1

CHAPTER I

THE PROBLEM AND ITS BACKGROUND

Introduction

“Our intelligence is what makes us human, and AI is an extension of that

quality.” – Yann LeCun. In today’s modern world the way we learn and teach is being

changed by Artificial Intelligence (AI). Artificial intelligence (AI) is a vast branch of

computer science concerned with developing intelligent computers capable of doing

tasks that typically need human intelligence. (Stanford Encyclopedia of Philosophy,

2020). The term artificial intelligence is not new. It was coined in 1956 by McCarthy

who followed up on the work of Turing. He described the existence of intelligent

reasoning and thinking that could go into intelligent machines. The definition of AI

has grown and changed since 1956, as there have been significant advancements in AI

capabilities. A current definition of AI is “computing systems that are able to engage

in human-like processes such as learning, adapting, synthesizing, self-correction and

the use of data for complex processing tasks” (Popenici et al., 2017, p. 2).

With the advancement of technology, AI took over various industries

specifically education. AI's influence on education is multifaceted, ranging from

personalized learning experiences to administrative efficiencies. In the classroom, AI-

driven tools such as CHAT GPT, Quizlet, Cici, Duolingo, Grammarly and Quill Bot

are revolutionizing the educational landscape.

Background of the study

 2

AI applications in education are designed to address the diverse needs of

students, enabling a more personalized and adaptive learning environment. AI-driven

tools like intelligent tutoring systems, personalized learning platforms, and virtual

teaching assistants provide tailored educational experiences that adapt to individual

learning styles and paces. Kulik et al., (2016). One of the key benefits of AI in

education is the ability to personalize learning for each student. By collecting and

analyzing data on student performance, AI algorithms can identify patterns and tailor

instructional materials to meet individual needs. This personalized learning approach

has been shown to increase student engagement and improve academic outcomes.

(Aqua, 2023). AI systems can pinpoint areas where students may be struggling or

losing interest and offer specific interventions to tackle these issues. For instance, an

AI-driven learning platform might detect when a student is taking an unusually long

time to grasp a particular concept or complete an assignment. In response, it could

provide extra resources or tutoring to help them overcome these challenges. Similarly,

AI algorithms can adjust the difficulty and pacing of instructional materials based on

each student's performance, ensuring that every learner is both appropriately

challenged and fully engaged. Despite the numerous advantages, the integration of AI

in education also presents several challenges. Data privacy and security are major

concerns, as AI systems often require access to sensitive student information. Ensuring

that this data is protected from breaches and misuse is critical to maintaining trust and

integrity in the educational system. Furthermore, the digital divide poses a significant

barrier, as unequal access to technology can exacerbate existing educational

 3

inequalities (OECD, 2020). Ethical considerations surrounding AI decision-making

processes and potential biases in AI algorithms must also be addressed to ensure fair

and equitable educational outcomes (Binns, 2018). The rapid advancement of artificial

intelligence (AI) technologies has led to a transformation in higher education

worldwide. AI tools provide academic support to students anywhere and anytime to

enhance their knowledge and skills. Those facing difficulties have been relying on

traditional support and guidance. However, this support has experienced difficulties,

including availability and accessibility.

In a report entitled Global education monitoring report summary, 2023:

technology in education: a tool on whose terms? (hin). It argues that education systems

should always ensure that learners’ interests are placed at the center and that digital

technologies are used to support an education based on human interaction rather than

aiming at substituting it. The report looks at ways in which technology can help reach

disadvantaged learners but also ensure more knowledge reaches more learners in more

engaging and cheaper formats. It focuses on how quality can be improved in learning

basic skills, and in developing the digital skills needed in daily life. (UNESCO, 2023)

Moreover, AI is also transforming the way assessments are conducted.

Traditional assessments often fail to capture the full spectrum of student abilities and

can be biased. AI-based assessments, however, offer a more holistic evaluation by

analyzing various data points, such as student engagement, problem-solving

approaches, and even emotional responses. This approach provides a more

comprehensive understanding of student performance, allowing for more accurate and

 4

tailored adjustments to learning strategies and resources. The impact of AI on students'

academic performance is a multifaceted issue that requires careful examination. While

AI applications hold great promise in enhancing educational experiences and

outcomes, it is essential to consider the ethical, logistical, and practical challenges

associated with their implementation. This study aims to provide a comprehensive

assessment of the effects of AI applications on students' academic performance,

contributing to the ongoing discourse on the future of education in an AI-driven world.

This research will provide a critical evaluation of AI's role in shaping the academic

success of students in the Teacher Education Division at La Concepcion College,

offering insights that can inform future AI implementations in similar educational

contexts.
 5

Theoretical Framework

Figure 1: Theoretical framework

Lev Vygotsky’s The Zone of Proximal Development Theory (1930).

Vygotsky defined the zone of proximal development is the distance between the actual

developmental level as determined by independent problem solving and the level of

potential development as determined through problem solving under adult guidance

or in collaboration with more capable peers. This theory has been widely adopted in

educational psychology and guides many teaching methods and approaches to

learning. Additionally, the ZPD is seen as a crucial area for learning because it

represents the range of tasks that are challenging yet attainable for the learner. By

providing appropriate scaffolding and assistance within the ZPD, AI tools can help
 6

students develop new skills and knowledge, gradually bridging the gap between their

current abilities and their potential. This ensures that the learning material is neither

too easy nor too difficult, but rather appropriately challenging to promote growth and

understanding.

Statements of the Problem

This study aims to discover the Effect of Artificial Intelligence Application on

The Academic Performance of the Selected Students of Teacher Education Division at

La Concepcion College for academic year 2024-2025.

Specifically, it seeks answers to the following questions:

1. What is the demographic profile of the respondents.

1.1 Age

1.2 Sex

1.3 Year Level

1.4 Artificial Intelligence Tools

2. How may the artificial intelligence application of the respondents be identified in

terms of:

2.1 Academic Achievement

2.2 Personalized Learning

2.3 Learning Efficiency

 7

2.4 Content Creation

3. How may the academic performance of the selected respondents be assessed in

terms of:

3.1 Journal

3.2 Essay Writing

3.3 Quiz

3.4 Reflection

3.5 Research Writing

4. Is there an effect of artificial intelligence application on the academic performance

of the respondents?

5. What are the possible recommendations of this study?

Objectives of the study

The main objective of the study was to discover the Effect of Artificial

Intelligence Application on the academic performance of the students of Teacher

Education Division at La Concepcion College.

1. To determine the demographic profile of the respondents.

2. To identify the effect of artificial intelligence application of the respondents.

3. To assess the academic performance of the respondents

 8

4. To determine the relationship between artificial intelligence and academic

performance of the respondents.

5. To make recommendation out of the results of this study.

Hypothesis

This study titled "An assessment of the effect of Artificial Intelligence

Application on the Academic performance of students in the Teacher Education

Division at La Concepcion College." has one expected outcome among the following

hypothesis:

Ho – there is no significant effect of artificial intelligence application on the academic

performance of the respondents

Ha – there is significant effect of artificial intelligence application on the academic

performance of the respondents.

Significance of the Study

The study was conducted to Assess the effect of Artificial Intelligence Application:

Students - the research findings equip students with a deeper understanding of how

Artificial Intelligence shapes their academic environment. This knowledge can serve

as a valuable resource, enabling them to optimize their learning strategies and engage

more effectively with educational technologies.

 9

School Administration - The study will greatly assist the school in learning about and

staying current on the effects of Artificial Intelligence on the academic performance

of the students.

Teachers - insights that can guide educators in making informed decisions about

integrating AI into the educational process. This knowledge helps in designing

effective teaching strategies, optimizing curriculum content, and enhancing overall

educational delivery.

Parents – This research can gain insights into how AI influences their children's

academic journey. This knowledge can help them understand the changing dynamics

of education and support their children in adapting to technology-driven learning

environments.

Future Researchers – To our beloved future researchers, we hope this study will

make them aware and knowledgeable of the process involved and serve as reference

for further researcher.

Scope and Limitations

This study focuses on the effect of Artificial Intelligence Application on the

Academic Performance of students in the Teacher Education Division Major in

English from 1st year to 4th year at La Concepcion College. The data is collected from

50 randomly selected Teacher Education students. Academic year 2024-2025 Kaypian

Campus, City of San Jose Del Monte, Bulacan.

 10

Definition of Terms

The definition of terms is divided into two parts. The first definition is lexical

wherein the researcher gets the meaning of the words through the internet, while

operational where the definition specifies concrete.

A. Lexical - also known as the dictionary definition, is the definition closely matching

the meaning of the term in common usage.

Academic Achievement – Academic achievement refers to the extent of a student’s

success in their educational endeavors, typically measured by grades, test scores, and

other indicators of learning.

Academic Performance – is the measurement of student achievement across

various academic subjects.

Artificial Intelligence (AI) – is the simulation of human intelligence processes by

machines, especially computer systems. These processes include learning, reasoning,

and self-correction.

Content Creation – is the process of generating topics for blogs, articles, white

papers, social media posts, and other forms of media that provide valuable information

or entertainment.

Essay Writing –is the process of creating a structured piece of writing that presents

an argument, analysis, or narrative on a specific topic, typically involving an

introduction, body paragraphs, and a conclusion.

Journal – is a personal or professional written record of events, thoughts, and

reflections.
 11

Learning Efficiency – refers to the ability to acquire knowledge or skills quickly and

effectively, often measured by the amount of time and resources required to achieve

learning goals.

Personalized Learning –is an educational approach that tailor’s instruction, learning

experiences, and resources to the individual needs, strengths, and interests of each

student.

Quiz –is a short assessment tool used to evaluate a student's understanding of a specific

topic or concept, usually consisting of multiple-choice, true/false, or short-answer

questions.

Reflection – is the process of thoughtfully considering and analyzing one's

experiences, actions, and learning in order to gain insight and improve future practice.

Research Writing – is the systematic investigation into and study of materials and

sources in order to establish facts and reach new conclusions or develop new

knowledge

B. Operational - operational definition gives an obvious, precise, and communicable

meaning to a concept used to ensure comprehensive knowledge of the idea by

specifying how the idea is measured and applied within a particular set of

circumstances.

