The Impact of AI-Powered Educational Tool Usage on Learning

Engagement of Grade 12 HUMSS Students

A Quantitative Research Study Presented to the

SHS Department of Holy Child College of Davao

Green Meadows Campus, Davao City

In Partial Fulfillment of the

Requirements for

Practical Research 2

Aranjuez, Aliyah I.
Balbuena, Jeremy Matthew E.
Belarmino, Ashley M.
Lapira, Kiara Johannah P.
Luy, Aira Lindsay D.
Pandac, Sean Michael Z.
Rosas, Elaiza Faith M.
Tormis, Jhea Mae G.
Valdez, Kiesha Kaye O.
Virtucio, Jef Priel O.

December 2024
 TABLE OF CONTENTS

PAGE

TITLE PAGE​ 1

TABLE OF CONTENTS​

CHAPTER 1​

INTRODUCTION

Background of the Study​ 3

Statement of the Problem​ 5

Review of Related Literature​ 6

Theoretical Framework​ 14

Conceptual Framework​ 14

CHAPTER 2​

METHODOLOGY​ 15

Research Design​ 16

Research Locale​ 16

Research Respondent​ 17

Research Instrument​ 18

Data Gathering Procedure​ 21

Statistical Tools​ 21

Ethical Considerations​ 22

REFERENCES​ 23

APPENDICES
 CHAPTER 1

INTRODUCTION

Background of the Study

Artificial intelligence refers to the ability of technology, particularly

computer systems, to simulate human intelligence processes. AI can

outperform humans in a variety of tasks in terms of accuracy and efficiency

(Laskowski et al., 2022). According to Huang et al. (2023), AI in education

reveals that it can affect learning engagement. There are a number of

obstacles and possible negative effects associated with the use of AI in

education. Relying too much on AI in education can cause unexpected

problems. For instance, students may become too dependent on AI tutoring

systems to finish assignments or answer difficulties, which would lower their

motivation and capacity for critical and independent thinking (Quan, 2024).

Furthermore, according to Kaledio et al. (2024), the integration of AI in

education presents challenges that require attention. Additionally, there is a

potential risk of over-reliance on AI technologies, leading to a passive learning

experience for students. Balancing the use of AI with human instruction and

guidance is crucial to maintaining meaningful interactions and promoting

deeper understanding.

In India, according to Basha (2024), relying upon artificial intelligence (AI)

technology in studies by students has some drawbacks too; while consistently

dependent on AI tools by the students, it fails to improve the basic

foundational skills, critical thinking and make them inactive in academic

performance and also potentially affect the problem-solving skills in real life. In
Indonesia, according to Chan and Hu (2023), they may eventually become

overly dependent on AI's capabilities, which could hinder their intellectual,

skill, and growth development. According to Warschauer et al. (2023),

over-reliance on generative AI technologies may threaten students' earnest

attempts to advance their writing abilities. Moreover, the use of AI tools may

lead to a loss of academic integrity.

A study conducted in Palawan by Junio and Bandala (2023), examines

the integration of Artificial Intelligence (AI). Focusing on the perceptions and

experiences of second language (L2) learners in academic writing, the study

reveals significant concerns about the potential adverse effects of AI.

Participants highlighted issues such as the suppression of critical and

analytical thinking skills, the risk of reinforcing biases or errors, increased

susceptibility to plagiarism, diminished depth and originality in written work,

unintended alterations in meaning, and an overreliance on technology. These

findings underscore the pressing need to address the negative implications of

AI in educational contexts to ensure balanced and effective usage.

A study conducted at the University of Mindanao in Davao by Obenza et

al. (2023) explored university students' perceptions and usage of generative

AI technology in higher education. The study also raises concerns about

potential negative impacts. The increasing reliance on generative AI

technologies poses risks to students' academic development, such as

reduced critical thinking, diminished problem-solving abilities, and a tendency

toward passive learning. These drawbacks underscore the need for cautious
integration of AI in educational practices to ensure it complements rather than

compromises essential cognitive and intellectual skills.

​ This study delves into the potential negative effects of AI on students'

learning engagement, with a particular focus on its influence on critical

thinking and problem-solving abilities. While AI has the capacity to enhance

learning experiences and boost motivation, its overuse raises valid concerns

about diminishing students' capacity for independent and analytical thinking.

