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“AI In Mathematics Education: Level of Awareness and Perceptions

on the Usefulness of AI-Driven Learning Tools Among Grade 12

STEM Students”

Avanceña, Mary June G.; Bandong, Rosauro Jr., S.; Carbaquil, Kyla Marie T.; and Genesis G.

Camarista, Ph.D.

West Visayas State University-Himamaylan City Campus, Philippines

DOI: https://doi.org/10.51244/IJRSI.2025.120800168

Received: 12 Aug 2025; Accepted: 18 Aug 2025; Published: 17 September 2025

ABSTRACT

This study examined the awareness and perception of Artificial Intelligence (AI)-driven learning tools among

Grade 12 STEM students in mathematics education. AI technologies, increasingly integrated into classrooms,

offer personalized learning, immediate feedback, and adaptive instruction. Using a quantitative descriptive-

correlational design, the study surveyed 63 STEM students from the only two secondary schools in

Himamaylan City offering the STEM strand during Academic Year 2025–2026. A researcher-developed Likert-

scale questionnaire measured students’ awareness of and perceptions toward AI-driven tools. Descriptive

statistics, Mann-Whitney U tests, and Spearman’s rho correlation were employed for analysis. Findings

revealed that students were highly aware of AI-driven learning tools (M = 4.19, SD = 0.54) and held an

appreciative perception of their usefulness (M = 3.68, SD = 0.66). No significant differences in awareness or

perception were found when classified by sex or socioeconomic status, suggesting equitable access to AI tools

across demographic groups. The absence of SES-related disparities may be attributed to school-provided

resources and inclusive technology policies. A moderate positive correlation (ρ = 0.424, p = .001) was found

between awareness and perception, indicating that greater familiarity with AI is associated with more positive

evaluations of its usefulness in mathematics learning. The results underscore the readiness of STEM students

for AI integration, highlighting that access alone is insufficient—critical engagement, ethical understanding,

and skillful application are essential for maximizing AI’s educational potential. These findings support the

need for AI literacy programs embedded within curricula to promote informed, equitable, and effective

adoption of AI technologies in secondary mathematics education.

Keywords: Artificial Intelligence (AI); STEM Education; Mathematics Learning; Awareness and Perception;

AI-Driven Learning Tools

INTRODUCTION

In today’s rapidly evolving world, driven by continuous technological advancements, Artificial Intelligence

(AI) has emerged as a transformative force across various sectors, including education. As societies

increasingly rely on digital technologies, AI offers adaptive and intelligent solutions tailored to the dynamic

needs of learners and educators (Holmes, Bialik, & Fadel, 2019). AI systems are capable of performing tasks

that typically require human intelligence, such as natural language understanding, pattern recognition,

problem-solving, and decision-making (Russell & Norvig, 2021). Within the educational landscape, AI

facilitates personalized learning experiences, delivers immediate feedback, and enhances outcomes for both

students and educators (Luckin & Holmes, 2016).

Initially applied to automate grading and administrative tasks, AI has evolved significantly due to advances in

machine learning and natural language processing. These developments have given rise to adaptive learning

platforms that customize educational content to individual learning styles while addressing both cognitive and

emotional aspects of student engagement (Luckin et al., 2016; Roll & Wylie, 2016; Holmes et al., 2019).

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In mathematics education, AI-powered tools such as intelligent tutoring systems and adaptive platforms

provide personalized instruction and timely feedback, which have been shown to improve student engagement

and academic performance. However, the success of these technologies hinges largely on the acceptance and

active engagement of both students and educators (Opesemowo & Ndlovu, 2024; Opesemowo & Adewuyi,

2024; Chhetri, 2022).

Public perception of AI in STEM education, as reflected in contemporary online discourse, tends to be

generally neutral to positive, characterized by optimism, curiosity, and a degree of trust in AI’s capabilities

(Smith-Mutegi, Mamo, Kim, Crompton, & McConnell, 2025). Nonetheless, ethical concerns persist, including

fears of AI misuse, over-reliance on technology, data privacy issues, and the potential displacement of human

educators, which continue to fuel ongoing debates (Stefanova & Georgiev, 2024).

