demographic characteristics

contributes to a comprehensive and representative understanding of Malaysian primary ESL teachers, offering

meaningful context for interpreting the study's findings. A summary of the respondents’ demographic

characteristics is presented in Table 3.

Table 3: Demographic Characteristics of Respondents (N =80)

Characteristic

Category

Frequency (n)

Percentage (%)

Gender

Female

75.0

Male

25.0

Age

21-30 years

55.0

31-40 years

33.8

41-50 years

7.5

Above 50 years

3.7

Highest Qualification

Diploma

5.0

Bachelor’s Degree

77.5

Master’s Degree

16.3

PhD

1.2

Teaching Experience

1-5 years

57.5

6-10 years

25.0

11-15 years

7.5

More than 15 years

10.0

School Location

Urban

12.5

Semi-urban

55.0

Rural

18.8

State of Teaching

Selangor

55.0

Sarawak

18.8

Johor

6.3

Sabah

5.0

Negeri Sembilan

2.5

Penang

2.5

Pahang

1.2

Kedah

1.2

Perlis

1.2

Perak

1.2

Federal Territories

(KL/Labuan/Putrajaya)

1.2

AI-Related Training Attended

Yes

71.3

28.7

Level of Readiness, Perceived Usefulness and Intention

Descriptive statistics were computed to examine the overall levels of readiness, perceived usefulness and

intention to integrate AI. As shown in Table 4, the results revealed that teachers reported moderately high levels

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for all three constructs. Teacher readiness recorded a mean of M = 3.85 (SD = .59), suggesting that teachers

generally felt confident and capable of using AI tools to support instructional delivery. Although readiness was

slightly lower than the other constructs, the score still indicates a positive orientation toward adopting AI.

Perceived usefulness showed a higher mean of M = 4.21 (SD = .54), indicating strong agreement that AI tools

can support English language teaching by enhancing feedback quality, facilitating personalised learning and

improving instructional efficiency. This finding suggests that teachers recognise the pedagogical benefits of

integrating AI into language learning contexts. Intention to integrate AI recorded the highest mean, M = 4.28 (SD

= .51), reflecting a strong willingness among teachers to adopt AI tools in future teaching practices. Taken

together, these results demonstrate that Malaysian primary ESL teachers perceive AI as valuable and are highly

inclined to implement it in their instructional routines, supported by their perceived readiness and positive

pedagogical attitudes.

Table 4: Descriptive Statistics for Readiness, Perceived Usefulness and Intention

Variable

Minimum

Maximum

Readiness

2.30

5.00

3.85

.59

Perceived Usefulness

2.90

5.00

4.21

.54

Intention to Integrate AI

3.25

5.00

4.28

.51

Following the construct-level results, item-level descriptive statistics were examined to obtain a more detailed

understanding of teachers’ perceptions. Table 5 presents the distribution of scores for each readiness item.

Overall, teachers showed moderately high readiness, with the highest-rated item being access to reliable internet

and devices (M = 4.21), followed by mental preparedness to adopt AI (M = 4.04). Teachers also reported

confidence in selecting appropriate AI tools (M = 4.01) and using them in instructional design (M = 3.86).

However, readiness was lower for aspects related to technical support (M = 3.46) and troubleshooting (M = 3.48),

indicating that while teachers themselves felt generally prepared, institutional and technical support were

comparatively weaker.

Table 5: Descriptive Statistics for Teacher Readiness Items (N=80)

Item

Mean

Degree

Rank

I am confident in using AI tools for English language teaching.

3.99

High

0.738

I possess sufficient digital skills to integrate AI in my lessons.

3.83

High

0.776

I can troubleshoot technical issues that may arise when using AI tools.

3.48

Moderate

0.954

I can select appropriate AI tools that suit my lesson objectives and student

needs.

4.01

High

0.684

I can design lesson activities that incorporate AI effectively.

3.86

High

0.742

My school provides adequate technical support for AI-based teaching.

3.46

Moderate

0.980

I receive sufficient professional development opportunities related to AI

integration.

3.63

High

0.848

I feel mentally prepared to adopt new AI technologies in my teaching

practice.

4.04

High

0.737

I believe my students are ready to use AI-based tools for learning.

3.95

High

0.855

I have access to reliable internet and devices for AI-based instruction.

4.21

High

0.650

To further explore teachers’ perceptions of AI’s instructional value, Table 6 summarises the item-level results for

perceived usefulness. All items scored in the high range, consistent with the overall construct mean. The

highestrated item indicated that AI improves lesson preparation efficiency (M = 4.39), followed by AI’s

contribution to students' language achievement (M = 4.28) and enhanced teaching performance (M = 4.29).

Teachers also perceived strong benefits in terms of workload reduction, feedback quality and supporting student

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comprehension. Even the lowest-scoring item—AI’s role in formative assessment (M = 4.13)—remained within

the high category, demonstrating uniformly positive perceptions of AI’s value across instructional domains.