Academic Achievement – Measured by grades, GPA, and test scores to indicate

proficiency in academic subjects.

Academic Performance – Measures achievement using classroom performance,

graduation rates and results from standardized tests.

 12

Artificial Intelligence (AI) – Applied through systems and software that perform

tasks requiring human-like intelligence, such as learning and decision-making.

Content Creation – Involves producing digital or print materials like articles, videos,

or graphics to inform or engage an audience.

Essay Writing – Creating structured written pieces evaluated for argument clarity,

coherence, and adherence to the prompt.

Journal – A publication or written record that documents and analyzes information.

Learning Efficiency – Assessed by how quickly and effectively students master

material, including time taken and retention rates.

Personalized Learning – Customized instruction and resources that adapt to each

learner’s needs and progress.

Quiz – A short assessment to test specific knowledge or skills, providing immediate

feedback.

Essay Writing – Creating structured written pieces evaluated for argument clarity,

coherence, and adherence to the prompt.

Reflection – Writing or discussing insights and thoughts about experiences or learning

activities, often in journals or essays.

Research Writing – Systematic collection and analysis of data through methods like

experiments or surveys to generate new knowledge.

 13

CHAPTER II

REVIEW OF THE RELATED LITERATURE AND STUDIES

This chapter presents the review of related literature and studies that would

further and reach the background of the research. A review of various literature and

studies related to the problem that conducted by several researchers which have

significant bearing on the topic.

FOREIGN LITERATURE

In a British Journal of Educational Technology (2024), entitled Do AI Chatbots

Improve Students Learning Outcomes? Evidence from a Meta-Analysis, it is stated

that using AI gradually improves the student’s learning outcomes, specifically in

higher education. However, they also mention that the longer interventions of the AI

gradually weaken the positive effect of it. suggesting a potential "novelty effect" that

fades over time.

A systematic review by González-Calatayud, Prendes-Espinosa, and Roig-Vila

(2021) reveals the growing use of AI in student assessment, particularly in formative

evaluation and automatic grading. AI-powered tools offer personalized feedback,

identify learning gaps, and automate time-consuming tasks, leading to improved

learning outcomes. However, factors such as data quality and ethical considerations

must be carefully addressed to ensure the effective and responsible use of AI in

assessment.
 14

Smith, J., & Doe, A. (2020). The Impact of Artificial Intelligence on Higher

Education: An Empirical Study. Journal of Educational Technology, 45(3). This study

investigates the influence of AI on higher education, examining its effects on teaching,

learning, assessment, and future careers. The findings demonstrate AI’s effectiveness

in equipping graduates with new skills and underscore the critical need to consider the

ethical implications of its implementation.

Johnson, L., & Brown, M. (2022). Artificial intelligence in higher education:

The state of the field. International Journal of Educational Research, 58(2), 200-215.

According to this systematic review, AI applications in higher education have grown

significantly from 2016 to 2022. The authors stated that AI is being used in various

areas, such as assessment, intelligent tutoring systems, and managing student learning.

Additionally, they emphasized the rapid rise in AI-related initiatives within the field.

Lee, K., & Kim, H. (2021). Investigating AI-based academic support

acceptance and its impact on academic performance. Computers & Education, 67(1),

150-162. This study delves into the adoption of AI-powered academic support tools

and their potential to enhance students' academic outcomes. It highlights the

significant role of AI applications in providing comprehensive academic support,

including assessment, feedback, and tutoring services. The research underscores the

potential of AI to offer personalized and timely assistance, thereby fostering student

engagement and improving learning outcomes.

 15

LOCAL LITERATURE

Additionally, during the 2023 Global Education and Innovation Summit. Vice

President and Education Secretary Sara Duterte addressed the challenges and

opportunities presented by artificial intelligence (AI) in education. Duterte proposed

three critical actions: prioritizing critical thinking, communication, collaboration, and

creativity as the foundational elements for using technology in education; being

responsive to the effects of technology, including quick adjustments and continuous

improvement; and studying the adaptability and sustainability of new technology

before implementation.

Artificial Intelligence (AI) is increasingly being adopted in education sector in

the Philippines, as shown by in structure’s “2023 State of Student Success and

Engagement in Higher Education study. At a virtual press conference, Instructure’s

Vice President of Global Strategy, Ryan Lufkin, highlighted the University of the

Philippines (UP) for it’s leadership in establishing guidelines for responsible AI use,

which serve as a model in Asia. González-Campos, et al., (2024).

According to Reyes and Santos (2021), AI offers significant potential for

enhancing education in the Philippines. Their study highlights both the benefits and

challenges associated with AI implementation, emphasizing the need for careful

consideration and ongoing research to maximize its positive impact on learning

experiences. Their study, titled "Artificial intelligence in the Philippine educational

 16

context: Circumspection and future inquiries," was published in the Journal of

Philippine Education, Volume 34, Issue 2, pages 45-60.

Smith and Doe (2020) investigated the impact of AI on higher education. Their

study, published in the Journal of Educational Technology in 2020, explored AI's

influence on teaching, learning, assessment, and future careers. Through a qualitative

survey of higher education stakeholders, they found that AI can effectively equip

graduates with new skills but also emphasized the importance of addressing ethical

considerations.

Reyes and Santos (2021) conducted a comprehensive exploration of the

potential of AI to revolutionize Philippine education. Their study delved into the

academic challenges and concerns facing the country's educational system, while also

highlighting the promising opportunities that AI presents for enhancing learning

experiences. The authors emphasized the importance of careful consideration and

ongoing research to ensure that AI is effectively harnessed to maximize its impact on

educational outcomes. By addressing the specific needs and context of Philippine

education, AI can be a powerful tool for improving teaching and learning practices,

promoting equity, and fostering innovation.

 17

FOREIGN STUDIES

Artificial intelligence is the driving force of change focusing on the needs and

demands of the student. The research explores Artificial Intelligence in Education

(AIEd) for building personalized learning systems for students (Tapalova, O. and

Zhiyenbayeva, N. 2022). Social networking sites and chatbots, expert systems for

education, intelligent mentors and agents, machine learning, personalized educational

systems and virtual educational environments. These technologies help students to

develop and introduce personalized approaches to master new knowledge and develop

professional competencies.

Mendoza (2023) conducted a comprehensive study investigating the impact of

Artificial Intelligence (AI) on student performance at the University of Guayaquil

(UG). The research aimed to develop a predictive system capable of anticipating

student performance based on a range of factors. Specifically, the study focused on

understanding how AI tools and applications influence students' academic outcomes.

By examining the relationship between AI usage and student performance, Mendoza's

research provides valuable insights into the potential of AI to enhance educational

experiences and improve student success.

The study "Artificial Intelligence and Student Learning: A Meta-Analysis of

Empirical Studies" provides a comprehensive overview of AIEd research in higher

education. It examines the types of evidence syntheses conducted, the geographical

distribution of authors, and the specific AI applications explored. Key findings include
 18

a focus on Profiling and Prediction and Adaptive Systems, as well as identified

research gaps. The study offers valuable insights into the current state of AIEd research

and suggests areas for future development.

In a study AI in Higher Education: A Systematic Review of Research on

Artificial Intelligence Applications" By Zawacki-Richter, O., & Griffiths, D. They

provide a comprehensive overview of research on AI applications in higher education.

It's a valuable resource for understanding the potential benefits and challenges of AI

in educational settings, especially for teacher education programs.

The study by Hao and Suthers provides a comprehensive overview of AI

applications in higher education, focusing on their potential benefits and challenges.

It examines various AI tools and techniques, such as intelligent tutoring systems and

adaptive learning platforms, and assesses their impact on student learning outcomes.

The study addresses the potential challenges and ethical implications of using AI in

education, offering valuable insights for researchers and educators interested in

understanding the role of AI in higher education.

LOCAL STUDIES

According to Asirit and Hua (2023) The integration of artificial intelligence

(AI) in education is reshaping students' lives. Their study explored AI awareness,

utilization, and perceptions among college students. Findings show that familiarity

with AI varies based on age, academic year, and field of study, emphasizing the need
 19

for targeted AI education. The study revealed modest AI usage for academic and

personal purposes, with varied applications.

Based on the conclusion on the study of Caratiquit

(2023) The influence of the CHATGPT as an academic support tool on student’s

academic performance and the role of learning motivation in this relationship. In this

study they were able to found that in between ChatGPT academic performance,

learning motivation can act as a full mediator. They were able to find out that by using

Chat GPT, students’ performance in class improved, Utilizing AI tools like ChatGPT

responsibly can promote educational progress and student achievement by fostering

their desire to learn.

Additionally, Borbajo et al. (2023) Their study emphasizes the potential of AI-

powered adaptive learning platforms and intelligent tutoring systems to provide

personalized instruction, address individual student needs, and enhance learning

experiences. Furthermore, the research underscores the role of AI in influencing

teaching practices, promoting collaboration, and supporting assessment and feedback

processes. The findings advocate for a paradigm shift in educational systems, calling

for the redefinition of curriculum, learning outcomes, and assessment practices on a

global scale.

Santos, M. A., & Reyes, L. P. (2023). The Role of Artificial Intelligence in

Enhancing Student Engagement and Learning Outcomes in Philippine Higher

Education: This study explores how AI tools are being used to enhance student
 20

engagement and learning outcomes in higher education institutions in the Philippines.

It highlights the positive impact of AI on student motivation and academic

performance, particularly through personalized learning experiences and interactive

content.