These ethical concerns hold particular weight in school settings, where decisions about adopting new

technologies often involve balancing innovation with safeguarding student welfare. Issues such as data

privacy, equity of access, and the fear of diminishing the teacher’s role may shape how readily administrators,

educators, and parents accept AI integration in classrooms. Even with clear evidence of AI’s potential benefits,

hesitation may persist if stakeholders perceive risks as outweighing advantages. As highlighted by Zawacki-

Richter et al. (2019), concerns about trust, transparency, and ethical responsibility strongly influence the extent

to which AI applications are adopted in educational contexts. Thus, addressing these ethical considerations is

not only essential for fostering trust but also for ensuring the sustainable and responsible use of AI in schools.

While AI’s benefits, such as reducing cognitive load and providing real-time feedback, are well documented,

there remains a notable gap in research regarding how Senior High School students perceive and interact with

AI-driven learning tools (Panqueban & Huincahue, 2024). Furthermore, questions about students’ trust in AI

recommendations and the broader ethical implications of AI in education warrant further exploration.

For the purposes of this study, awareness is defined as the extent of Grade 12 STEM students’ knowledge and

understanding of AI technology, particularly its applications in mathematics education. Perception refers to

students’ attitudes, beliefs, and opinions concerning the usefulness and effectiveness of AI-driven learning

tools.

Gaining insight into students’ perceptions of AI in mathematics education is critical, as these views

significantly influence their willingness to adopt and engage with such technologies. Moreover, cultural

factors, perceived usefulness, and the enjoyment derived from AI tools are important determinants of student

acceptance and sustained engagement (Setälä, Heilala, Sikström, & Kärkkäinen, 2025).

This study aimed to address these gaps by examining the levels of awareness and perceptions of AI-driven

learning tools among Grade 12 STEM students in the context of mathematics education. By doing so, it seeks

to contribute to a deeper and more comprehensive understanding of AI adoption within educational settings.

Specifically, this study seeks to answer the following research questions:

What is the level of awareness of AI-driven learning tools among Grade 12 STEM students when taken as a

whole, and when classified by:

Sex

Socioeconomic status (SES)

What is the level of perception of Grade 12 STEM students regarding the usefulness of AI-driven learning

tools in mathematics education when taken as a whole, and when classified by:

Sex

Socioeconomic status (SES)

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Level of awareness

Is there a significant difference in the level of awareness of AI-driven learning tools among Grade 12 STEM

students when classified by:

Sex

Socioeconomic status (SES)

Is there a significant difference in the level of perception of AI-driven learning tools among Grade 12 STEM

students when classified by:

Sex

Socioeconomic status (SES)

Is there a significant relationship between the students’ level of awareness and their perception of the

usefulness of AI-driven learning tools in mathematics education?

Framework of the Study

This study is grounded in the Technology Acceptance Model (TAM) developed by Davis (1989), which posits

that individuals’ acceptance of technology is primarily influenced by two key factors: perceived usefulness and

perceived ease of use. Perceived usefulness refers to the belief that using a particular technology will enhance

one’s performance, while perceived ease of use pertains to the degree to which the technology is perceived as

effortless to use. Within the context of this study, TAM serves to explain how Grade 12 STEM students form

their perceptions of AI-driven learning tools in mathematics based on their awareness of these tools and their

beliefs regarding their effectiveness and usability.

To expand the analytical framework, this study also incorporates the Unified Theory of Acceptance and Use of

Technology (UTAUT) proposed by Venkatesh, Morris, Davis, and Davis (2003). UTAUT extends TAM by

introducing four additional determinants of technology adoption: performance expectancy, effort expectancy,

social influence, and facilitating conditions. Performance expectancy reflects students’ beliefs that AI tools can

improve their academic performance, while effort expectancy relates to the perceived ease of using such tools.

