INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026

Effects ofAI on the Trust and Confidence of Faculty Members in

SubmittedAcademic Works by Students

Wyeth A. Aguio¹, Robinne Louisse Chretien M. Cuerdo², Nina R. Gerero³, Shena E. Gutierrez⁴, Ronah

Pearl V. Kadusale⁵, Harold R. Lucero⁶

College of Computer Studies, Quezon City University

DOI: https://doi.org/10.51583/IJLTEMAS.2026.150500177

Received: 14 May 2026; Accepted: 19 May 2026; Published: 11 June 2026

ABSTRACT

This study examined faculty trust, student confidence, and the perceived impact of artificial intelligence (AI)

tools in academic assessment within higher education institutions. Specifically, it explored faculty perceptions

regarding authenticity, authorial verifiability, and evaluative certainty; students’ confidence in submitting

academic work; and the influence of AI-related practices such as algorithmic surveillance and linguistic

flattening. Using a quantitative descriptive-correlational research design, data were gathered from faculty

members and students through survey questionnaires and analyzed using weighted mean, Pearson correlation,

and ANOVA. Findings revealed that faculty members generally remained confident in evaluating student

submissions, with a grand mean interpreted as “Confident/Agree,” although concerns regarding authenticity and

authorship verification persisted. Students likewise demonstrated confidence in submitting academic work,

particularly in academic self-efficacy, but also reported anxiety regarding AI detection systems and moderate

trust in institutional assessment practices. The study further found that AI tools significantly influenced student

behavior, particularly through linguistic flattening, where students intentionally simplified writing styles to avoid

detection. Correlation analysis showed no significant relationship between the frequency of AI detector use and

faculty trust in student submissions, while ANOVA results revealed no significant differences in perceptions

based on years of teaching experience. Overall, the study concluded that AI has substantially reshaped academic

assessment practices, faculty perceptions, and student writing behavior, emphasizing the need for balanced

institutional policies, ethical AI governance, improved AI literacy, and assessment frameworks that promote

both academic integrity and responsible AI use in higher education.

Keywords: academic integrity, artificial intelligence, faculty trust, higher education, student submissions,

quantitative research

INTRODUCTION

Artificial intelligence (AI) refers to computer systems designed to perform tasks that traditionally require human

intelligence, including learning, reasoning, and decision-making. Copeland, (2026) and National Aeronautics

and Space Administration, (2024) describe AI systems as capable of processing complex information, adapting

to new data, and supporting decision-making across diverse domains. In higher education, AI has become a

significant driver of digital transformation, where technological tools are integrated with pedagogical strategies

and organizational support to enhance teaching and learning processes (Oliviera & Souza, 2022). Parra G. &

Calero S. (2019) demonstrated that AI-assisted writing tools can improve students' writing performance when

used alongside instructor feedback, a finding that underscores the growing functional role of AI in academic

output. Vinay (2023) further highlights that AI applications in school and university settings are reshaping both

instructional delivery and student engagement with learning materials. These developments reflect a broader

transformation in higher education, where AI increasingly shapes how academic work is produced, submitted,

and evaluated (Robert, 2026)

In the current educational landscape, AI plays an increasingly influential role in students' academic activities.

Many students now rely on AI tools for writing assistance, research summarization, problem-solving, and

organizing academic tasks, a trend that Vieriu & Petrea (2025) attribute to AI's capacity for personalized learning

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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026

support and improved academic outcomes. While these tools can enhance productivity and reduce workload-

related stress, Elrefaei et al. (2025) note that the traditional processes of evaluating student outputs—previously

based on the assumption of individual effort and originality—are becoming more complex. Faculty members

face challenges in determining whether submitted files truly reflect students’ understanding or are heavily

assisted or generated by AI. This situation has raised concerns about academic integrity, authenticity, and

fairness in assessment, as well as inefficiencies in existing evaluation methods that were not designed for an AI-

assisted academic environment (Kasneci et al., 2023). Faculty members now face persistent challenges in

determining whether submitted files authentically reflect students' understanding or are heavily assisted or

generated by AI. Shrivastava & Shrivastava (2022) point out that the broader digitalization of higher education

had already introduced structural readiness gaps in institutions, and the arrival of generative AI has intensified

these pressures further. This situation has raised pressing concerns about academic integrity, authenticity, and

fairness in assessment, as well as significant inefficiencies in existing evaluation methods that were not designed

for an AI-assisted academic environment (Lee et al., 2024).