Table 6: Descriptive Statistics for Perceived Usefulness Items (N=80)

Item

Mean

Degree

Rank

AI tools improve the quality of feedback I provide to students.

4.19

High

0.658

AI applications make lesson preparation more efficient.

4.39

High

0.626

Using AI tools helps me personalize learning for students.

4.18

High

0.652

AI enhances student engagement in English lessons.

4.19

High

0.638

AI supports effective formative assessment and progress tracking.

4.13

High

0.700

Integrating AI improves my overall teaching performance.

4.29

High

0.578

AI helps students understand language concepts more effectively.

4.19

High

0.644

AI allows me to focus more on interactive classroom activities.

4.16

High

0.679

AI reduces my workload related to marking and assessment.

4.22

High

0.694

AI contributes positively to students’ language achievement.

4.28

High

0.610

Finally, Table 7 presents the item-level descriptive statistics for intention to integrate AI. The intention construct

recorded the highest overall mean among the three constructs and this pattern was consistent at the item level.

Teachers expressed the strongest intention to explore more AI tools (M = 4.41), followed by motivation to learn

more about AI in pedagogy (M = 4.36). High intentions were also reflected in teachers’ willingness to attend

future AI-related workshops (M = 4.28) and to incorporate AI into their regular teaching routines (M = 4.28).

Even the lowest-ranking item—intending to use AI for assessing student outcomes (M = 4.18)—remained high,

signifying broad and consistent enthusiasm for long-term AI adoption.

Table 7: Descriptive Statistics for Intention to Integrate AI Items (N=80)

Item

Mean

Degree

Rank

I plan to integrate AI tools into my English lessons in the near future.

4.20

High

0.625

I am willing to explore more AI tools for teaching English.

4.41

High

0.568

I intend to use AI for assessing students’ learning outcomes.

4.18

High

0.661

I will recommend AI tools to my colleagues for language teaching.

4.26

High

0.611

I am motivated to learn more about integrating AI into pedagogy.

4.36

High

0.601

I will attend future workshops or courses related to AI in education.

4.28

High

0.573

I intend to make AI a regular part of my teaching routine.

4.28

High

0.693

I am confident that I will continue using AI tools in the long term.

4.24

High

0.641

Relationship Between Readiness, Perceived Usefulness and Intention

Pearson correlation analysis was conducted to examine the relationships among readiness, perceived usefulness

and intention. As shown in Table 8, all correlations were positive, strong and statistically significant. Readiness

was strongly correlated with perceived usefulness (r = .75, p < .001), indicating that teachers who felt more

prepared to use AI were also more likely to view AI as beneficial for teaching and learning. Readiness was also

strongly correlated with intention (r = .68, p < .001), suggesting that teachers who felt more confident and

equipped to use AI expressed higher intention to integrate it into their instructional practices. Perceived

usefulness similarly showed a strong correlation with intention (r = .71, p < .001), reinforcing the idea that

teachers who believe AI can improve teaching outcomes are more inclined to adopt it.

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Table 8: Pearson Correlations Among Readiness, Perceived Usefulness and Intention

Variable

Readiness

3.85

.59

Perceived Usefulness

4.21

.54

.75***

Intention

4.28

.51

.68***

.71***

A multiple regression analysis was conducted to determine whether readiness and perceived usefulness

significantly predicted intention to integrate AI. The regression model was statistically significant, F(2, 77) =

48.66, p < .001, and accounted for 55.8% of the variance in intention (R² = .558), indicating that the two predictors

together substantially explain teachers’ intention to integrate AI. As presented in Table 9, both predictors

contributed significantly to the model. Perceived usefulness emerged as the strongest predictor (β = .45, p <

.001), suggesting that teachers who perceive AI as beneficial are more likely to intend to use it. Readiness was

also a significant predictor (β = .31, p = .003), indicating that teachers who feel confident and prepared to use AI

demonstrate stronger intention to adopt AI tools. These findings highlight the influential roles of both perceived

usefulness and readiness, aligning with the Technology Acceptance Model, which emphasises the importance of

users’ beliefs and preparedness in shaping AI adoption behaviours.

Table 9: Multiple Regression Analysis Predicting Intention to Integrate AI

Predictor

SE B

Constant

1.34

0.31

4.38

< .001

Readiness

0.30

0.10

.31

3.05

.003

Perceived Usefulness

0.43

0.11

.45

3.98

< .001

Note. R = .747, R² = .558, Adjusted R² = .547, F(2, 77) = 48.66, p < .001.

Supporting Teacher Insights

The open-ended responses provided additional descriptive insights that complemented the quantitative findings.