Social influence accounts for the impact of teachers, peers, and other significant individuals in shaping

students’ attitudes toward AI technologies. Facilitating conditions refer to the availability of necessary

resources, infrastructure, and support that enable technology use.

By integrating both TAM and UTAUT, this study offers a comprehensive framework that addresses both

individual perceptions and broader contextual factors influencing Grade 12 STEM students’ awareness and

perceptions of AI-driven learning tools in mathematics education.

METHODOLOGY

Research Design

This study employed a quantitative descriptive-correlational research design to investigate the levels of

awareness and perception of AI-driven learning tools among Grade 12 STEM students, specifically within the

context of mathematics education. This design was selected to enable the quantification and statistical analysis

of student responses, facilitating the identification of patterns, group differences, and relationships between

key variables.

According to Creswell and Creswell (2018), quantitative research involves the objective measurement and

analysis of data using statistical methods. It is particularly suitable for educational studies that aim to quantify

attitudes, opinions, behaviors, or other clearly defined variables. In this study, the primary variables examined

were: (1) students’ awareness of AI-driven learning tools, and (2) their perception of the usefulness and

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effectiveness of these tools in mathematics learning. Both variables were operationalized through measurable

indicators in a structured survey instrument, allowing for the collection of standardized data amenable to

statistical analysis of trends, correlations, and group differences.

The descriptive component of the design focused on assessing the overall level of students’ awareness of AI-

driven learning tools, including their general knowledge of AI and its applications in mathematics education.

This approach also enabled comparisons across demographic factors such as sex and socioeconomic status

(SES). Similar research by Kamoun, El Ayeb, Jabri, Sifi, and Iqbal (2024), as well as Yim and Wegerif (2024),

explored AI awareness among diverse student and teacher populations. For instance, Kamoun et al. (2024)

examined attitudes toward AI-driven models like ChatGPT among students and faculty, while Yim and Wegerif

(2024) assessed teachers’ perceptions and readiness to implement AI literacy programs, finding significant

variations in awareness linked to prior exposure to AI technologies.

The correlational component examined the relationship between students’ awareness of AI and their

perceptions of AI-driven learning tools. Correlational research is a non-experimental method that investigates

associations between variables without manipulation, allowing researchers to identify patterns and

relationships (Sutradhar, Adhikari, Sutradhar, & Sen, 2023). Related studies, such as Marrone, Zamecnik,

Joksimovic, Johnson, and De Laat (2024), used a correlational approach to explore students’ trust and

collaboration with AI systems, revealing that higher AI awareness was associated with more positive

perceptions and greater willingness to engage with AI in educational settings.

By integrating descriptive and correlational methods within a quantitative framework, this study offers a

comprehensive understanding of how Grade 12 STEM students’ awareness of AI varies across demographic

groups and how this awareness influences their perceptions of AI-driven learning tools in mathematics

education.

The Respondents of the Study

The respondents of this study were Grade 12 students enrolled in the STEM strand, selected from one section

in each of the only two secondary schools in Himamaylan City offering the STEM program for the Academic

Year 2025–2026. These students were chosen due to their direct relevance to the research objectives,

particularly their involvement in mathematics education where AI-driven learning tools may be integrated.

To qualify as respondents, students had to meet the following inclusion criteria:

Currently enrolled as Grade 12 STEM students;

Attending a secondary school within Himamaylan City;

Have exposure to mathematics education incorporating AI-driven learning tools.

Purposive sampling, a non-probability sampling technique, was employed to intentionally select participants

based on these specific characteristics relevant to the study (Etikan, Musa, & Alkassim, 2016). This approach

ensured that only students with the appropriate educational background and contextual experience were

included, thereby enhancing the relevance and richness of the data collected. Given that these sections

represented the entire population available and applicable to the study focus, purposive sampling was deemed

the most suitable method.

This sampling strategy enabled the researchers to gather comprehensive and representative data on students’

awareness and perceptions of AI-driven learning tools in mathematics education. By including the whole

population of interest rather than a subset, the study strengthened the validity of its findings within this specific

context.