Although several studies have examined the use of AI in education, much of the existing literature focuses on

student outcomes, learning effectiveness, and the ethical implications of AI adoption. Edmund (2025) observes

that limited attention has been given to how AI specifically affects faculty trust and confidence in evaluating

student-submitted work. Existing institutional systems and policies often emphasize text-matching plagiarism

detection tools, which are effective for identifying direct plagiarism but insufficient for addressing AI-generated

or AI-rephrased content that does not constitute technical plagiarism yet raises legitimate concerns about

authenticity and originality (Ogwueleka, 2025). While some studies have begun examining faculty involvement

in AI-driven assessment, previous research rarely explores how faculty perceptions of AI influence their

decision-making, grading practices, and confidence in the academic credibility of student submissions

(Choiriyah et al., 2025; Herath et al., 2025). Herath et al. (2025) further add that although AI tools demonstrate

strong surface-level performance in educational tasks, they consistently fall short in the nuanced judgment that

characterizes meaningful academic assessment—a distinction that current detection frameworks have yet to

reliably capture. This gap highlights the need for focused investigation into the limitations of current evaluation

practices and the absence of clear frameworks to support faculty in maintaining trust within an AI-integrated

learning environment.

The increasing use of AI tools has also raised concerns about the reliability and consistency of academic

evaluation. Nassar (2025) found that comparisons between AI-generated and instructor feedback reveal only

moderate agreement between the two, underscoring the need for continued human oversight to ensure credible

and fair grading practices. Building on this, Lee et al. (2024) document that faculty across higher education

institutions report diminished confidence in the credibility of student submissions as AI use grows more

prevalent, further eroding the assessment relationship between educators and students. The specific problem

addressed by this study, therefore, is the lack of clear strategies and institutional support mechanisms that help

faculty adapt their assessment practices and maintain trust and confidence in evaluating student-submitted files

within an AI-integrated educational environment.

To address this problem, the study proposes practical strategies aimed at restoring trust and strengthening

evaluation practices in AI-assisted academic settings. These include the development of clearer AI-use

guidelines, the redesign of assessment methods to better account for AI involvement, the provision of faculty

training on AI literacy, and the integration of transparent academic integrity policies. Diamante et al. (2025)

support this direction, demonstrating that targeted professional development significantly enhances faculty trust

and confidence in AI-oriented teaching and assessment strategies. Toquero (2026) further argues that the absence

of coherent institutional AI policies has left faculty to manage these challenges individually, reinforcing the need

for structured, institution-wide frameworks as proposed in this study.

This study is significant to multiple stakeholders within the academic community. Faculty members will benefit

from a clearer understanding of how AI influences their trust in student work and from recommended strategies

to strengthen evaluation practices, contributing to more consistent and credible academic assessment (Khlaif et

al., 2024). Academic institutions may use the findings to develop updated policies, guidelines, and professional

development programs that address AI-related challenges in assessment (Toquero, 2026). Students will benefit

from clearer expectations regarding acceptable AI use, promoting both fairness and academic integrity (Borbon

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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026

et al., 2025). Lastly, future researchers may use this study as a foundation for further investigation into the

intersections of AI, trust, and assessment practices, contributing to the growing body of knowledge on the

responsible integration of AI in higher education.