The findings will emphasize the need for a balanced approach, advocating for

AI to be utilized as a supplementary tool rather than a primary dependency.

By integrating AI thoughtfully alongside traditional teaching methods,

educators can safeguard essential cognitive skills, foster holistic development,

and ensure a well-rounded educational experience. Moreover, there is also a

noticeable lack of local and national studies examining how AI usage impacts

students' learning engagement, underscoring the need for more

context-specific research in this area.

Statement of the Problem

This study aims to investigate the impact of AI-powered educational tool

usage on the academic engagement of the students at Holy Child College of

Davao. Therefore, this sought to answer the following questions:

1.​ What is the level of AI-powered educational tool usage among Grade

12 HUMSS students of HCCD?

1.1 Effectiveness of AI Tools

1.2 Quality of education with Artificial intelligence

 1.3 Artificial intelligence Integration

2.​ What is the level of learning engagement among Grade 12-HUMSS

students in terms of:

2.1. Cognitive Engagement

2.2 Communication skills

3.​ Is there a significant relationship between AI-powered educational tool

usage and learning engagement among Grade 12 HUMSS students of

HCCD?

Review of Related Literature

AI-Powered Tools Usage

The incorporation of AI in education represents a continuous path of

innovation and experimentation, motivated by the desire to improve

educational experiences and outcomes through intelligent technologies. As AI

evolves, its integration into education offers the potential to create more

personalized, adaptive, and inclusive learning environments (Farahani &

Ghasemi, 2024). The integration and adoption of AI in education have

primarily aimed at enhancing learners' experiences while also significantly

influencing various other aspects of the educational process (Chen et al.

2020). A key significance of AI in education lies in its ability to support and

promote personalized teaching and learning (Huang et al. 2021). AI has

transformed both teaching methods and learning processes, enabling the

creation of personalized learning plans tailored to students' individual needs

and learning progress (Dishon, 2017).

Effectiveness of AI Tools

In recent years, the integration of artificial intelligence (AI) into

education has transformed how students engage with learning materials,

fostering personalized and efficient learning experiences. This review

explores existing studies on the effectiveness of AI in education. The potential

benefits of AI in education are substantial. Personalized learning, one of the

most notable advantages of AI, enables students to learn at their own pace

and in ways that align with their unique learning styles, ultimately leading to

improved academic outcomes (Shrivastava et al., 2023). Similarly, Haseski

(2019) highlighted that the use of AI makes learning more individualized,

enhances creativity, allows students to discover their talents, provides

effective learning experiences, and reduces teachers’ workloads. AI's

adaptability and responsiveness also play a crucial role in education.

Buckingham Shum (2024) emphasized that AI can deliver real-time feedback

to students and adjust to their learning progress, making the educational

process more dynamic and effective. Furthermore, virtual tutors and adaptive

learning systems present new opportunities to improve teaching and learning.

These systems can modify teaching strategies based on each student’s

needs, making the learning process more inclusive and personalized (Lim et

al., 2023; Dai et al., 2023). The impact of AI extends beyond academic

outcomes, influencing students’ self-confidence and overall academic

performance. AI-powered tools, such as virtual tutors and learning analytics

platforms, enable students to receive personalized and timely feedback,

helping them understand subjects better and build confidence in their

academic abilities (Hamid et al., 2023). Confidence, as noted by Han et al.

(2023), is linked to higher motivation, active engagement in academic

activities, and a willingness to face new challenges. By fostering this sense of

belief in their abilities, AI can empower students to take initiative and embrace

lifelong learning. In conclusion, the integration of AI into education offers

numerous benefits, from personalized learning experiences to fostering

creativity and improving self-confidence. By adapting to students' needs and

providing real-time feedback, AI has the potential to transform education,

making it more effective and inclusive. While challenges remain, the growing

role of AI in education signals a promising future for teaching and learning

processes.