Teachers described their readiness using terms such as “Mid,” “Intermediate,” and “High,” alongside expanded

statements indicating developing confidence, for example, “I am quite ready to use AI tools in my English

lessons,” and “I am still learning how to use AI tools.” Responses regarding usefulness were consistently positive,

with teachers describing AI tools as “very useful,” “highly effective,” and “helpful because they make lessons

more engaging,” along with brief comments such as “High” and “Strongly effective.” For factors influencing

intention, teachers referred to usefulness, reliability and relevance, represented by responses such as “Usefulness

of AI tools” and “My decision to use AI depends on how useful and effective the tools are.” Some teachers also

mentioned practical considerations such as the ease of using AI tools and the availability of technical support,

indicating that intention is shaped not only by perceived usefulness but also by the practicality of implementation.

Regarding support needs, teachers requested “Hands-on training,” “Workshops,” “Practical training,” and

“Tutorials,” with several providing elaborated responses such as “I would like practical training and clear

guidelines,” and “Workshops and examples of AI use in class would help.” These responses collectively describe

the range of views expressed in the dataset and serve to support the quantitative results.

DISCUSSION

Level of Readiness, Perceived Usefulness and Intention

The findings of this study show that Malaysian primary ESL teachers demonstrate moderately high levels of

readiness, strong perceptions of usefulness and high intention to integrate AI in language learning and teaching.

From a psychological perspective, the readiness mean (M = 3.85) reflects teachers’ developing confidence and

digital self-efficacy. This psychological readiness is echoed in the teacher insights, where respondents described

themselves as “mid,” “intermediate,” “still learning,” and “quite ready.” Such responses reveal an awareness of

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AI’s relevance, accompanied by lingering uncertainty regarding technical mastery. These patterns are consistent

with Chan and Tang (2024), who found that teachers’ psychological readiness is often shaped by prior exposure,

perceived competence and levels of digital anxiety.

From a pedagogical standpoint, readiness also reflects teachers’ understanding of how AI can fit meaningfully

into lesson planning and classroom practice. Several teachers indicated that they were exploring AI tools for

generating teaching ideas, personalising worksheets or supporting pronunciation practice. These responses

indicate that readiness extends beyond technical familiarity and increasingly involves teachers’ ability to

integrate AI into CEFR-aligned tasks and curriculum requirements. These insights suggest that teachers are

beginning to see AI not only as a technological tool but as a pedagogical asset. However, the responses also imply

that teachers’ pedagogical readiness remains emergent, with many still uncertain about integrating AI within

curriculum requirements and CEFR-aligned learning outcomes.

Perceived usefulness recorded the highest levels in the study (M = 4.21), supported strongly by teachers’

qualitative responses such as “AI is very useful,” “highly effective,” “strongly effective,” and “helpful because it

makes lessons more engaging.” Psychologically, these responses indicate strong positive beliefs about AI’s value,

which TAM identifies as the most influential determinant of behavioural intention (Davis, 1989; Venkatesh &

Davis, 2000). Pedagogically, teachers emphasised AI’s ability to enhance feedback quality, support speaking

tasks, generate personalised materials and streamline lesson preparation. These pedagogical benefits align with

findings by Guan et al. (2025), who argue that AI improves linguistic scaffolding, enhances immediate feedback

and supports differentiated instruction—which are central pedagogical concerns in primary ESL classrooms.

Intention to integrate AI was the highest among the three constructs (M = 4.28), indicating strong willingness to

adopt AI. Teachers expressed eagerness in comments such as “I would use AI when it is useful” and “AI improves

teaching efficiency.” Psychologically, this reflects positive attitudes and future-oriented motivation.

Pedagogically, it suggests that teachers see AI as compatible with instructional goals and capable of enriching

classroom practice. The relatively small standard deviations across all constructs (readiness SD = 0.59; perceived

usefulness SD = 0.54; intention SD = 0.51) indicate that these positive perceptions were consistently shared

across respondents, further reinforcing the stability of these findings. Collectively, the results in this section

suggest that teachers are favourably positioned for AI adoption, provided that their psychological confidence and

pedagogical competence continue to be developed.

In addition to the construct-level findings, the item-level results further reinforce these interpretations. Teachers

rated themselves particularly high in areas related to device and internet access, mental preparedness and the

ability to identify suitable AI tools, while lower ratings on troubleshooting and institutional technical support

suggest that personal readiness exceeds infrastructural readiness. Likewise, perceived usefulness scores were

uniformly high across all items, especially for efficiency, improved teaching performance and positive effects on

student learning. Intention items also showed strong and consistent willingness to explore, learn and adopt AI

tools on a long-term basis. These item-level trends support the conclusion that teachers hold broadly positive

perceptions across all constructs, providing strong empirical support for Research Question 1.

Relationship Between Readiness, Perceived Usefulness and Intention

The results demonstrated strong and significant relationships among readiness, perceived usefulness and

intention, confirming the relevance of TAM in explaining AI adoption in Malaysian ESL contexts.