A. Statement of the Problem

This study evaluates the effects of Artificial Intelligence (AI) on the trust and confidence of faculty members

regarding academic work submitted by students at Quezon City University. It examines how student AI usage

affects faculty evaluation and the academic relationship.

Specifically, this study seeks to answer the following questions:

a. What is the demographic profile of the respondents in terms of:

i. Faculty:

1. Academic department;

2. Years of teaching experience; and

3. Frequency of AI detection tool usage.

ii. Students:

1. Frequency of AI tool usage for academic tasks; and

2. Self-reported rating of AI utility (benefits vs. negative effects).

b. What is the level of faculty members' confidence in student submissions in terms of:

i. Perceived Authenticity;

ii. Authorial Verifiability; and

iii. Evaluative Certainty.

c. What is the level of students’ confidence in submitting academic works in terms of:

i. Academic Self-Efficacy;

ii. Detection Anxiety; and

iii. Institutional Trust.

d. How do students perceive the impact of AI on the academic environment in terms of:

i. Algorithmic Surveillance; and

ii. Linguistic Flattening.

e. What is the overall level of trust of faculty members toward student-submitted academic works?

f. Is there a significant relationship between the frequency of AI detection tool usage and the level

of trust faculty members have in student submissions?

g. Is there a significant difference in the perception of AI-mediated work when respondents are

grouped according to their demographic profile?

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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026

Related Studies

The reviewed literature consistently highlights that the integration of artificial intelligence in higher education

has significantly reshaped faculty perceptions of assessment, academic integrity, and student evaluation. Across

multiple studies, educators reported declining confidence in their ability to verify the authenticity of student

work, largely due to the increasing sophistication of AI-generated outputs and the limitations of current

detection technologies. Khlaif et al. (2024), Lee et al. (2024), Abdelaal & Al Sawy (2024), and Opele et al.

(2024) collectively emphasized that faculty concerns extend beyond technological unfamiliarity to deeper

anxieties surrounding authorship verification, credibility of submissions, and the erosion of traditional

assessment validity. These concerns are intensified by evidence showing that AI detection systems remain

unreliable and inconsistent, often failing to distinguish between authentic human writing and advanced AI-

assisted text (Kotmungkun et al., 2024; Plattner et al., 2024). Tang (2024) and Parker (2024) further argued that

the issue is no longer limited to detection accuracy but instead challenges the very purpose and meaning of

academic assessment in AI-mediated learning environments.

The literature also demonstrates that students engage with AI tools within a complex landscape shaped by

institutional expectations, academic pressures, and technological accessibility. Studies by Borbon et al. (2025),

Giray et al. (2025), and Désiron & Petko (2023) revealed that many students strategically conceal or modify

their AI use to avoid detection, reflecting what this study conceptualizes as the “performance of compliance.”

Research on AI-assisted writing further showed that these technologies reshape student voice, writing style, and

linguistic structure, complicating originality assessments and making the distinction between human and AI

contribution increasingly blurred (Marzuki et al., 2023; Delfin et al., 2025; Llausas et al., 2024; Clorion et al.,

2024). Collectively, these studies suggest that faculty challenges are no longer centered solely on identifying

AI-generated work but on interpreting student submissions in environments where AI and human authorship

are deeply intertwined.

Beyond academic integrity concerns, the literature highlights the broader relational and pedagogical

consequences of AI integration. Studies by Guan et al. (2021), Jinowat et al. (2026), Arshavskaya (2026), and

Otermans et al. (2026) found that AI-mediated assessment and feedback practices alter teacher-student

dynamics by increasing faculty vigilance, weakening trust, and complicating authentic feedback processes.

These relational tensions directly influence grading and evaluation practices, with instructors revising

assessment frameworks due to uncertainty regarding authorship and the role of AI in student outputs (Chavez

et al., 2024; Espartinez, 2025). Herath et al. (2025) and Antonelli et al. (2025) additionally noted that although

AI demonstrates strong surface-level capabilities, it lacks nuanced human judgment, reinforcing the need for

careful faculty oversight and institutional guidance.