Quality of education with artificial intelligence

Students in a single classroom can be very different, making it hard for

teachers to give each one enough attention, especially in schools or countries

with limited budgets and not enough staff. AI helps solve this problem by

offering personalized tutoring to meet each student's needs (Ahmad et al.,

2022). AI tools allow teachers to help students improve and prepare for the

digital future. Personalized learning platforms and smart tutoring systems

make learning more interesting and help students achieve their goals (Fošner,

2024). AI is being used in education for things like creating curriculum,

developing content, and teaching with technologies such as virtual reality,

online platforms, robotics, video calls, audio-visual materials, and 3D

technology. These tools help students learn more effectively. Teachers can

work more efficiently, and students enjoy a more personalized and enriched

learning experience (Chen et al., 2020). Right now, AI is used in education in

many ways, such as chatbots that offer round-the-clock support to students

and personalized learning systems that adjust to each student's individual

needs (Perez, 2023). The purpose of AI in Education (AIEd) is to enhance

education, not just promote AI. Its success should be measured by how it

improves learning outcomes. For example, using AI to reduce teachers'

workload is only valuable if it allows teachers to spend more time helping

students learn, resulting in better educational results (Chaudhry & Kazim,

2021). AI has the power to greatly change how we learn and teach. To fully

use its potential in education, teachers, policymakers, tech experts, and

researchers need to work together, come up with new ideas, and address

ethical and teaching challenges. It's important to embrace AI's benefits while

being careful about its risks, ensuring it helps make education fairer, more

accessible, and more effective (Ifenthaler et al., 2024).

Artificial intelligence integration

In recent years, the integration of artificial intelligence (AI) into

education has significantly reshaped how students engage with learning

materials, offering new opportunities for personalization, accessibility, and

proactive educational support. By leveraging advanced technologies, AI

fosters individualized learning experiences, ensures equitable access to

resources for all students, and enables data-driven interventions that enhance

academic outcomes. This review explores existing studies on the

effectiveness of AI in education, highlighting its transformative role in

personalized learning, accessibility enhancements, and predictive analytics.

The potential benefits of AI in education are substantial. Personalized

learning, one of the most notable advantages, enables students to learn at

their own pace and in ways that align with their unique learning styles,

ultimately leading to improved academic outcomes (Shrivastava et al., 2023).

Similarly, Haseski (2019) emphasized that AI individualizes learning,

enhances creativity, allows students to discover their talents, provides

effective learning experiences, and reduces teachers’ workloads. AI also

plays a crucial role in improving accessibility for students with disabilities. Rob

Gibson (2024) highlights how AI-powered tools, such as automatic speech

recognition and real-time captioning, are essential for students with hearing

impairments, ensuring they can fully engage in live discussions and video

content. Similarly, AI-driven virtual assistants and text-to-speech systems

empower visually impaired learners by helping them navigate digital platforms

and access educational materials. Gibson further underscores the importance

of inclusivity in the development of these technologies, noting that while AI

offers customized resources like simplified text, audio formats, and visual

aids, the underrepresentation of assistive technology users in product

development remains a challenge. Muhammad Umar Riaz Abbasi (2024)

extends this view by emphasizing how AI addresses diverse needs, including

sensory, motor, and cognitive disabilities, enabling teachers to better engage

with students who require tailored support.

The use of predictive analytics powered by AI enhances proactive

educational support systems. Deeptanshu Tiwari (2024) describes how

predictive analytics, driven by machine learning algorithms, identifies early

warning signs of academic struggles or disengagement by analyzing student

data, behaviors, and historical trends. This allows educators to intervene with

targeted support to improve outcomes. Similarly, Kate Tattersfield (2024)

highlights how predictive analytics provides educators with a deeper

understanding of barriers that hinder student progress. These insights not

only diagnose challenges but also help enhance learning experiences,

increase engagement, and promote success. In conclusion, the integration of

AI into education demonstrates remarkable potential in personalized learning,

accessibility enhancements, and predictive analytics. These advancements

allow for tailored learning, equitable access to educational resources, and

data-informed interventions that support diverse learner needs. To unlock AI’s

full potential, it is crucial to prioritize inclusivity, collaboration, and the

representation of all stakeholders, ensuring the benefits of these technologies

extend to every student.

Learning Engagement

Learning engagement is strongly linked to key educational outcomes,

such as academic success and overall student satisfaction (Halverson et al.,

2019). It has emerged as a significant area of interest in educational research,

particularly as educators strive to enhance student performance through more

engaging and interactive teaching strategies. Engagement is typically

understood through three primary dimensions: behavioral, cognitive, and

emotional. These dimensions are essential for promoting deep learning and

ensuring that students remain actively involved in their educational

experiences (Johar et al., 2023).