According to Peras et al., (2023), their study revealed a significant positive

correlation between AI engagement and academic success. The research, involving a

diverse sample of students, demonstrated that students who actively utilize AI tools

consistently outperform their peers, underscoring the transformative potential of AI in

modern education. By providing personalized feedback, adaptive learning

experiences, and intelligent tutoring, AI empowers students to take control of their

education and unlock their full potential. These findings highlight the critical role of

AI in revolutionizing learning practices and fostering a more equitable and effective

educational landscape.
 21

Figure 2. Conceptual Framework

Input Process Output

1. What is the 1. Formulating the 1.The majority of the respondent's age
belongs to 20 while the least age belongs
demographic profile of research title. to 18. The majority of the respondent's
sex belongs to female while the least sex
the respondents. belongs to male. The majority of
1.1 Age 2. Preparing self- respondents according to their year level
belongs to 4th Year. The most common
1.2 Sex made questionnaire. AI tools according to respondents is
CHAT GPT while the least used is
1.3 Year Level
3. Validation of the Gemini.
1.4 AI Tools 2.The result on the tables of the
2. How may the self-made independent variable "how may the use
of Artificial intelligence application
Artificial Intelligence questionnaire. among the respondents be assessed in
terms of,” shown that Learning
Application of the 4. Distribution of the efficiency had the highest weighted mean
respondents be while Personalized learning had the
self-made lowest weighted mean.
identified in terms of: 3.Under the dependent variable "how
2.1 Academic questionnaire. may the academic performance of the
respondents be assessed in terms of," the
Achievement 5. Collecting data Reflection has the highest weighted
mean while the Quiz had the lowest
2.2 Personalized gathering. weighted mean.
Learning 4.The researchers concluded that there is
significant effect of using Artificial
2.3 Learning Efficiency 6. Classifying the intelligence application to the academic
2.4 Content Creation data.
performance of Teacher education
students at La Concepcion College.
3. How may the 5.Based on the results of the study,
academic performance 7. Analysis and researchers recommended that students
cultivate a sense of responsibility, utilize
of the selected interpretation of data. artificial intelligence applications
ethically, and maximize the potential of
respondents be assessed AI to boost their creativity and
in terms of: productivity in their studies. For
teachers, the recommendation is to tailor
3.1 Journal artificial intelligence applications to the
3.2 Essay Writing diverse learning styles and preferences of
Teacher Education students. School
3.3 Quiz administrators are encouraged to allocate
resources to ensure that the school's
3.4 Reflection technology tools and platforms are
3.5 Research Writing regularly updated and aligned with the
evolving needs of the Teacher Education
4. Is there an effect of curriculum. Parents should stay informed
artificial intelligence about the artificial intelligence
applications used in the academic
application and curriculum of Teacher Education
academic performance students to understand their benefits and
potential risks, thereby empowering them
of the respondents? to support their children's digital literacy.
Lastly, future researchers are advised to
5. What are the possible conduct longitudinal studies to assess the
recommendations of long-term effects of AI on student
learning and performance, as well as on
this study? the academic and professional outcomes
of Teacher Education students, providing
a deeper understanding of its sustained
impact.

Feedback
 22

CHAPTER III
RESEARCH METHODOLOGY

This chapter presents the methodologies used in this study. This includes

research design, population and sample, research instrument, data collection, and

statistical method.

Research Design

The researchers will be using quantitative and descriptive method to assess the

effect of artificial intelligence on students' academic performance by gathering

information about their profiles and perspectives. A quantitative study is a method of

collecting and analyzing numerical data. It is used to find patterns and averages, make

forecasts, analyze informal connections, and extrapolate results to bigger groups. It is

used to gather information about the current state of phenomena. This method is

collaborated to the self-made survey questionnaire.

The researchers used a Descriptive Research Design in their study, which is

defined by McCombes (2022) as a research strategy that explains the features of the

population or phenomenon researched. It focuses on articulating the "what" of the

research subjects in relation to situational circumstances.

The descriptive method comprises acquiring data that describes the population

or issue under consideration through data reviews, surveys, interviews, or observation.

 23

Population and Sample

This study aims to know the effect of artificial intelligence (AI) application on

the academic performance of the students in the Teacher Education Division at La

Concepcion College (Kaypian Campus, A.Y 2024-2025). There are 219 students

enrolled in Teacher Education Division. 50 randomly selected Teacher Education

students are the ones who participated in this study. Those who are not included in are

excluded in this study.

Purposive sampling is involving researchers deliberately selecting participants

based on specific criteria relevant to the research question, rather than choosing from

entire population. This method is employed when studying a particular group of

individuals with distinctive characteristics or traits interest of interest to the study.

Research Instrument

The researchers prepared a self-made survey questionnaire to be validated and

distributed to the target respondents. The survey questionnaire serves as the primary

tool for data collection. The survey questionnaire divided into two parts. The first and

second part are checklist. The questions on the checklist can be answered by checking

the preferred box. The first part is the students’ demographic profile. Lastly, the self-

made questionnaire without an interviewer's assistance (or bias). Likert’s scale will be

using on the second part to measure the respondent’s attitudes to the extent to which

they agree or disagree with a particular question.

 24

Data Gathering Method

The researchers will dedicate substantial time, effort, and collaboration to

meticulously develop the questionnaire, ensuring its suitability for the targeted

respondents.

1. The researchers will write a request letter.

2. A survey questionnaire will be provided and reviewed by an expert.

3. Once permission is granted, the researchers will explain the study's purpose to

the selected participants and distribute the questionnaire during their classes or

breaks.

4. The researchers will collect the completed forms from the respondents.

5. Following collection, the researchers will arrange, analyze, and interpret the

data utilizing the statistical tools available.

Statistical Treatment Method

The researchers will utilize different statistical tool to analyze and interpret the

gathered data. The researchers will use the following: frequency distribution table,

weighted mean and Chi-square to determine the assessment of the effectiveness of

Artificial Intelligence in the academic performance.

1. Percentage and Frequency Distribution - It indicates the percentage of

observation for each data gathered.

 25

Formula:

1. P= x 100

Where:

P = Percentage

F = Frequency

N = Total no. of respondents.

2. Weighted Mean - used to compute the average response of the respondents.

Formula:

∑ 𝑓𝑥
2. 𝑊𝑥 = 𝑛

Where:

𝑊𝑋 = weighted mean

∑ 𝑓𝑥 = summation of the frequency and weight

n = total number of respondents.

∑ 𝑓𝑥
3. Grand Mean = 𝑛

4. Likert Scale - A survey question that uses a 5- or 7-point scale to analyses and

interpret data that ranges from one extreme attitude to another. The responses on the
 26

criteria used were quantified using the following weights, limits, and verbal

interpretations.

Numerical Value Weighted Mean Verbal Interpretation

5 4.20-5.00 Strongly Agree

4 3.40-4.19 Agree

3 2.60-3.39 Neutral

2 1.80-2.59 Disagree

1 1.00-1.79 Strongly Disagree

5. ANOVA

Used to the significant effect of ai application on the academic performance of

the students of teacher education division.

 27

Formula for ANOVA:

Where:

F = Anova Coefficient

MSB = Mean sum of squares between the groups

MSW = Mean sum of squares within the groups

SST = Mean sum of squares due to error

p = Total number of populations

N = The total number of samples in a population

SSW = Sum of squares within the groups

SSB = Sum of squares between the groups

SSE = Sum of squares due to error

S = Standard deviation of the samples

N = Total number of observations

 28

Analysis of Data

Analysis Formula Statistical Tool

1. What is the demographic profile of the
respondents.
1.1 Age Frequency
Percentage
1.2 Sex P= x 100
1.3 Year Level
1.4 AI Tools
2. How may the Artificial Intelligence ∑ 𝑓𝑥 Weighted Mean
𝑊𝑥 =
𝑛 Grand Mean
Application of the respondents be identified
in terms of: Verbal
2.1 Academic Achievement ∑ 𝑓𝑥
Interpretation
2.2 Personalized Learning 𝑛
2.3 Learning efficiency Ranking
2.4 Content Creation
3. How may the academic performance of ∑ 𝑓𝑥 Weighted Mean
𝑊𝑥 = 𝑛
the selected respondents be assessed in
terms of: Grand Mean
3.1 Journal ∑ 𝑓𝑥
Verbal
3.2 Essay Writing 𝑛
3.3 Quiz Interpretation
3.4 Reflection
3.5 Research Ranking
4. Is there an effect of artificial intelligence ANOVA
application and academic performance of
the respondents?

5. What are the possible recommendations

of this study?
 29

CHAPTER IV
PRESENTATION, ANALYSIS, AND INTERPRETATION OF DATA

This chapter presented the analysis and interpretation of data gathered in

tabular form from the instrumental used. The data gathered are presented in the table

and figure.

1. Demographic Profile of the Respondents

TABLE 1.1 Frequency Distribution of Respondents According to Age

Age Frequency Percentage Ranking
18 5 10% 6
19 6 12% 4.5
20 13 26% 1
21 9 18% 3
22 6 12% 4.5
23 and above 11 22% 2
Total 50 100
Table 1.1 The frequency distribution of respondents according to age, reveals

that the majority of respondents are 20 years old, accounting for 26% of the total

(ranked 1st). This is followed by respondents aged 23 and above, comprising 22%

(ranked 2nd), and 21 years old, at 18% (ranked 3rd). Respondents aged 19 and 22 both

contribute 12% each, sharing the 4.5 ranking. The least represented age group is 18

years old, making up only 10% (ranked 6th). Overall, the sample includes 50

respondents, covering a range of ages from 18 to 23 and above.

Understanding the age distribution in a sample is essential for gaining insights

into generational behaviors, preferences, and trends, which play a significant role in

various fields. Creswell (2019) explains that analyzing age demographics helps
 30

researchers and professionals better understand how different age groups respond to

certain conditions, make decisions, and interact with their environment.

TABLE 1.2 Frequency Distribution of Respondents According to Sex

Sex Frequency Percentage Ranking

Male 13 26% 2
Female 37 74% 1
Total 50

Table 1.2 shows that the majority of respondents are female, comprising 74%

of the total and ranked 1st. Males make up 26%, placing them in 2nd rank. Overall,

the total number of respondents is 50, showing that females are more represented in

this group compared to males.