The reviewed studies further establish that faculty confidence in AI-related assessment is strongly influenced

by psychological readiness, institutional support, and policy environments. Shahid et al. (2024), Sultan et al.

(2025), and Wu et al. (2025) identified risk perception, self-efficacy, organizational culture, and institutional

climate as major determinants of faculty acceptance and trust in AI systems. Within the Philippine context,

researchers consistently found that while awareness of AI technologies is relatively high, institutional policies

and structured guidance remain insufficient (Giray et al., 2024; Toquero, 2026; Jala et al., 2026). Nonetheless,

professional development initiatives and increased AI literacy among faculty were shown to improve confidence

and encourage more constructive AI integration (Diamante et al., 2025; Capinding, 2026; Capinding &

Dumayas, 2024). Broader governance and ethical concerns were likewise emphasized by Arcilla et al. (2023),

Chua et al. (2023), Mallillin et al. (2025), and Sy et al. (2024), who argued that transparent policies,

accountability mechanisms, and institutional infrastructures are necessary to restore trust and support effective

AI governance in education.

Finally, the literature underscores that students experience AI integration differently depending on their literacy,

self-efficacy, and socio-demographic context. Studies focusing on ESL and under-resourced learners revealed

that AI detection systems may disproportionately affect vulnerable students, contributing to anxiety, impostor

syndrome, and fear of false accusations (Domingo, 2025; Asio, 2024; Albino et al., 2025). Additional research

demonstrated that demographic variables, psychosocial influences, and perceived usefulness significantly shape

how students adopt and interact with AI technologies (Hortelano & Salamia, 2025; Balasa et al., 2025; Acosta-

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Enriquez et al., 2025; Chen, 2025; Cao et al., 2025). At the same time, some studies acknowledged the positive

educational potential of AI, particularly in supporting self-directed learning and student engagement when used

responsibly (Giray et al., 2025; Fošner, 2024; Luo & Tang, 2026). Overall, the literature suggests that effective

responses to AI in higher education require balanced and context-sensitive approaches that consider not only

academic integrity and detection reliability but also student vulnerability, literacy, motivation, and institutional

readiness.

DESIGN AND METHODOLOGY

A. Research Design

This study employed a quantitative descriptive-correlational research design to examine the effects of Artificial

Intelligence on faculty members' trust and confidence in student-submitted academic work at Quezon City

University. The descriptive component allowed the researchers to measure and characterize the current levels of

faculty confidence across dimensions such as Perceived Authenticity, Authorial Verifiability, and Evaluative

Certainty, as well as students' confidence indicators, including Academic Self-Efficacy, Detection Anxiety, and

Institutional Trust. This design was appropriate given the study's aim to quantify perceptions and attitudes rather

than manipulate variables, making it well-suited for capturing the state of AI's influence on academic evaluation

practices within a defined institutional context.

The correlational component was employed to determine whether a significant relationship exists between

faculty members' frequency of AI detection tool use and their overall level of trust in student submissions, and

to identify significant differences in perceptions when respondents were grouped by demographic profile. Data

were gathered through a structured survey instrument using a four-point Likert scale, administered to both faculty

and students across five colleges: the College of Computer Studies, College of Engineering, College of

Education, College of Accountancy, and College of Business. The sample size of 386 respondents was

determined using Slovin's Formula with a 5% margin of error, applied against Quezon City University's total

population of 10,599. Statistical tools, including frequency and percentage, weighted mean, Pearson Correlation

Coefficient, and One-Way ANOVA, were utilized to analyze the gathered data systematically and objectively.

B. Data Gathering

The study was conducted in May 2026, and the data collection procedure analyzed the following procedure:

The researchers first secured the validation of the research instrument with the Statement of the Problem as the

basis and was validated by a degree holder of Bachelor of Science in Statistics graduated in University of the

Philippines - Visayas. Following this expert consultation, the "Level of Faculty Trust" section was converted

into a quantitative Likert scale. To mitigate pattern bias, reverse-coded statements were integrated throughout

the questionnaire. Additionally, the variables of "Algorithmic Surveillance" and "Linguistic Flattening",

previously combined, were separated into distinct independent sections.