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Data Gathering Procedure

Data for this quantitative descriptive-correlational study were collected using a researcher-developed

structured survey questionnaire. The instrument was divided into two main sections: the first assessed

students’ awareness of AI technology and its applications in mathematics education, while the second

evaluated their perceptions regarding the usefulness and effectiveness of AI-driven learning tools.

Prior to full administration, the questionnaire underwent expert validation by three mathematics-major teachers

with expertise in the field. Their qualitative feedback established content validity and confirmed the clarity and

relevance of the items. A pilot test was then conducted with 45 Grade 12 STEM students from schools outside

Himamaylan City but with similar academic profiles. Results of the pilot showed that the items were well

understood, and no revisions were necessary. Reliability of the instrument was confirmed using Cronbach’s

alpha, which yielded coefficients of .910 for the awareness scale (21 items) and .948 for the perception scale

(18 items), both indicating high internal consistency.

The researchers initiated the data collection process by submitting formal letters of request to the principals of

all STEM-offering secondary schools in Himamaylan City. Upon receiving approval, coordination with Grade

12 STEM advisers was arranged to schedule the administration of the questionnaires. Informed consent was

obtained from all participants prior to data collection.

On the agreed dates, the researchers personally administered the questionnaires during regular class hours to

maximize participation and address any student questions. Completed questionnaires were collected

immediately following administration.

The collected data were carefully organized, coded, and entered into a spreadsheet and statistical software to

ensure accuracy and confidentiality. Data entry and coding were cross-checked by the research adviser and a

statistician for completeness and correctness. The final dataset was then analyzed using appropriate statistical

methods aligned with the descriptive-correlational design to determine the levels of awareness, perceptions,

and relationships among the variables.

Research Instrument

The primary research instrument for this study was a researcher-developed structured survey questionnaire

designed to assess Grade 12 STEM students’ awareness of and perceptions toward AI-driven learning tools in

mathematics education. The instrument consisted of two components, both measured using a 5-point Likert

scale.

Awareness Scale (Cronbach’s α = .910)

This section measured students’ knowledge and understanding of AI-driven learning tools, including general

AI concepts and specific applications in mathematics education. Responses ranged from “Fully Aware” to

“Not aware at All.”

Perception Scale (Cronbach’s α = .948)

This section assessed students’ attitudes, beliefs, and opinions regarding the usefulness and effectiveness of

AI-driven tools in mathematics education. Statements covered areas such as learning improvement,

personalized instruction, and problem-solving support. Students responded on a 5-point scale ranging from

“Strongly Agree” to “Strongly Disagree.”

The questionnaire was validated by three mathematics-major teachers through qualitative feedback and pilot-

tested with 45 Grade 12 STEM students outside the target population. Since pilot participants demonstrated

clear understanding of the items, no revisions were made. The final version of the survey was therefore

considered both valid and reliable for assessing awareness and perceptions of AI-driven learning tools in

mathematics education.

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Data Analysis Method

This study utilized both descriptive and inferential statistical techniques to address its objectives regarding

Grade 12 STEM students’ awareness and perception of AI-driven learning tools in mathematics education.

In the initial phase, descriptive statistics were employed to summarize and interpret key variables. Measures of

central tendency, specifically the mean, were calculated to determine the average levels of awareness and

perception among respondents. Standard deviations were used to assess the variability of responses, offering

insights into how consistently or inconsistently students rated their knowledge and attitudes. These analyses

provided a general overview of the students’ understanding of and attitudes toward AI tools in the mathematics

classroom.

Descriptive statistics were also applied to examine variations in awareness and perception scores across

demographic subgroups, such as sex and socioeconomic status (SES). This step helped identify whether

particular demographic factors influenced students' familiarity with or attitudes toward AI-driven technologies.