Cognitive Engagement

The concept of learning engagement has generated a lot of interest in the

field of educational intelligence because of its impact on students'

engagement, especially cognitive engagement. According to Tran (2024),

cognitive engagement is the state of mind where students are inspired to

understand and apply new information. Also, it involves students exerting an

effort to accept challenges and go above and beyond their basic obligations.

To put it another way, cognition is the mental process of learning and

comprehending through experience, thought, and the senses. It includes

every phase of cognitive processes (Subedi, 2022). Students can be inspired,

interested, and involved in their studies in the classroom when they are

actively participating. According to Torto (2020), Current research suggests

that learning engagement can be understood through three distinct but

interconnected dimensions: cognitive, emotional, and behavioral engagement.

While cognitive engagement shows students' commitment to study and desire

to take on problems, behavioral engagement is demonstrated by students'

obedience to instructions. Students' good thoughts regarding a subject and

sense of community are related to emotional engagement. In order to promote

deeper learning, these aspects are interrelated and essential. (McDowell,

2024). According to Carballo (2023), the degree and quality of support,

encouragement, group projects, and interpersonal interactions that students

receive over the course of their academic careers—from the time of their first

interest to the eventual completion of their degrees—is referred to as learning

engagement. However, there are many potential causes of learning

disengagement, including a lack of interest in the material, lack of personal

relevance, lack of motivation, poor classroom management, and lack of

support from teachers and peers. The degree and quality of support,

encouragement, group projects, and interpersonal interactions that students

receive over the course of their academic careers—from the time of their first

interest to the eventual completion of their degrees—is referred to as learning

engagement (Promethean, 2023).

Communication Skills

Communication skills is an individual’s ability to interact and respond

verbally or nonverbally, written, and digital communication serves as a critical

input to the learning process. In line with that, it marks an open field for one’s

communication skills to subsequently fill the gap between each other’s

responses. According to (Juan, 2023), communication skills construct an

opportunity for a learning environment to succeed in a form of conversing

through a series of effective interactions, such as understanding each other

through communication and expression. Thus, the importance of a

well-diverse communication skill traverses jointly through understanding the

efficiency of an effective environment. Furthermore, great communication is a

very important talent that can help us in all aspects of our lives. One of the

most important life skills that students should acquire is excellent

communication (McMillan, 2021). According to Marchiori and Mclean (2022),

students should be able to communicate effectively since it is a necessary

ability for many other learning processes that occur throughout active

learning. Communication skills play a critical role in enhancing learning

engagement. Evidence shows that students who engage actively with their

peers, course content, faculty members, and campus activities through clear,

open, and effective communication are more likely to demonstrate improved

learning engagement. According to Mills, O. (2024), we are drawn to one

another and communicate with each other in a variety of capacities. Hence,

focusing on improving the ability to communicate creates an opening path for

effective learning, it solely revamp cognitive skills and behavior in the surface

of the learning environment. Consequently, the learning environment

significantly affects the development of communication skills and overall

learning outcomes (Dörnyei and Mui, 2019).

Theoretical Framework

David Krish's Cognitive Offloading Theory (Storm, 2016) warns of the

potential downsides of relying on external tools or technology to perform

cognitive tasks. AI tools, by providing quick answers and solutions, may cause

students to depend on external aids rather than developing their own

problem-solving and critical thinking skills. As students offload cognitive tasks

to AI, they risk undermining their ability to think independently and engage in

deeper learning processes, which are essential for long-term retention and

understanding (Taylor & Francis, 2016).

By integrating this theory, this study explores how AI-powered tools can

also pose the risk of cognitive complacency. AI technologies offer valuable

scaffolding for students, but overreliance on them may weaken critical

cognitive abilities such as independent reasoning and critical thinking. This

research aims to examine the balance between utilizing AI as an educational

aid and ensuring that students continue to develop essential cognitive skills

through independent learning.

Conceptual Framework

This part shows the conceptual framework containing the basic

components of the study, showcasing the significant relationship between

these two variables. Figure 1 distinguished the independent variable, the

impact of AI-powered tool usage, including the effectiveness of AI tools,

quality of education, and artificial intelligence integration, and also the

dependent variable, cognitive engagement and communication skills. This

stage is a crucial part of the process of the design, and the development of

the alternative material will be based.