Upon receiving certification, a reliability test was performed to ensure the instrument's internal consistency.

Utilizing Slovin’s Formula with a 5% margin of error (e = 0.05) to account for the 10,599 members of the Quezon

City University population, the researchers determined a minimum requirement of 386 respondents.

The collection employed a dual-approach strategy: Digital Distribution: An online survey via Google Forms was

utilized for streamlined data sorting and; Physical Distribution: Due to time constraints, traditional pen-and-

paper methods were also implemented, requiring a more intensive manual tallying process. Prior to distribution,

the researchers obtained formal Ethical Clearance from their research adviser. This permission authorized the

collection of data from both faculty and students, strictly stipulating that the information be used for academic

purposes only.

To maintain integrity, each participant was restricted to a single submission; once the target sample size was

reached, the digital portal was closed. The study was localized within Quezon City University, spanning the

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College of Computer Studies, College of Engineering, College of Education, College of Accountancy, and

College of Business.

In strict adherence to the Data Privacy Act of 2012, respondent confidentiality was prioritized, with privacy

notices explicitly included in both the physical and digital versions of the survey. Finally, the accumulated data

was organized and calculated according to the designated Statistical Treatment.

C. Statistical Treatment of Data

As soon as the researchers gathered the data, they were compiled, sorted, organized, and tabulated. They were

subject to statistical treatment in order to answer the questions proposed in the study. The following statistical

tools were employed:

1.

Frequency and Percentage.

Used to determine the proportion of each given data point in relation to the total population, specifically

for the demographic profile of students and faculty members (SOP 1) . The formula used is:

퐟

% =

× ퟏퟎퟎ

퐍

Where:

% = Percentage

F = Frequency

N = Total number of cases

Weighted Mean.

2.

Used to measure the average response of students and faculty across various domains, including

Academic Self-Efficacy, Detection Anxiety, Institutional Trust, and Perceived Authenticity (SOP 2, 3, 4,

and 5) . Responses were based on a 4-point Likert scale. The formula used is:

Σ(f × w)

WM =

N

Where:

MW = Weighted Mean

∑ = Summation

w = Weight of each response

f = Frequency of each response

N = Total number of respondents

3.

Pearson Correlation Coefficient (r).

Used to determine if a significant relationship exists between the frequency of AI detection tool usage

and the level of trust faculty members have in student submissions. The formula is:

[(

)(

)]

Σ x − x y − y̅

r =

2

∑(

)

∑(

)

y − y̅

√

x − x̅

×

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Where:

r = Pearson Correlation Coefficient

X = Frequency of AI detection tool usage

Y = Level of faculty trust

N = Total number of faculty respondents

One-Way Analysis of Variance (ANOVA)

4.

This was employed to determine if there are significant differences in the perception of AI-mediated

work when respondents are grouped according to their demographic profile, such as years of teaching

experience or academic department (SOP 7). The formula for the F-statistic is:

MS_d

F =

MS_E

Where:

F = ANOVA Coefficient

MS_d= Mean square between groups (variance caused by the different categories)

MS_e= Mean square within groups (variance within each specific category)

RESULT AND DISCUSSION

This chapter presents the results, analysis, and interpretation of the data gathered to evaluate the effects of

Artificial Intelligence (AI) on the trust and confidence of faculty members and students at Quezon City

University. The presentation follows the order of the objectives stated in the Statement of the Problem.

Profile of the Respondents

A total of 386 respondents were surveyed, consisting of 346 students and 40 faculty members.