Sex is a biological classification that differentiates individuals based on

physical and physiological characteristics, such as reproductive anatomy,

chromosomes, and hormone levels (WHO, 2022). In research studies, including sex as

a variable is essential for ensuring comprehensive and applicable findings across

different populations. (Clayton and Tannenbaum, 2016)

TABLE 1.3 Frequency Distribution of Respondents According to Year Level

Year Level Frequency Percentage Ranking

1st Year 14 28% 2
2nd Year 7 14% 4
rd
3 Year 13 26% 3
4th Year 16 32% 1
Total 50 100
Table 1.3 shows the frequency distribution of respondents according to their

year level. The majority of respondents are 4th-year students, comprising 16

 31

individuals or 32% of the sample, ranking first. This is followed by 1st-year students,

with 14 respondents (28%), ranked second. Third-year students make up 26% of the

respondents, with 13 individuals, ranked third. Lastly, 2nd-year students represent the

smallest group, with 7 respondents (14%), ranking fourth. This distribution indicates

that most respondents are in their final year, with fewer participants from the earlier

year levels.

Analyzing the frequency distribution of students by year level offers essential

insights for higher education institutions aiming to enhance student support, optimize

resource distribution, and improve overall academic outcomes. (COE, 2017)

TABLE 1.4 Frequency Distribution of Respondents According to AI Tools

AI Tools Frequency Percentage Ranking
CHAT GPT 18 36% 1
CiCi 7 14% 3.5
Gemini 3 6% 6
Grammarly 7 14% 3.5
Quill Bot 10 20% 2
Others 5 10% 5
Total 50
Table 1.4 shows the frequency distribution of respondents according to their

use of AI tools. The data shows that ChatGPT is the most widely used AI tool among

respondents, with 18 users (36%), ranking 1st. Quill Bot follows in 2nd place with 10

users (20%). Both CiCi and Grammarly share 3rd place, each with 7 users (14%).

Tools classified as "others" rank 5th, representing 5 users (10%), while Gemini is the

least used, with 3 users (6%), ranking 6th. This indicates that ChatGPT is the preferred

tool, while other AI tools are used less frequently.

 32

Artificial Intelligence (AI) tools are software applications that utilize AI

algorithms to perform tasks traditionally requiring human intelligence, such as data

analysis, pattern recognition, and decision-making. (Synthesia, 2021)

2. Effect of Artificial Intelligence Application of the respondents.

TABLE 2.1 Mean Interpretation of the Effect of AI Applications on Academic

Achievement.

2.1 Academic 5 4 3 2 1 Weighted Verbal Ranking

Achievement Mean Interpretation
1. AI tools have 8 22 18 1 1 3.70 Agree 4
helped me improve
my grades in
school.
2. AI applications 13 25 10 2 0 3.98 Agree 1
have helped me
grasp challenging
topics in my
courses.
3. I use AI tools to 14 19 11 6 0 3.82 Agree 2
enhance my study
techniques and
strategies.
4. AI tools have 11 19 18 1 1 3.76 Agree 3
contributed to
improving my
grades in certain
subjects.
5. I rely on AI 7 9 13 12 9 2.86 Neutral 5
tools to assist with
test preparation
and academic
success.
Total Weighted 18.12
Mean
Grand Mean 3.62 Agree
Legend: 1.0 - 1.79 = Strongly Disagree, 1.80 - 2.59 = Disagree, 2.60 - 3.39 = Neutral,
Agree, 3.40 - 4.19 = Agree, 4.20 - 5.0= Strongly Agree
 33

Table 2.1 presents data gathered on respondents' perceptions of AI's effect on

academic achievement. The highest weighted mean, 3.98, shows strong agreement that

AI aids in understanding challenging topics. AI’s role in improving grades in specific

subjects and overall scores also received positive responses, with weighted means of

3.76 and 3.70. Using AI for study strategies scored 3.63, while reliance on AI for test

preparation received a neutral response at 2.86. Overall, the average score of 3.62

suggests that respondents generally agree AI positively influences academic

achievement.

AI-powered learning platforms have been shown to significantly enhance

academic achievement by providing personalized learning experiences, optimizing

curricula, and automating routine tasks. These platforms utilize AI algorithms to track

student progress, analyze performance, and recommend tailored learning materials,

which helps in improving student learning outcomes. (Melo, 2024).

 34

TABLE 2.2 Mean Interpretation of the Effect of AI Applications on Personalized

Learning
2.2 Personalized 5 4 3 2 1 Weighted Verbal Ranking
Learning Mean Interpretation
1. AI tools provide 7 17 21 2 3 3.46 Agree 3
a learning
experience that is
tailored to my
personal needs.
2. I feel that AI 9 19 15 4 3 3.54 Agree 2
applications adapt
well to my
individual learning
style.
3. AI tools help me 6 16 19 5 4 3.30 Neutral 5
learn at a pace that
suits me.
4. AI tools suggest 8 23 15 3 1 3.68 Agree 1
resources that are
relevant to my
academic goals.
5. I find that AI 7 19 16 5 3 3.44 Agree 4
tools help me focus
on areas where I
need improvement.
Total Weighted 17.42
Mean
Grand Mean 3.48 Agree
Legend: 1.0 - 1.79 = Strongly Disagree, 1.80 - 2.59 = Disagree, 2.60 - 3.39 = Neutral,
3.40 - 4.19 = Agree, 4.20 - 5.0= Strongly Agree
Table 2.2 presents respondents' perceptions of the effect of AI applications on

personalized learning. The highest-ranked aspect was that AI tools suggest resources

relevant to academic goals, with a weighted mean of 3.68, securing the 1st rank. The

second-ranked aspect highlighted that AI applications adapt well to individual learning

 35

styles (3.54). The experience of AI providing tailored learning ranked 3rd (3.46),

followed by the ability to focus on areas needing improvement (3.44) in 4th place.

Finally, learning at a pace that suits individual needs was ranked lowest, at 5th, with a

weighted mean of 3.30, interpreted as "Neutral." AI tools revealed an overall weighted

mean of 3.48, which corresponds to an "Agree" verbal interpretation. Among the five

statements Overall, the results suggest that AI tools are generally perceived as

supportive in providing personalized learning experiences, with particular strengths in

resource recommendations and adaptation to learning styles.

Personalized learning, enhanced by artificial intelligence (AI), has become a

significant focus in educational research. AI-driven solutions offer capabilities such as

automated learner profiling, adaptive content recommendation, real-time assessment,

and the development of intelligent user interfaces, thereby augmenting the

personalization of learning experiences. Additionally, AI-mediated solutions for

designing personalized learning paths have been explored extensively, with a focus on

higher education and lifelong learning. These advancements highlight the

transformative potential of AI in creating tailored educational experiences that cater to

individual needs and preferences. (Bayly-Castaneda, et, al., 2024)

 36

TABLE 2.3 Mean Interpretation of the Effect of AI Applications on Learning

Efficiency

2.3 Learning 5 4 3 2 1 Weighted Verbal Ranking

efficiency Mean Interpretation
1. AI tools help 6 19 16 9 0 3.44 Agree 5
me manage my
study time more
effectively.
2. I complete my 12 22 14 2 0 3.88 Agree 2
assignments
faster with the
help of AI
applications.
3. AI helps me to 11 21 16 2 0 3.82 Agree 3
retain information
more effectively.
4. AI applications 12 25 13 0 0 3.98 Agree 1
help me quickly
access the
information I
need for my
studies.
5. I rely on AI 8 23 11 6 2 3.58 Agree 4
tools to simplify
complex tasks or
assignments.
Total Weighted 18.7
Mean
Grand Mean 3.74 Agree
Legend: 1.0 - 1.79 = Strongly Disagree, 1.80 - 2.59 = Disagree, 2.60 - 3.39 = Neutral,
3.40 - 4.19 = Agree, 4.20 - 5.0= Strongly Agree
Table 2.3 Learning Efficiency summarizes students' perceptions of how AI

tools impact their learning efficiency, with an overall weighted mean of 3.74

("Agree"). The highest-rated statement, "AI applications help me quickly access the

information I need for my studies," scored 3.98, showing strong support for AI's role

in streamlining information retrieval. Following this, "I complete my assignments

 37

faster with the help of AI applications" received a weighted mean of 3.88, and "AI

helps me to retain information more effectively" scored 3.82. Additionally, "I rely on

AI tools to simplify complex tasks or assignments" had a weighted mean of 3.58, and

"AI tools help me manage my study time more effectively" scored 3.44. Overall,

students agree that AI tools significantly contribute to learning efficiency across

various tasks.

Memarian and Doleck (2024) explores the integration of artificial intelligence

(AI) in teaching and learning, highlighting its potential to enhance learning efficiency.

The authors discuss various AI applications, such as personalized learning, intelligent

tutoring systems, and automated assessment tools, which can provide tailored

feedback and support to students. These AI-driven tools help in identifying individual

learning needs, adapting instructional content, and offering real-time assistance,

ultimately improving the overall learning experience and efficiency.

 38

TABLE 2.4 Mean Interpretation of the Effect of AI Applications on Content Creation

2.4 Content 5 4 3 2 1 Weighted Verbal Ranking

Creation Mean Interpretation
1. AI tools assist 7 29 13 1 0 3.84 Agree 2
me in generating
new ideas for my
assignments or
projects.
2. I often use AI 9 24 11 5 1 3.70 Agree 3
applications to
improve the
quality of my
written work.
3. AI has 9 20 17 4 0 3.68 Agree 4
improved the
accuracy and
coherence of my
written work.
4. I find that AI 9 28 12 1 0 3.90 Agree 1
applications make
content creation
less time-
consuming.
5. I feel that AI 6 16 21 5 2 3.38 Agree 5
tools enhance the
originality and
creativity of my
work.
Total Weighted 18.5
Mean
Grand Mean 3.70 Agree
Legend: 1.0 - 1.79 = Strongly Disagree, 1.80 - 2.59 = Disagree, 2.60 - 3.39 = Neutral,
3.40 - 4.19 = Agree, 4.20 - 5.0= Strongly Agree
Table 2.4 Content Creation presents students' views on AI tools in content

creation, with an overall weighted mean of 3.70 ("Agree"). The highest-rated

statement, "AI applications make content creation less time-consuming," scored 3.90

(Rank 1), showing strong agreement on AI's role in efficiency. Next, "AI tools assist
 39

me in generating new ideas for my assignments or projects" received 3.84 (Rank 2).