To determine the relationship between students’ awareness of AI tools and their perceptions of these tools in

mathematics education, the study employed Spearman’s rank-order correlation coefficient (ρ). This non-

parametric statistical method was selected due to its appropriateness for analyzing ordinal or non-normally

distributed data. It measured the strength and direction of a monotonic relationship between the two variables.

A positive and statistically significant correlation would suggest that greater awareness is associated with more

favorable perceptions.

Additionally, the Mann–Whitney U test was used to assess whether significant differences existed in awareness

and perception scores between independent groups—such as male and female students, or different SES

categories. As a non-parametric test, the Mann–Whitney U was appropriate for comparing mean ranks when

the assumptions of parametric tests (e.g., normal distribution) could not be met.

All statistical analyses were performed using SPSS (Statistical Package for the Social Sciences). This software

was selected for its reliability and widespread use in educational and social science research. SPSS facilitated

the computation of descriptive statistics, correlation coefficients, and non-parametric tests, ensuring systematic

and accurate interpretation of the data collected.

Ethical Considerations

The researchers upheld the ethical principles of informed consent, anonymity, and data confidentiality

throughout the conduct of the study. Respondents were provided with a briefing on the study’s purpose,

procedures, and their rights, including the right to withdraw at any time without penalty. No personally

identifiable information was collected in the survey.

The questionnaire was designed to be respectful and age-appropriate, aligning with ethical standards for

research. All responses were kept confidential and were stored in secure digital formats accessible only to the

research team and their adviser. The results of the study were reported in aggregate form and were intended

solely for academic purposes.

RESULTS AND DISCUSSION

This section presents and interprets the findings of the study on Grade 12 STEM students’ level of awareness

and perceptions regarding the usefulness of AI-driven learning tools in mathematics education. Using both

descriptive and inferential statistical analyses, the results provide insights into students’ familiarity with AI

technologies and their attitudes toward their application in the classroom. The findings are discussed in relation

to existing literature and theoretical frameworks to highlight key patterns, implications, and areas for further

inquiry.

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Table 1

The Level of Awareness of Ai-Driven Learning Tools Among Grade 12 Stem Students When Taken as a

Whole, and When Classified by Sex and Socioeconomic Status

Variable

Category

Mean

Description

Sex

Male

3.74

.69

Appreciative

Female

3.64

.64

Appreciative

Socio-economic Status

Low

3.74

.65

Appreciative

High

3.62

.67

Appreciative

As an Entire Group

3.68

.66

Appreciative

Note: 4.51-5.00 Advocative; 3.51-4.50 Appreciative; 2.51-3.50 Neutral; 1.51-2.50 Doubtful; 1.00-1.50

Dismissive

The data revealed that Grade 12 STEM students hold a positive perception regarding the usefulness of AI-

driven learning tools in mathematics classes. As a whole, the group obtained a mean score of 3.68 (SD = 0.66),

which falls under the "Appreciative" category. This indicates that students generally acknowledge the benefits

of integrating AI technologies into their learning experience and view these tools as supportive in

understanding and engaging with mathematical concepts.

When considered as a whole, the respondents obtained a mean score of 3.68 (SD = 0.66), which falls within

the Appreciative range (3.51–4.50). This indicates that, on average, students value and recognize the relevance

and benefits of AI-based learning tools in enhancing their mathematics learning experiences. This perception

aligns with Bulman and Fairlie (2016), who argued that digital technologies, including AI, improve

educational outcomes by promoting personalized learning and engagement, especially in technical subjects

like mathematics.

The data show that both male and female Grade 12 STEM students hold an “Appreciative” perception of AI-

driven learning tools in mathematics, with males reporting a slightly higher mean (M = 3.74, SD = 0.69) than

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females (M = 3.64, SD = 0.64). Although not statistically significant, this difference aligns with global trends

in gender and AI engagement.

Otis et al. (2024) reported a consistent gender gap in generative AI usage across more than 140,000

individuals. Their findings suggest that women are less likely to use AI tools, even when access is equal. This

gap is shaped by social, cultural, and institutional factors, which may also influence how students perceive and

engage with AI in education. The slight disparity observed in the current study may reflect these broader

dynamics, including differences in confidence and familiarity with technology.