INDEPENDENT VARIABLE DEPENDENT VARIABLE

Figure 1. The Conceptual Framework Showing the Variables of the Study

 CHAPTER 2

METHODOLOGY

This chapter contains responses to the methodological procedures and

strategies that were employed to carry out the study. This comprises thorough

information on the equipment used for the study, background on the target

location and participants, and explanations of the study's research

methodology.

Research Design

This research will use the quantitative research design, a correlational

approach. Quantitative research design focuses on assessing objective

concepts by exploring the relationships between variables. This design will

enable them to collect relevant data information that works effectively by using

surveys and analyzing existing data that measures existing levels of AI usage

and engagement without manipulating the variables. According to Bhat

(2018), researchers gather information for descriptive correlational research in

order to explain the variables of interest and determine their relationships.

Researchers do not attempt to find cause-and-effect relationships or change

any variables. Rather, they merely observe and quantify the variables of

interest before examining the patterns and connections that show up in the

data.

The study utilizes the research design to measure the relationship

between AI-powered educational tool usage (their use of AI as a part of

students learning interaction, communication, and overall engagement) and

learning engagement. Artificial intelligence's function as a mediator in this

relationship will also be measured. In order to determine whether there are

significant relationships between these variables, the design will include

collecting numerical data on them and evaluating the results.

Research Locale

The research will take place within the premises of Davao City (Figure 2),

which is the regional center of the Davao Region. Positioned on the southeast

side of Mindanao, it faces the Davao Gulf with Samal Island across the

waters.

The study will be conducted at Holy Child College of Davao, which is

located in Brgy. Sto. Niño, Green Meadows Subdivision, Tugbok District,

Davao City. This school was chosen for its diverse student population,

particularly from the HUMSS track, as well as its ability to provide secondary

education at both the junior and senior high school levels. The study will only

include students enrolled in these levels at Holy Child College in Davao.

Schools outside of this location, as well as institutions that do not provide

secondary education, will be excluded from the study.

Figure 2. Maps of Holy Child College of Davao, Del Sur

Research Respondent

The researchers will use stratified sampling to identify the sample size

for the study. Through the use of Raosoft, it will be determined that the total

sample size will consist of 142 respondents from Grade 12 HUMSS students.

Raosoft is an online tool designed to help researchers determine the

appropriate sample size for their study based on statistical parameters. It

calculates the sample size needed to achieve a desired level of confidence,

margin of error, and population size. Humanities and Social Sciences

(HUMSS) students are ideal respondents for this study because their strand

emphasizes critical thinking, ethical reasoning, and societal impact, which

align with the discussion of AI's ethical implications. According to Stefan

(2023), the humanities play a vital role in understanding the ethical

implications of AI by guiding choices and evaluating its impact on society.

They help define and articulate ethical values like human dignity, autonomy,

privacy, fairness, accountability, transparency, and diversity. Additionally, they

aid in translating these values into norms and standards for governing AI.

Research Instrument

This research utilized an adapted questionnaire from pre-existing

instruments, which were modified by the researchers with the help of the

research adviser.

The first part of the questionnaire consisted of the independent variable,

the AI-Powered Educational Usage Questionnaire (APEUQ) by Alcantara et

al. (2024), with indicators categorized into three subcategories: Effectiveness

of AI Tools, Quality of Education with Artificial Intelligence, and Artificial

Intelligence Integration. The second part of the questionnaire consisted of the

dependent variable, the Learning Engagement Questionnaire (LEQ).

developed by Kember et al. (2006), with indicators categorized into two

subcategories: cognitive engagement and communication skills.

A five-point Likert-type scale was used to determine the level of

AI-Powered Educational Usage of Grade 12 HUMSS senior high school

students. In the independent variable, the highest scale (5) indicated that the

level of AI-Powered Educational Usage of Grade 12 HUMSS senior high

school students was always manifested, and the lowest scale (1) indicated

that the level of AI-Powered Educational Usage of Grade 12 HUMSS senior

high school students was never manifested. For the dependent variable, the

highest scale (5) indicator that the level of learning engagement of Grade 12

HUMSS senior high students was always manifested, and the lowest scale (1)

indicated that the level of learning engagement of Grade 12 HUMSS senior

high school students was never manifested.