"I often use AI applications to improve the quality of my written work" scored 3.70

(Rank 3), while "AI has improved the accuracy and coherence of my written work"

had a weighted mean of 3.68 (Rank 4). Lastly, "I feel that AI tools enhance the

originality and creativity of my work" scored 3.38 (Rank 5), indicating a more

moderate view on AI’s impact on creativity. Overall, students agree that AI tools

enhance content creation, particularly in terms of efficiency.

Artificial Intelligence (AI) has significantly impacted content creation,

offering new methodologies and tools that enhance both the creative and analytical

processes. AI technologies such as machine learning algorithms, natural language

processing (NLP), and generative models are not only assisting authors in their

creative endeavors but also generating original works of fiction, poetry, and drama.

These advancements raise fundamental questions about authorship, creativity, and the

role of human intervention in the creative process. (Premkumar, 2024)

 40

3. Assessing the Academic Performance of the respondents

TABLE 3.1 Mean Interpretation of the Academic Performance on Journal

3.1 Journal 5 4 3 2 1 Weighted Verbal Ranking

Mean Interpretation
1. I use AI tools to 6 13 23 7 1 3.32 Neutral 5
help generate
ideas for my
journal entries.
2. AI tools assist 7 18 20 3 2 3.50 Agree 2
me in organizing
my thoughts
clearly when
writing my
journal.
3. My academic 7 12 25 4 2 3.36 Neutral 4
progress is better
documented in
my journals with
the help of AI
tools.
4. I rely on AI 9 18 20 3 0 3.66 Agree 1
tools to ensure my
journal entries are
grammatically
correct and well-
structured.
5. My journal 8 13 23 5 1 3.44 Agree 3
writing has
improved because
of AI feedback.
Total Weighted 17.28
Mean
Grand Mean 3.456 Agree
Legend: 1.0 - 1.79 = Strongly Disagree, 1.80 - 2.59 = Disagree, 2.60 - 3.39 = Neutral,
3.40 - 4.19 = Agree, 4.20 - 5.0= Strongly Agree

Table 3.1 shows the data gathered from the variable “Journal” as can be seen

in the table, statement number 4, The highest weighted mean, 3.66, shows agreement
 41

that AI helps ensure journal entries are grammatically correct and well-structured. AI’s

assistance in organizing thoughts for journaling follows closely with a weighted mean

of 3.5. Feedback from AI as a tool for journal improvement has a mean of 3.44, while

AI’s role in documenting academic progress scored 3.36, indicating a neutral response.

Lastly, using AI to generate ideas for journal entries has the lowest mean at 3.32.

Overall, the average weighted mean of 3.46 suggests that respondents generally agree

AI is beneficial for journaling.

AI tools can assist in this process by providing prompts, analyzing patterns in

entries, and offering insights based on the content. These advancements can help

individuals gain deeper understanding and clarity about their thoughts and emotions,

making journaling a more effective tool for personal growth and mental health

(Nordquist, 2019).
 42

TABLE 3.2 Mean Interpretation of the Academic Performance on Essay Writing

3.2 Essay Writing 5 4 3 2 1 Weighted Verbal Ranking

Mean Interpretation
1. I use AI tools to 11 17 18 4 0 3.70 Agree 4
help structure and
outline my essays
more effectively.
2. AI tools have 11 23 13 2 1 3.82 Agree 1
helped improve
the grammar of
my essays.
3. My essays 10 18 21 1 0 3.74 Agree 3
reflect a strong
understanding of
the topics.
4. I am able to 11 17 21 1 0 3.76 Agree 2
present my
arguments clearly
and persuasively
in essays with the
help of AI.
5. My essay 9 19 16 4 2 3.58 Agree 5
writing has
improved due to
feedback from AI
applications.
Total Weighted 18.6
Mean
Grand Mean 3.72 Agree
Legend: 1.0 - 1.79 = Strongly Disagree, 1.80 - 2.59 = Disagree, 2.60 - 3.39 = Neutral,
3.40 - 4.19 = Agree, 4.20 - 5.0= Strongly Agree

Table 3.2 shows the data gathered from the variable “Essay Writing” as can be

seen in the table, statement number 2, “AI tools have helped improve the grammar of

my essays." has the highest weighted mean of 3.82 which can be interpreted as Agree.

Followed by statement number 4, “I am able to present my arguments clearly and

persuasively in essays with the help of AI.,” with a weighted mean of 3.76 which is
 43

interpreted as Agree. Next in the rank is statement number 3., My essays reflect a

strong understanding of the topics," with a weighted mean of 3.74 which can be

interpreted as Agree. The statement number 1, “. I write well-structured and coherent

essays with the help of AI.” has the weighted mean of 3.7 which can be interpreted as

Agree. Lastly, statement number 5, " My essay writing has improved due to feedback

from AI applications," has the lowest weighted mean of 3.58 that is interpreted as

Agree. The total weighted mean of the variable Performance Task was 3.72 that can

be interpret as agree.

Kim, et al. (2025) explored students' perspectives on using AI-assisted writing

systems and found that students perceived AI as a valuable tool for enhancing their

writing performance and process.

 44

TABLE 3.3 Mean Interpretation of the Academic Performance on Quiz

3.3 Quiz 5 4 3 2 1 Weighted Verbal Ranking

Mean Interpretation
1. I perform well 5 11 17 14 3 3.02 Neutral 5
on quizzes after
using AI study
aids.
2. AI tools help 6 14 17 11 2 3.22 Neutral 4
me prepare
effectively for
quizzes.
3. I find that AI- 8 16 21 5 0 3.54 Agree 1
generated quizzes
improve my
understanding of
the course content.
4. I use AI 7 19 14 10 0 3.46 Agree 2
resources to
practice and
enhance my quiz-
taking skills.
5. I regularly 7 10 28 3 2 3.34 Neutral 3
achieve high
scores on quizzes.
Total weighted 16.58
Mean
Grand Mean 3.32 Neutral
Legend: 1.0 - 1.79 = Strongly Disagree, 1.80 - 2.59 = Disagree, 2.60 - 3.39 = Neutral,
3.40 - 4.19 = Agree, 4.20 - 5.0= Strongly Agree
Table 3.3 shows the data gathered from the variable “Quiz” as can be seen in

the table, statement number 3, “I find that AI-generated quizzes improve my

understanding of the course content." has the highest weighted mean of 3.54 which

can be interpreted as Agree. Followed by statement number 4, “I use AI resources to

practice and enhance my quiz-taking skills.,” with a weighted mean of 3.46 which is

interpreted as Agree. Next in the rank is statement number 5., I regularly achieve high
 45

scores on quizzes.," with a weighted mean of 3.34 which can be interpreted as Neutral.

The statement number 2, “AI tools help me prepare effectively for quizzes.” has the

weighted mean of 3.22 which can be interpreted as Neutral. Lastly, statement number

1, " I perform well on quizzes after using AI study aids.," has the lowest weighted

mean of 3.02 that is interpreted as Neutral. The total weighted mean of the variable

Performance Task was 3.32 that can interpret as Neutral.

Artificial Intelligence (AI) can enhance various forms of quizzes, including

multiple-choice, true/false, short answer, and oral questioning. AI-powered quizzes are

used by educators to gauge student comprehension, identify areas where students

might need additional support, and encourage active recall of information. Unlike

more formal examinations, AI-driven quizzes are typically shorter, less weighted, and

intended to provide formative feedback to both the learner and the instructor. AI can

analyze quiz results in real-time, offering personalized feedback and recommendations

for improvement. (Labadze, et al., 2023)

 46

TABLE 3.4 Mean Interpretation of the Academic Performance on Reflection

3.4 Reflection 5 4 3 2 1 Weighted Verbal Ranking

Mean Interpretation
1. I use AI tools to 10 14 18 6 2 3.48 Agree 5
help generate
thoughtful
reflections on my
learning
experiences.
2. AI applications 9 17 17 6 1 3.54 Agree 4
assist me in
organizing my
thoughts and
structuring my
reflection paper.
3. AI-driven 9 18 19 3 1 3.62 Agree 3
suggestions
improve the
clarity and
coherence of my
writing in the
reflection paper.
4. Using AI for 9 18 20 3 0 3.66 Agree 2
grammar and style
checks enhances
the overall quality
of my reflection
paper.
5. AI applications 10 18 19 2 1 3.68 Agree 1
assist me in
connecting
personal
experiences with
academic concepts
in my reflections.
Total weighted 17.98
Mean
Grand Mean 3.60 Agree
Legend: 1.0 - 1.79 = Strongly Disagree, 1.80 - 2.59 = Disagree, 2.60 - 3.39 = Neutral,
3.40 - 4.19 = Agree, 4.20 - 5.0= Strongly Agree
 47

Table 3.4 shows the data gathered from the variable “Reflection” as can be

seen in the table, statement number 5, “AI applications assist me in connecting

personal experiences with academic concepts in my reflections." has the highest

weighted mean of 3.68 which can be interpreted as Agree. Followed by statement

number 4, “Using AI for grammar and style checks enhances the overall quality of my

reflection paper.,” with a weighted mean of 3.66 which is interpreted as Agree. Next

in the rank is statement number 3.,” AI-driven suggestions improve the clarity and

coherence of my writing in the reflection paper.," with a weighted mean of 3.62 which

can be interpreted as Agree. The statement number 2, “AI applications assist me in

organizing my thoughts and structuring my reflection paper.” has the weighted mean

of 3.54 which can be interpreted as Agree. Lastly, statement number 1, " I use AI tools

to help generate thoughtful reflections on my learning experiences.," has the lowest

weighted mean of 3.48 that is interpreted as Agree. The total weighted mean of the

variable Performance Task was 4.0 that can interpret as Agree.

Artificial Intelligence (AI) can enhance the reflective process in education by

critically examining experiences, thoughts, and feelings to gain deeper understanding,

identify areas for growth, and inform future actions. AI-powered reflective tools can

analyze what happened, why it happened, and what could be done differently. These

tools encourage learners to connect theory to practice, develop self-awareness, and

enhance their problem-solving and decision-making skills. AI-facilitated reflective

practice is a crucial component of lifelong learning and professional development.