The results showed that low-SES students reported a slightly higher perception of AI-driven learning tools (M

= 3.74, SD = 0.65) than high-SES students (M = 3.62, SD = 0.67), although both groups remained in the

“Appreciative” category. This suggests that low-SES students may view these tools as more beneficial, likely

due to their potential to supplement limited academic support. Eynon and Geniets (2015) explained that

digitally excluded youth often develop focused, outcome-driven internet use due to restricted access and fewer

support networks. In this context, AI tools may be seen not simply as enhancements but as necessary

instruments to meet learning needs.

The slightly higher mean score may also reflect a deeper reliance on technology for academic success among

low-SES students. While high-SES students often have broader digital exposure and access to various

educational resources, low-SES learners may perceive AI tools as more impactful because they fill critical

gaps. The results point to the importance of contextual factors, where access and motivation influence how

students evaluate the usefulness of digital learning tools.

Table 3 Differences in the Awareness of AI-driven Learning Tools Among Grade 12 STEM Students

Mean Rank

Sum of Ranks

Mann-Whitney U

Sig

Sex

Male

34.24

924.50

425.50

.400

Female

30.32

1091.50

SES

Low

32.25

1032.00

488.00

.912

High

31.74

984.00

Note: *p < .05 indicates a significant difference

The results of the Mann-Whitney U test reveal no statistically significant difference in the awareness of AI-

driven learning tools among Grade 12 STEM students when classified by sex or socioeconomic status. This

finding highlights an encouraging trend toward equitable exposure to educational technology across different

demographic groups within the senior high school STEM track.

Specifically, the analysis showed that sex does not significantly influence students’ awareness (U = 425.50, p =

.400). Both male and female respondents demonstrated comparable levels of awareness of AI tools used in

mathematics education. This suggests that students, regardless of gender, are receiving relatively uniform

access to digital learning platforms that incorporate artificial intelligence. Such parity reflects a broader shift

observed in contemporary research. According to the OECD (2018), gender disparities in digital literacy and

access to educational technology are steadily diminishing, particularly in STEM-focused environments where

instructional tools are distributed more equally.

In terms of socioeconomic status, the data likewise indicate no significant difference in AI awareness between

students from low and high SES backgrounds (U = 488.00, p = .912). This outcome is particularly notable

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given the extensive body of research that has linked SES with disparities in access to technology. Traditionally,

students from higher-income families have been more likely to use advanced digital tools due to home access

and additional resources. However, the results of this study suggest that school-based interventions and shared

digital infrastructures may be mitigating those disparities. Factors such as institutionally provided AI

platforms, uniform digital curricula, and public access initiatives may be contributing to a more level playing

field. While earlier studies such as Van Deursen and van Dijk (2014) emphasized the negative impact of SES

on digital access and skills, more recent efforts are making a measurable difference. For example, UNESCO

(2022) reported that many educational institutions have adopted inclusive technology policies aimed at

reducing digital inequities by ensuring that all students, regardless of background, can access AI-supported

learning tools.

The results showed that the absence of significant differences in AI awareness by sex and socioeconomic status

reflects a growing inclusivity in the digital learning environment. As AI continues to shape educational

practices, ensuring equal access across student demographics remains critical. These findings suggest that, at

least within this sample of STEM students, efforts toward digital equity are yielding positive outcomes, paving

the way for more balanced opportunities in technologically enhanced education.

Table 4 Differences in the Perceptions of AI-driven Learning Tools Among Grade 12 STEM Students

Mean Rank

Sum of Ranks

Mann-Whitney U

Sig

Sex

Male

33.30

899.00

451.00

.626

Female

31.03

1117.00

SES

Low

33.27

1064.50

455.50

.577

High

30.69

951.50

Note: *p < .05 indicates a significant difference

The analysis aimed to determine whether students’ sex or SES significantly influences their perception of the

usefulness of AI-driven learning tools in mathematics education.