Psychologically, the strong correlations between readiness and usefulness (r = .75, p < .001) and between

readiness and intention (r = .68, p < .001) indicate that teachers who feel competent and confident are more likely

to perceive AI positively and intend to use it. The teacher insights support this, with respondents noting comments

such as “I am still learning,” “I need more confidence and skills,” indicating that psychological readiness is a

meaningful factor influencing intention. These patterns are consistent with TAM’s proposition that internal

beliefs, particularly confidence and perceived capability, shape teachers’ cognitive evaluations of new

technologies (Venkatesh & Davis, 2000).

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Pedagogically, readiness shapes teachers’ ability to envision how AI can be incorporated into classroom activities.

Teachers expressing that they are “still learning” or “not fully confident” reflect pedagogical uncertainty about

aligning AI use with teaching strategies, curriculum demands or assessment requirements. Such pedagogical

readiness is essential because AI integration requires teachers to make instructional decisions, scaffold student

interaction with technology and manage AI-supported learning activities—skills that teachers may still be

developing. This supports prior studies suggesting that pedagogical readiness directly influences how effectively

teachers can translate digital tools into meaningful learning experiences (Wulandari & Purnamaningwulan,

2024).

Perceived usefulness emerged as the strongest predictor of intention in the regression analysis (β = .45, p < .001),

indicating that teachers’ belief in AI’s pedagogical value is the most influential factor shaping their adoption

decisions. Teacher statements such as “My decision to use AI depends on how useful and effective the tools

are”directly reflect TAM’s assertion that perceived usefulness has the greatest impact on intention (Davis, 1989).

From a psychological angle, teachers demonstrate strong task-related beliefs and positive attitudes toward AI’s

potential. Pedagogically, teachers indicated that AI enhances lesson engagement, feedback provision and

linguistic practice, demonstrating that usefulness is framed within concrete instructional benefits. These findings

reinforce the idea that teachers’ perceptions of usefulness are grounded not only in general attitudes but in

observable improvements in instructional processes and learner outcomes.

The findings also highlight the influence of contextual and institutional factors on readiness and intention.

Teachers frequently expressed a need for “hands-on training,” “practical workshops,” “tutorials,” and “examples

of AI use in English lessons.” Psychologically, such training would enhance confidence, reduce anxiety and

strengthen digital self-efficacy. Pedagogically, practical, example-based training would help teachers understand

how AI can be meaningfully integrated into planning, delivery and assessment within CEFR-aligned classroom

contexts. This aligns with research highlighting that institutional support, structured training and ongoing

professional development are essential for promoting sustainable classroom technology use (Jackman et al.,

2025; Riggs, 2025). These requests align with studies indicating that meaningful AI adoption requires both

psychological preparedness and pedagogical capability supported by institutional structures (Metwally &

BinHady, 2025; Riggs, 2025).

Overall, the findings demonstrate that readiness and perceived usefulness are not only statistically significant

predictors of intention but are also supported by clear psychological and pedagogical explanations. Teachers

intend to integrate AI because they believe it is valuable and because they feel increasingly competent in

navigating it. Nevertheless, continuous and well-structured professional support is necessary to ensure that these

intentions translate into sustained, effective and pedagogically sound AI integration in classroom practice. The

findings clearly address Research Question 2 by demonstrating that both teacher readiness and perceived

usefulness significantly influence ESL teachers’ intention to integrate AI in language learning and teaching.

CONCLUSION

The study examined Malaysian primary ESL teachers’ readiness, perceived usefulness and intention to integrate

AI in language teaching. The findings showed moderately high readiness, strong perceptions of usefulness and

high intention to adopt AI, with perceived usefulness emerging as the strongest predictor of intention. These

results indicate that teachers recognise AI’s value in enhancing feedback, personalising learning and supporting

instructional efficiency. Although teachers demonstrated positive attitudes toward AI, their responses also

highlight the need for continued professional development to strengthen both digital competence and pedagogical

application. Overall, the study provides timely empirical evidence on AI adoption in Malaysian primary ESL

education and underscores the importance of supporting teachers to ensure meaningful and sustainable

integration.

In practice, these findings suggest that strengthening teachers’ digital readiness, improving infrastructural support

and expanding access to AI-focused professional development are essential for effective implementation. Future

efforts should prioritise clearer guidelines, targeted training and more robust infrastructure to ensure sustainable

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and  meaningful  AI  integration  in  schools.  At  the  same  time,  the  study’s  limited  timeframe,  reliance  on 
convenience sampling and brief qualitative responses constrain the generalisability and depth of the findings, 
indicating  the  need  for  further  research  that  explores  classroom  practices,  contextual  factors  and  long-term 
adoption patterns.  
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