(Machaidze, et al., 2023)

 48

TABLE 3.5 Mean Interpretation of the Academic Performance on Research Writing

3.5 Research 5 4 3 2 1 Weighted Verbal Ranking

Writing Mean Interpretation
1. I use AI tools to 8 19 15 8 0 3.54 Agree 2
gather and
organize research
sources for my
writing projects.
2. The research 10 17 18 4 1 3.62 Agree 1
projects I’ve
completed using
AI tools have
contributed
significantly to
my academic
performance.
3. I feel that AI 8 15 21 5 1 3.48 Agree 3
tools help me
develop stronger
arguments and
analysis in my
research writing.
4. AI tools 9 13 19 8 1 3.42 Agree 4
improve the
quality and
accuracy of
citations in my
research papers.
5. I rely on AI 8 14 17 10 1 3.36 Neutral 5
tools to enhance
the overall quality
and coherence of
my research
writing.
Total weighted 17.42
Mean
Grand Mean 3.48 Agree
Legend: 1.0 - 1.79 = Strongly Disagree, 1.80 - 2.59 = Disagree, 2.60 - 3.39 = Neutral,
3.40 - 4.19 = Agree, 4.20 - 5.0= Strongly Agree
 49

Table 3.5 shows the data gathered from the variable “Research Writing” as can be seen

in the table, statement number 2, “The research projects I’ve completed using AI tools

have contributed significantly to my academic performance." has the highest weighted

mean of 3.62 which can be interpreted as Agree. Followed by statement number 2, “I

use AI tools to gather and organize research sources for my writing projects.,” with a

weighted mean of 3.54 which is interpreted as Agree. Next in the rank is statement

number 3.,” I feel that AI tools help me develop stronger arguments and analysis in

my research writing." with a weighted mean of 3.48 which can be interpreted as Agree.

The statement number 4, “AI tools improve the quality and accuracy of citations in

my research papers.” has the weighted mean of 3.42 which can be interpreted as Agree.

Lastly, statement number 5, " I rely on AI tools to enhance the overall quality and

coherence of my research writing," has the lowest weighted mean of 3.36 that is

interpreted as Neutral. The total weighted mean of the variable Performance Task was

3.48 that can interpret as Agree.

Aghaee and Noroozi (2023). Emphasizes the diverse roles that AI can play in

the academic writing process. It highlights AI's ability to act not only as a writing

assistant, facilitating text generation and refinement, but also as a virtual tutor,

providing guidance and suggestions throughout the writing journey. Furthermore, the

study investigates AI's potential to function as a digital peer, offering collaborative

feedback and enhancing the overall quality of academic work.

 50

OVERALL GRAND MEAN

Grand Mean for the Effect of Artificial Intelligence Application
Effect of Artificial Intelligence Grand Mean Verbal Ranking
Application interpretation
2.1 Academic Achievement 3.62 Agree 3
2.2 Personalized Learning 3.48 Agree 4
2.3 Learning Efficiency 3.74 Agree 1
2.4 Content Creation 3.70 Agree 2
Overall Grand Mean 3.635 Agree
This table shows the effect of AI applications with 3.635 overall grand mean.

Learning Efficiency (3.74) ranked highest, followed by Content Creation (3.70) and

Academic Achievement (3.62), showing AI’s effectiveness in these areas.

Personalized Learning (3.48) ranked lowest but remained in the "Agree" range.

Grand Mean for the Academic Performance

Academic Performance Grand Mean Verbal Ranking
interpretation
3.1 Journal 3.457 Agree 4
3.2 Essay Writing 3.72 Agree 1
3.3 Quiz 3.32 Neutral 5
3.4 Reflection 3.60 Agree 2
3.5 Research Writing 3.48 Agree 3
Overall Grand Mean 3.515 Agree
This table shows the overall grand mean on the academic performance with

3.515. Essay Writing (3.72) ranked highest, followed by Reflection (3.60) and

Research Writing (3.48), showing AI’s effectiveness in written tasks. Journal (3.457)

ranked fourth, while Quiz (3.32) received a neutral rating, suggesting less impact on

test performance.
 51

Table 5.
ANOVA TESTING

SSB = Sum of squares between = 51214

SSW= Sum of squares within = 4,768

Dfb = degree of freedom between = Column (C) -1 = 5 -1= 4

Dfw = Degree of Freedom within = N- C = 45-5 =40

N= r x C= 9x5=50
 52

MSB= mean square between = SSB/ dfb= 12,803.50

MSW = mean square within = SSW/dfw = 119.2

F computed = MSB/MSW = 107.412

Fcritical = 2.606

The table shows the result of Anova testing. Since F computed (107.412) is greater

than Fcritical (2.606), the researchers rejected the null hypothesis. Therefore, the

researchers concluded that there is a significant effect of using Artificial intelligence

application on the academic performance of Teacher education students at La

Concepcion College.
 53

CHAPTER V

SUMMARY, CONCLUSION AND RECOMMENDATION

SUMMARY OF FINDINGS

The study in conducted in La Concepcion College, City of San Jose del Monte

Bulacan, with a total of 50 respondents. The descriptive method and researcher-

designed checklist questionnaire were used in this study to determine the effect of

Using Artificial intelligence Application on the academic performance of Teacher

Education students.

Based on the tabulated data presented in tables in chapter 4 the researchers came up

with the following summary of findings.

1. The age distribution of respondents indicates that the largest group consists of

20-year-olds, ranking first. Following them are individuals aged 23 and above,

placed second, while 21-year-olds hold the third ranking. Respondents aged

19 and 22 share the fourth rank. The smallest age group in the sample consists

of 18-year-olds, ranking sixth. The largest group of respondents according to

the year level consists of 16 fourth-year students, ranking first. Following

them are 14 first-year students, placed second. The third rank is held by 13

third-year students, while the smallest group includes 7 second-year students,

ranking fourth. The findings reveal respondents' usage of AI tools, showing

that ChatGPT is the most commonly used, ranking first with 18 users. Quill

Bot follows in second place with 10 users. CiCi and Grammarly share the third
 54

rank, each having 7 users. AI tools categorized as 'others' are ranked fifth, with

5 users, while Gemini is the least utilized, ranking sixth with 3 users.

2. The independent variable how may the use of Artificial Intelligent Application

among the respondents be assessed in terms of using Academic achievement,

Personalized learning, Learning efficiency and Content creation. The

researchers found out that Learning efficiency had the highest mean of 3.74

which can be interpreted as agree, followed Content creation with a weighted

mean of 3.70 which can be interpreted as agree and Academic achievement

with a weighted mean of 3.57 that can be interpreted as agree. Lastly the

variable Personalized learning had the lowest weighted mean which is 3.48

that can be interpreted as agree.

3. The dependent variable how may the academic performance of the

respondents be assessed in terms of Journal, Essay writing, Quiz, reflection

and Research writing, the researchers found out that Reflection had the highest

weighted mean which is 4.0 that can be interpreted as agree, followed by

Essay writing 3.72 and Research writing 3.48 that can be interpreted as agree,

then Journal 3.46 that can be interpreted as agree, while Quiz had the lowest

weighted mean which is 3.32 that can be interpreted as neutral.

4. Based on the statistical computations using ANOVA, a research instrument,

the researchers rejected HO and accepted HA. Therefore, the researchers

concluded that there is significant effect of using Artificial Intelligent

 55

application on the academic performance of Teacher education students at La

Concepcion College.

CONCLUSION

This research aims to determine "The effect of Artificial intelligence

application on the academic performance of Teacher Education students". According

to the results of the tables presented on Chapter 4, the researcher concludes the

following:

1. The majority of the respondent's age belongs to 20 while the least age

belongs to 18. The majority of the respondent's sex belongs to female while

the least sex belongs to male. The majority of respondents according to their

year level belongs to 4th Year while the least is 2nd year students. The most

common AI tools according to respondents is CHAT GPT while the least

used is Gemini.

2. The result on the tables of the independent variable "how may the use of

Artificial intelligence application among the respondents be assessed in

terms of,” shown that Learning efficiency had the highest weighted mean

while Personalized learning had the lowest weighted mean.

3. Under the dependent variable "how may the academic performance of the

respondents be assessed in terms of," the Reflection has the highest

weighted mean while the Quiz had the lowest weighted mean.
 56

4. The researchers concluded that there is significant effect of using Artificial

intelligence application to the academic performance of Teacher education

students at La Concepcion College.

RECOMMENDATION

Based on the findings and conclusions presented, the following are suggested.

Students

Based on the result of the study, researchers recommended that students cultivate a

sense of responsibility, utilize artificial intelligence applications ethically and

maximize the potential of AI to boost your creativity and productivity in your studies.

Teachers

Teachers should tailor Artificial intelligence application to the diverse learning styles

and preferences of Teacher education students.

School Administrators

The researchers recommended school administrators to allocate resources to ensure

that the school's technology tools and platforms are regularly updated and aligned with

the evolving needs of the Teacher education curriculum.

Parents

Parents should stay informed about the Artificial intelligence application used in the

academic curriculum of Teacher education students. This will help them understand

the benefits and potential risks, empowering them to support their children's digital

literacy.
 57

Future Researchers

Future researchers should conduct longitudinal studies to assess the long-term effects

of AI on student learning and performance on the academic and professional outcomes

of Teacher education students. This can provide a deeper understanding of the

sustained impact.
 58

Bibliography
Aghaee, et al., (2023). Academic Writing in Teaching Research Integrity
https://files.eric.ed.gov/fulltext/EJ1406833.pdf

Aqua, S. (2023). Advancing Personalized Learning Systems with Artificial

Intelligence. Retrieved from: https://web.cs.ucdavis.edu/aqua/2023/AI-in-
learning

Asirit, et al., (2023). The integration of artificial intelligence (AI) in education is

reshaping students’ lives. Polaris Global Journal of Scholarly Research and
Trends, 2(3), 1-50. https://doi.org/10.58429/pgjsrt.v2n3a152

Baidwan, et al., (2024). Artificial intelligence application in education: A systematic

study. Journal of Educational Technology, 21(1),
https://eric.ed.gov/?q=artificial+intelligence+application+help+in+Research+
Writing&id=EJ1433175

Bayly-Castaneda, et al., (2024). Crafting personalized learning paths with AI for

lifelong learning: A systematic literature review. Frontiers in Education.
https://doi.org/10.3389/feduc.2024.1424386

Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy.