Results showed no significant difference in the male and female students’ perception of the usefulness of AI-

driven learning tools (U = 451.00, p = .626). This suggests that male and female students generally perceive

AI-driven learning tools in similar ways. This implies that gender does not meaningfully affect how students

evaluate or appreciate the usefulness of AI in their academic learning, particularly in mathematics. This is

supported by Gerard, J., Singh, S., Macleod, M., McKay, M., Rivoire, A., T. Chakraborty, & Singh, M. (2024),

whose study involved 1, 211 higher education students across Northern Ireland and Indi, found no statistically

significant effect of gender on perceptions of AI tools after controlling for institution and subject area. Their

study suggested that both male and female students had equal opportunities to explore AI tools, leading to

similar attitudes toward their usefulness. Likewise, OECD (2018) has documented a narrowing digital gender

divide, especially in formal school environments, where both genders increasingly have equal access to digital

tools, including AI-assisted learning platforms.

Research by Qazi et al. (2021) showed that gender-related differences in technology use and self-efficacy are

generally small and not consistently significant, supporting the view that male students may display slightly

higher confidence—but the gap is modest and closing.

In terms of SES, the analysis also revealed no significant difference across groups. This indicates that students

from various economic backgrounds perceive AI-driven learning tools similarly in terms of usefulness. This

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could be attributed to equal access provided by the school, such as shared devices, internet access, and

institutional platforms that reduce disparities in technological exposure.

While earlier studies (e.g., Van Dijk, 2005) showed that students from higher SES backgrounds tend to have

more positive perceptions and experiences with technology due to better access at home, recent efforts to

bridge the digital divide may have mitigated this effect. For instance, UNESCO (2022) notes that schools

increasingly ensure equal access to digital resources, particularly in STEM-oriented tracks, by integrating AI

tools directly into classroom instruction.

Furthermore, Zawacki-Richter et al. (2019) emphasize that when AI tools are embedded within structured

learning environments and supported by teachers, students’ perceptions tend to converge, regardless of their

personal or economic background

Table 5 Relationship Between Students’ Awareness and Perception of the Usefulness of AI-driven Learning

Tools in Mathematics Education

Awareness

Perception

Spearman's rho

Awareness

Correlation

Coefficient

1.000

.424

Sig. (2-tailed)

.001

Perception

Correlation

Coefficient

.424

1.000

Sig. (2-tailed)

.001

Note: p < .01 – Significant at .01 level

Spearman’s rho: ±0.1 to ±0.3 – Small/Weak, ±0.3 to ±0.5 – Medium/Moderate,

±0.5 to ±1.0 – Large/Strong

The result of the Spearman’s rho analysis indicates a moderate positive correlation between students’ level of

awareness and their perception of the usefulness of AI-driven learning tools in mathematics education. The

correlation coefficient of 0.424 suggests that as students become more aware of AI technologies and how they

function, their perception of these tools as beneficial to their learning also increases. The p-value of 0.001

confirms that this relationship is statistically significant at the 0.05 level, indicating that the observed

correlation is unlikely due to chance.

This finding aligns with prior research showing that awareness and familiarity with technology often enhance

its perceived usefulness. For instance, Davis (1989) in his Technology Acceptance Model (TAM) emphasized

that both perceived usefulness and perceived ease of use influence technology adoption, with awareness

playing a critical role in shaping perceptions.

Similarly, Zawacki-Richter et al. (2019) in their systematic review of AI applications in education concluded

that a user’s awareness and understanding of AI significantly affect their willingness to engage with AI-

powered educational tools. They noted that students who are informed about how AI adapts to learning styles,

provides immediate feedback, and supports personalized instruction tend to view such tools more positively.

In the context of mathematics education, where abstract concepts and problem-solving skills are central,

awareness of how AI can scaffold learning, automate practice, and provide intelligent feedback enhances the

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perceived value of these tools. For example, Holstein et al. (2019) found that when students understood how

AI-powered tutoring systems functioned, their trust in and engagement with the tool increased significantly,

leading to more effective use.