Table 1. Descriptors for the mean level of AI-Powered Educational Usage of

Grade 12 HUMSS senior high school students

Range of Descriptive Level Interpretation

Mean

4.20-5.00 Very High It indicates that the AI-Powered Educational

Usage of Grade 12 HUMSS senior high

school students is always manifested.

3.40-4.19 High It indicates AI-Powered Educational Usage of

Grade 12 HUMSS senior high school students

is oftentimes manifested.

2.60-3.39 Moderate It indicates that the AI-Powered Educational

Usage of Grade 12 HUMSS senior high

school students is sometimes manifested.

1.80-2.59 Low It indicates that the AI-Powered Educational

Usage of Grade 12 HUMSS senior high

school students is seldomly manifested.

1.00-1.79 Very Low It indicates that the AI-Powered Educational

Usage of Grade 12 HUMSS senior high

school students is never manifested.

Table 2. Descriptors for the mean level of learning engagement of Grade 12

HUMSS senior high students.

Range of Descriptive Level Interpretation

Mean

4.20-5.00 Very High It indicates that the Learning Engagement

of Grade 12 HUMSS senior high students

is always manifested.

3.40-4.19 High It indicates that the Learning Engagement

of Grade 12 HUMSS senior high students

 is oftentimes manifested.

2.60-3.39 Moderate It indicates that the Learning Engagement

of Grade 12 HUMSS senior high students

is sometimes manifested.

1.80-2.59 Low It indicates that the Learning Engagement

of Grade 12 HUMSS senior high students

is seldomly manifested.

1.00-1.79 Very Low It indicates that the Learning Engagement

of Grade 12 HUMSS senior high students

is never manifested.

Data Gathering Procedure

The procedures for carrying out the study "The Impact of AI-Powered

Educational Usage on Learning Engagement of Grade 12 HUMSS Students"

are described in this section. In order to gather a list of potential Grade 12

HUMSS student responders, the researcher will first obtain consent from the

principals or heads of schools at a few private colleges and institutions.

Respondents will be made aware of the purpose and nature of the study

prior to data collection. To make sure participants are aware of the study's

scope, a properly signed Informed Consent Form (ICF) will be acquired.

Students will be separated by gender and academic achievement (high,

average, and poor) strata using stratified sampling. Data will be tabulated,

processed, analyzed, and interpreted when validated survey questions have

been disseminated and gathered. The results will be displayed in tables, and

then the related ramifications will be discussed.

Statistical tools

The data will be interpreted using descriptive statistics like mean, Standard

Deviation, Pearson-r correlation, and regression.

Mean. This will be used to determine the impact of AI-powered educational

tool usage on students' learning engagement.

Standard Deviation. This will be used to quantify how the variables in this

study vary or disperse from the mean.

Pearson-r. This will be used to find if the AI-powered educational tool has a

significant relationship with the students' learning engagement.

Ethical Considerations

Ethical considerations play a crucial role in research involving human

participants, ensuring that the rights, well-being, and confidentiality of

individuals are protected throughout the study. In the study "The Impacts of

AI-Powered Educational Tools on Learning Engagement of Grade 12 HUMSS

Students," the following ethical considerations will be applied:

Confidentiality: We will ensure the confidentiality of participants' personal

information and data collected during the study. All data will be anonymized

and stored securely to prevent unauthorized access. Confidentiality will be

maintained throughout data collection, analysis, and surveys.

 Respect for Autonomy: We will respect the autonomy of participants by

allowing them to make informed decisions about their involvement in the

study. Students have the freedom to decline participation or withdraw from the

study without facing any negative consequences.

Non-Maleficence: We will take measures to ensure that the study does not

cause harm to participants. This includes avoiding sensitive or triggering

questions and ensuring a comfortable environment for all participants.

Fair Treatment: All participants will be treated fairly and respectfully

throughout the research process. We will avoid any form of discrimination,

coercion, or exploitation. Equal opportunities for participation will be provided

to all eligible students.

By adhering to these ethical considerations, we can conduct the study on

Grade 12 HUMSS students regarding the negative impacts of AI-powered

educational tools on learning engagement in an ethical and responsible

manner, ensuring the protection of participants' rights, confidentiality, and

well-being throughout the research process.

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