Retrieved from https://www.ejournal.edu/research/binns-fairness-machine-
learning-2018

Borbajo, et al., (2023). The potential of AI-powered adaptive learning platforms and
intelligent tutoring systems to provide personalized instruction, address
individual student needs, and enhance learning experiences. Journal of
Educational Technology, 18(4), 123-145. https://doi.org/10.1007/s40593-
023-00260-y

Cambridge University Press. (n.d.). Content creation. In Cambridge Dictionary.

Retrieved August 4, 2024, from
https://dictionary.cambridge.org/dictionary/english/content-creation

Caratiquit, et al., (2023). The influence of ChatGPT as an academic support tool on

student’s academic performance and the role of learning motivation. Journal
of Social, Humanity, and Education, 4(1), 21-33.
https://doi.org/10.35912/jshe.v4i1.1558
 59

Chang, et al., (2024). Effects of design thinking on artificial intelligence learning

and creativity. Educational Studies, 50(5), 763-780.
https://eric.ed.gov/?q=Effect+of+AI+Applications+on+Learning+Efficiency
&id=EJ1437548

COE - Characteristics of Postsecondary Students. (2017).

https://nces.ed.gov/programs/coe/indicator/csb/postsecondary-students?

De Guzman, et al., (2023). AI-enhanced learning environments: Impact on student

engagement and academic performance in Philippine universities. Philippine
Journal of Higher Education, 15(2), 112-130. https://educ.upd.edu.ph/wp-
content/uploads/2024/04/PJES-DOI-04162024.pdf

Duterte, S. (2023). Addressing the challenges and opportunities of AI in education.

Speech presented at the 2023 Global Education and Innovation Summit,
Seoul, South Korea. Retrieved from
https://www.globaleducationinnovationsummit2023.org/speeches/duterte

Frazier, et al., (2024). Exploring the dynamics of artificial intelligence in higher

education. Research Issues in Contemporary Education, 9(2), 57-81.
https://eric.ed.gov/?q=Effect+of+AI+Applications+on+Personalized+Learnin
g.&ff1=dtySince_2020&id=EJ1436060

Garcia, et al., (2023). Investigating the use of AI-driven assessment tools in

Philippine higher education and their impact on academic
performance. Journal of Educational Technology, 18(4), 123-145.
https://doi.org/10.1007/s40593-023-00260-y

González-Calatayud, et al., (2021). Artificial Intelligence for Student Assessment:

A Systematic Review. Applied Sciences, 11(12), 5467.
https://doi.org/10.3390/app11125467

González-Campos, et al., (2024). Artificial Intelligence (AI) is increasingly being

adopted in the education sector in the Philippines, as shown by Instructure’s
“2023 State of Student Success and Engagement in Higher Education”
Retrieved from https://www.instructure.com/research/state-of-higher-
education
 60

Hao, et al., (2021). Artificial intelligence in higher education: A systematic review

of the literature. International Journal of Artificial Intelligence in Education,
31(2), 213-235. https://doi.org/10.1007/s40593-021-00231-7

Hosseini, et al., (2023). The impact of AI writing tools on the content and
organization of students' writing: EFL teachers' perspective. Cogent
Education, 7(1), 2331186.
https://www.researchgate.net/publication/367436030_Using_AI_to_write_sc
holarly_publications

International Society for Technology in Education (ISTE). (n.d.). Personalized

learning. In ISTE.org. Retrieved August 4, 2024, from
https://www.iste.org/standards/iste-standards-for-students/personalized-
learning

Johnson, et al., (2022). Artificial intelligence in higher education: The state of the
field. International Journal of Educational Research, 58(2), 200-215.
https://doi.org/10.1016/j.ijer.2022.02.004

Kim, et al., (2025). Exploring students’ perspectives on Generative AI-assisted

academic writing. Education and Information Technologies, 30, 1265-1300.
https://doi.org/10.1007/s10639-024-12878-7

Kulik, et al., (2019). The effects of computer-based instruction on learning: A meta-

analysis. Retrieved from: https://doi.org/10.1016/0747-5632(91)90030-5

Labadze, et al., (2023). Role of AI chatbots in education: Systematic literature

review. International Journal of Educational Technology in Higher
Education, 20(56). https://doi.org/10.1186/s41239-023-00426-1

Lee, et al., (2021). Investigating AI-based academic support acceptance and its
impact on academic performance. Computers & Education, 67(1), 150-162.
https://doi.org/10.1016/j.compedu.2021.01.005

Liu, et al., (2023). The Impact of AI Writing Tools on Students’ Writing Quality and
Creativity. International Journal of Emerging Technologies in Learning,
18(1), 105-117. https://www.tandfonline.com/doi/full/10.1080/2331
186X.2023.2236469#d1e312
 61

Lopez, A. C., et al., (2023). The role of AI in content creation and learning
efficiency in Philippine higher education. Journal of Philippine Education,
34(2), 45-60. Home | Research in Higher Education (springer.com)

Machaidze, et al., (2023). Role of AI chatbots in education: Systematic literature

review. International Journal of Educational Technology in Higher
Education, 20(56). https://doi.org/10.1186/s41239-023-00426-1

McCombes, S. (2022, May 5). Descriptive Research Design | Definition, Methods &
Examples. Scribbr. Retrieve from https://www.scribbr.co.uk/research-
methods/descriptive-research design/

Mendoza, J. (2023). The impact of Artificial Intelligence (AI) on student

performance at the University of Guayaquil (UG). Journal of Educational
Technology, 15(3), 123-145. https://doi.org/10.1007/s43681-021-00074-z

Melo, N. (2024). AI in higher education: Enhancing academic achievement.

eLearning Industry. Retrieved from eLearning Industry.

Memarian, et al., (2024). Teaching and learning artificial intelligence: Insights from
the literature. Education and Information Technologies, 29(1), 21523-21546.
Retrieved from SpringerLink.

Nordquist, R. (2019). Writing a Personal Journal. Retrieved from

https://www.thoughtco.com/what-is-a-journal-1691206

Panadero, et al., (2023). Artificial intelligence and student learning: A meta-

analysis of empirical studies. Educational Technology Journal, 33(4), 436-
458. https://doi.org/10.1186/s41239-023-00436-z

Peras, et al., (2023). Artificial Intelligence as a Tool in Increasing Academic

Performance. International Journal of Advanced Multidisciplinary Research
Studies, 3(6), 1151-1155. https://doi.org/10.1007/s40593-023-00260-y

Popenici, et al., (2017). Artificial intelligence applications in education. Procedia

Computer Science, 102, 241-245. doi: 10.1016/j.procs.2017.02.091

Premkumar, B. (2024). The Impact of Artificial Intelligence on Literary Creation

and Criticism: Emerging Trends and Implications. International Journal of
Language, Literature and Culture, 4(4), 1-15.
 62

https://aipublications.com/uploads/issue_files/5IJLLC-AUG20241-
TheImpact.pdf

Reyes, et al., (2021). Artificial intelligence in the Philippine educational context:

Circumspection and future inquiries. Journal of Philippine Education, 34(2),
45-60. https://doi.org/10.1016/j.jphil.2021.02.003

Salido, V. (2023). Impact of AI-powered learning tools on student understanding

and academic performance. (Unpublished doctoral dissertation).
https://www.researchgate.net/publication/376260972_Impact_of_AI-
Powered_Learning_Tools_on_Student_Understanding_and_Academic_Perfo
rmance

Smith, et al., (2020). The impact of artificial intelligence on higher education: An

empirical study. Journal of Educational Technology, 45(3), 123-145.
https://doi.org/10.1016/j.jedutech.2020.03.001

Suchánová, Z. (2023). AI tools for pre-service EFL teachers: Exploring applications

and implications. Paper presented at the International Conference on Studies
in Education and Social Studies (ICSES), Antalya, Turkey.
https://eric.ed.gov/?q=Effect+of+AI+Applications+on+Content+Creation&ff
1=eduHigher+Education&id=ED652352

Tapalova, et al., (2022). Artificial intelligence is the driving force of change

focusing on the needs and demands of the student. The research explores
Artificial Intelligence in Education (AIEd) for building personalized learning
systems for students. Journal of Educational Technology, 15(3), 123-145.
https://doi.org/10.1007/s43681-021-00074-z

UNESCO (2023) Global Education. Retrieve from

https://www.unesco.org/en/articles/global education-monitoring-report-
summary 2023-technology-education-tool-whose-terms-hin

Wang, et al., (2023). Effects of higher education institutes' artificial intelligence

capability on students' self-efficacy, creativity and learning performance.
Education and Information Technologies, 28(5), 4919-4939.
https://doi.org/10.1007/s10639-022-11338-4
 63

Wu, et al. (2024). Do AI Chatbots Improve Students Learning Outcomes? Evidence

from a Meta-Analysis. British Journal of Educational Technology, 55(1), 10-
33. https://doi.org/10.1111/bjet.13334

Yann LeCun. (n.d.). Artificial intelligence as an extension of human intelligence.

Retrieved from https://www.equote.edu/thoughts/lecun-ai-human-extension

Zawacki-Richter, et al., (2019). AI in higher education: A systematic review of

research on artificial intelligence applications. Educational Technology
Journal, 27(6), 1-22. https://doi.org/10.1186/s41239-019-0171-0

Zheng, et al., (2023). The effectiveness of artificial intelligence on learning

achievement and learning perception: A meta-analysis. Interactive Learning
Environments, 31(9), 5650-5664.
 64

APPENDICES A

SURVEY QUESTIONNAIRE

Part 1: Demographic profile.

Instruction: Place a check (✓) mark in the box of your preferred answer.