Moreover, Chen et al. (2020) discovered that students who had higher exposure to AI-supported platforms in

mathematics showed not only improved performance but also a more positive attitude toward such

technologies, driven largely by their increased familiarity and understanding.

CONCLUSION

The findings of this study present a compelling narrative of technological readiness and equitable access

among Grade 12 STEM students in the context of AI-driven learning tools in mathematics education. The data

revealed a high overall level of awareness and a positive perception of usefulness among students, regardless

of sex or socioeconomic status. These results suggest that AI technologies are becoming well-integrated into

the academic experiences of senior high school learners, reflecting successful institutional efforts to

democratize access to digital resources.

No significant differences were found in students’ awareness or perception when classified by sex or SES,

indicating that barriers traditionally associated with gender and economic background are being mitigated

through structured school-based interventions and inclusive digital policies. This progress highlights the

growing impact of equitable technology distribution and curriculum standardization in reducing digital

disparities. Furthermore, the significant relationship between awareness and perception reinforces the

importance of informed exposure, as familiarity with AI tools directly influences students’ recognition of their

academic value.

These outcomes point to a vital transition in STEM education. It is no longer sufficient for students to merely

be exposed to AI tools. To fully realize the educational potential of AI, schools must move beyond access and

ensure that learners develop the skills to engage with these technologies critically, ethically, and effectively.

Embedding AI literacy, promoting practical application, and fostering reflective understanding will be essential

in preparing students to thrive in increasingly AI-enhanced academic and professional environments. The study

ultimately affirms that Grade 12 STEM students are not only ready for AI integration but also positioned to

become capable and responsible users of these transformative educational tools.

RECOMMENDATIONS

Based on the findings of this study, the following recommendations are proposed for educators, school

administrators, policymakers, and future researchers:

Integrate AI Education into the STEM Curriculum. Given the high level of awareness and positive

perceptions among students, schools may consider formally incorporating AI-related content into the STEM

curriculum, especially in mathematics subjects. Lessons could include practical applications of AI-driven

learning tools, helping students deepen their understanding and usage of emerging technologies.

Promote Equitable Access to AI Tools Across All Student Groups. Although no significant differences in

awareness or perception were observed based on sex or socioeconomic status, efforts may continue to ensure

that access remains equitable. Schools should maintain or expand access to digital devices, internet

connectivity, and AI-based learning platforms across all student demographics to sustain inclusive and

technology-rich learning environments.

Offer Training and Support for Students and Teachers. To maximize the effectiveness of AI tools in the

classroom, training sessions or orientation programs may be provided. These should help both students and

educators understand how to navigate and fully utilize AI-driven platforms for personalized and interactive

mathematics learning.

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Encourage Student-Centered and Interactive Use of AI. Educators may design activities that allow students 
to actively engage with AI tools, such as intelligent tutoring systems or adaptive problem-solving applications. 
Doing so can enhance not only mathematical understanding but also digital literacy and critical thinking skills. 
Monitor and Evaluate AI Integration Regularly. School administrators and IT coordinators may implement 
mechanisms  for  regularly  assessing  how  AI  tools  are  used  in  classrooms,  including  their  effectiveness  in 
improving  student  engagement  and  academic  performance.  Feedback  from  both  students  and  teachers  can 
guide adjustments to the implementation of these tools. 
Support Further Research. Future studies may examine the long-term impact of AI-driven learning tools on 
academic achievement across various disciplines, not just mathematics. Researchers are encouraged to explore 
additional  variables  such  as  students'  motivation,  digital  literacy,  and  learning  outcomes  in  AI-integrated 
environments. 
By  implementing  these  recommendations,  educational  institutions  can  better  prepare  students  for  a  future 
where  AI  literacy  is  increasingly  essential,  while  also  creating  learning  environments  that  are  engaging, 
inclusive, and technologically forward. 
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