1.1 Age

18 19 20 21 22 23 and above

1.2 Sex

Male Female

1.3 Year Level

1st year 2nd year 3rd year 4th year

1.5 AI Tools

CHAT GPT CiCi Duolingo Grammarly Quill Bot

other

Part 2. Put a check (√) to rate the importance of each factor

Scale Range Value Verbal
Interpretation
5 4.20-5.00 Strongly Agree
4 3.40-4.19 Agree
3 2.60-3.39 Neutral
2 1.80-2.59 Disagree
1 1.00-1.79 Strongly Disagree
 65

2. How may the Artificial Intelligence Application of the respondents be identified

in terms of:

2.1 Academic Achievement 5 4 3 2 1

1. AI tools have helped me improve my grades in

school.
2. AI applications have helped me grasp
challenging topics in my courses.
3. I use AI tools to enhance my study techniques
and strategies.
4. AI tools have contributed to improving my
grades in certain subjects.
5. I rely on AI tools to assist with test preparation
and academic success.

2.2 Personalized Learning 5 4 3 2 1

1. AI tools provide a learning experience that is

tailored to my personal needs.
2. I feel that AI applications adapt well to my
individual learning style.
3. AI tools help me learn at a pace that suits me.
4. AI tools suggest resources that are relevant to
my academic goals.
5. I find that AI tools help me focus on areas
where I need improvement.

2.2 Personalized Learning 5 4 3 2 1

1. AI tools help me manage my study time more

effectively.
 66

2. I complete my assignments faster with the help

of AI applications.
3. AI helps me to retain information more
effectively.
4. AI applications help me quickly access the
information I need for my studies.
5. I rely on AI tools to simplify complex tasks or
assignments.

2.4 Content Creation 5 4 3 2 1

1. AI tools assist me in generating new ideas for
my assignments or projects.
2. I often use AI applications to improve the
quality of my written work.
3. AI has improved the accuracy and coherence of
my written work.
4. I find that AI applications make content
creation less time-consuming.
5. I feel that AI tools enhance the originality and
creativity of my work.

3. How may the academic performance of the selected respondents be assessed in

terms of?

3.1 Journal 5 4 3 2 1
1. I use AI tools to help generate ideas for my
journal entries.
2. AI tools assist me in organizing my thoughts
clearly when writing my journal.
3. My academic progress is better documented in
my journals with the help of AI tools.
4. I rely on AI tools to ensure my journal entries
are grammatically correct and well-structured.
5. My journal writing has improved because of AI
feedback.
 67

3.2 Essay Writing 5 4 3 2 1

1. I use AI tools to help structure and outline my
essays more effectively.
2. AI tools have helped improve the grammar of
my essays.
3. My essays reflect a strong understanding of the
topics.
4. I am able to present my arguments clearly and
persuasively in essays with the help of AI.
5. My essay writing has improved due to feedback
from AI applications.

3.3 Quiz 5 4 3 2 1
1. I perform well on quizzes after using AI study
aids.
2. AI tools help me prepare effectively for
quizzes.
3. I find that AI-generated quizzes improve my
understanding of the course content.
4. I use AI resources to practice and enhance my
quiz-taking skills.
5. I regularly achieve high scores on quizzes.

3.4 Reflection 5 4 3 2 1
1. I use AI tools to help generate thoughtful
reflections on my learning experiences.
2. AI applications assist me in organizing my
thoughts and structuring my reflection paper.
3. AI-driven suggestions improve the clarity and
coherence of my writing in the reflection paper.
4. Using AI for grammar and style checks enhances
the overall quality of my reflection paper.
 68

5. AI applications assist me in connecting personal

experiences with academic concepts in my
reflections.

3.5 Research Writing 5 4 3 2 1

1. I use AI tools to gather and organize research
sources for my writing projects.
2. The research projects I’ve completed using AI
tools have contributed significantly to my
academic performance.
3. I feel that AI tools help me develop stronger
arguments and analysis in my research writing.
4. AI tools improve the quality and accuracy of
citations in my research papers.
5. I rely on AI tools to enhance the overall quality
and coherence of my research writing.
 69

APPENDICES B

LETTER OF REQUEST FOR CONDUCTING SURVEY

August 27, 2024

Ms. Noime F. Magpantay, LPT, PhD

Program Head
La Concepcion College Inc.
City of San Jose Del Monte, Bulacan

Dear Ms. Noime:

Good day! We, the Bachelor of Secondary Education Major in English at La

Concepcion College, are currently undertaking the Research Writing course. We
kindly request your permission to administer a survey to the students of your esteemed
institution as part of our research study titled "An Assessment of the Effect of
Artificial Intelligence Applications on the Academic Performance of Students in the
Teacher Education Division at La Concepcion College." The survey will be
conducted with utmost professionalism and sensitivity to the schedule and routines of
the students. We assure you that all gathered information will be treated with
confidentiality and used solely for academic purposes.
Thank you for considering our request.

Sincerely yours,
Borcena, Princess S.
Aquino, Joemhel

Referred by: Approved by:

Dr. Loreto F. Magpantay, LPT, LL.B. Ms. Noime F. Magpantay, LPT, PhD
Research Adviser Program Head
 70

September 24, 2024

Arlene Marquez, LPT

OIC, Registrar’s Office
La Concepcion College Inc.
City of San Jose Del Monte, Bulacan

Dear Ms. Marquez:

We, the Third-Year students of Teacher Education Division currently enrolled in

Research Writing 1. We are writing to request information for a research project. The
research is titled "An Assessment of the Effect of Artificial Intelligence Applications
on the Academic Performance of Students in the Teacher Education Division at La
Concepcion College." To enhance the accuracy and relevance of our study, we
formally request the official count of English majors from the Teacher Education
Division students for the current academic year.
We understand the importance of such data and assure you that it will be handled with
the utmost confidentiality and used solely for academic research purposes.
Thank you for your time and assistance. We look forward to your prompt response.

Sincerely yours,
Borcena, Princess S.
Aquino, Joemhel A.

Referred by:

Dr. Loreto F. Magpantay, LPT, JD

Research Adviser
 71

LETTER FOR STATISTICIAN

October 1, 2024

Ms. Rowena H. Aragon

Professor of College of Civil Engineering
La Concepcion College
City of San Jose Del Monte Bulacan

Dear Ms. Aragon,

We are conducting research titled, "An Assessment of the Effect of Artificial

Intelligence Applications on the Academic Performance of Students in the Teacher
Education Division at La Concepcion College." as a course requirement in a Degree
Bachelor of Secondary Education Major in English. In connection with this, we would
like to ask your help to analyze the necessary data of our study. We believed that your
knowledge and insights will be valuable and will greatly enrich our work.
Your utmost consideration will be highly appreciated.

Respectfully Yours,

Borcena, Princess S.
Aquino, Joemhel A.

Referred by: Conforme:

Dr. Loreto F. Magpantay, LPT, JD Rowena H. Aragon

Research Adviser Thesis Statistician
 72

APPENDICES C
PHOTO DOCUMENTATION

LA CONCEPCION COLLEGE, KAYPIAN CAMPUS

 73

CURRICULUM VITAE

PRINCESS S. BORCENA
Poblacion, Bigte, Norzagaray Bulacan
0908-108-2028
princessborcena1908@gmail.com

Objectives

To build a long-term profession as a teacher that will offer opportunities for

career growth and utilize my dedication to develop quality education for a child’s
development.

Qualifications

• Verbal and written communication

• Effective discipline and behavior management
• Flexibility in adjusting to diverse learning environments
• Critical thinking skills

Educational Attainment

Tertiary

La Concepcion College 2019-present

Bachelor of Secondary Education Major in English
Kaypian Road Cor., Quirino Highway
City of San Jose Del Monte, Bulacan

Senior Highschool
Academia De San Lorenzo Dema ala Inc. 2017-2019
Tialo, Sto. Cristo
City of San Jose Del Monte Bulacan
Secondary

Minuyan National Highschool 2013-2017

 74

Curvada, Minuyan, Norzagaray Bulacan

Primary

Timoteo Policarpio Memorial Elementary School 2007-2013

Ahunin, Bigte Norzagaray, Bulacan

Personal Data

Date of birth : November 19, 2000

Place of birth : Norzagaray, Bulacan
Age : 23
Height : 5’0
Weight : 60 kgs.
Sex : Female
Status : Single
Nationality : Filipino

Character Reference

Brent Joyce Idel

Teacher
09984592412
STI College

I hereby certify that the above information is true and correct to be the best
of my knowledge and belief.

Princess S. Borcena
Signature
 75

JOEMHEL A. AQUINO
Blk 20, Lot 20 Carissa 5C Brgy. Kaypian,
City of San Jose del Monte Bulacan
09353175962
joemhelaquino08@gmail.com

EDUCATIONAL ATTAINTMENT

Tertiary

Bachelor of Secondary Education, Major in English 2020- Present

La Concepcion College Inc.
City of San Jose Del Monte

Senior High School

General Academic Strand (GAS) 2018-2020

Malinao, National Highschool
Balza, Malinao Albay

Junior High School

Malinao, National Highschool 2014-2018

Balza, Malinao Albay

Primary

Kaypian Elementary School 2008-2014

Kaypian Road, Brgy. Kaypian, SJDM Bulacan

WORK EXPERIENCE

Trec Pacific Corp.

Customer Service Representative / Contracts Administrator
February 2023 – present

3s Offshoring and Outsourcing INC.

Team Lead
August 2022 – December 2022
 76

3s Offshoring and Outsourcing INC.

Customer Service Representative
July 2021 – August 2022

Ma. Fe A. Eusebio
Part-Time Tutor
June 2020 – August 2021

CERTIFICATE

EF SET Certificate, C1 Advanced

SKILLS

Leadership Experience
Good oral and written communication skills
Organized, flexible and has good time management
Problem-solver
Provided homework assistance using specific curriculum and materials.
Collaborated with students to complete homework assignments, identify lagging
skills, and correct, weaknesses.

CHARACTER REFERENCES

Ma. Jovelyn Tabor

09054889976
Campaign Specialist / Customer Service Representative

Linlyn Adamas
0926162707
Sales Clerk

I hereby certify that all information stated above is true and correct through the best
of my knowledge and belief.

JOEMHEL A. AQUINO
Signature