Table 1. Distribution of Faculty by Academic Department

DEPARTMENT

CCS

FREQUENCY

PERCENTAGE

22.5%

9

6

15.0%

COA

7

17.5%

COB

14

4

35.0%

COED

COE

10.0%

This presents the demographic profile of the faculty respondents according to their academic department. Data

shows that the College of Education (COEd) had the highest number of participants with 14 (35%), while the

College of Engineering (COE) had the lowest with 4 (10%).

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Table 2. Years of Teaching Experience

EXPERIENCE

0–5 YEARS

6–10 YEARS

11–20 YEARS

21+ YEARS

FREQUENCY

PERCENTAGE

25.0%

10

25.0%

Table 2 displays the years of teaching experience of the faculty. The respondents are equally distributed across

all experience brackets, with each category containing exactly 10 respondents (25%). This ensures a balanced

perspective between new and veteran educators.

Table 3 below, describes the frequency of AI detection tool usage among faculty. A total of 18 respondents

(45%) use these tools "Sometimes," while 7 (17.5%) use them "Always." This indicates that a majority of

faculty rely on algorithmic verification to some degree.

Table 3. Frequency of AI Detection Tool Usage

FREQUENCY FREQUENCY PERCENTAGE

COUNT

7

17.5%

45.0%

35.0%

2.5%

ALWAYS

SOMETIMES

RARELY

NEVER

18

14

1

Table 4. Student Frequency of AI Tool Usage

USAGE

FREQUENCY PERCENTAGE

FREQUENCY

COUNT

150

157

37

43.4%

45.3%

10.7%

0.6%

DAILY/ALWAYS

SOMETIMES

RARELY

2

NEVER

Table 4 summarizes the frequency of student AI usage. Out of 346 students, 157 (45.4%) use AI tools

"Sometimes," and 150 (43.4%) use them "Daily." Only 2 (0.6%) students reported "Never" using AI, showing

that AI is deeply integrated into student academic habits.

Table 5. Student Self-Reported Rating of AI Utility

UTILITY RATING

FREQUENCY COUNT

PERCENTAGE

52.0%

180

148

25

HIGHLY BENEFICIAL

SOMEWHAT BENEFICIAL

SOMEWHAT NEGATIVE

42.8%

7.2%

Table 5 presents the students' perceived utility of AI. A majority of 180 students (52%) find AI "Highly

Beneficial," while only 25 (7.2%) perceive it as "Somewhat Negative."

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Level of Faculty Members' Confidence

Table 6. Level of Faculty Confidence in Student Submissions

INDICATOR

WEIGHTED INTERPRETATION

MEAN

2.70

2.71

3.01

2.81

Confident/Agree

PERCEIVED

AUTHENTICITY

AUTHORIAL

VERIFIABILITY

EVALUATIVE

CERTAINTY

GRAND MEAN

The table presents the respondents’ level of confidence in evaluating student outputs within an AI-assisted

academic environment. The grand mean of 2.81, interpreted as “Confident/Agree,” indicates that faculty

members generally remain confident in their ability to assess student work despite the growing use of AI tools.

Among the indicators, Evaluative Certainty obtained the highest weighted mean of 3.01, suggesting that

respondents still trust their professional judgment, grading practices, and ability to determine learning outcomes

even when AI may have influenced student submissions. This finding supports Herath et al. (2025), who

emphasized that human evaluators continue to possess stronger contextual and nuanced judgment compared to

AI systems.

Meanwhile, Authorial Verifiability (2.71) and Perceived Authenticity (2.70) received comparatively lower

weighted means, although both remained within the “Confident/Agree” interpretation. These findings suggest

that while faculty members generally trust student submissions, they experience moderate uncertainty in

verifying whether outputs genuinely reflect students’ own work. The results align with Khlaif et al. (2024), Lee

et al. (2024), and Opele et al. (2024), who found that educators increasingly struggle with confirming authorship

and originality due to the sophistication of AI-generated writing. Overall, the findings indicate that faculty

confidence in evaluation remains positive, but concerns regarding authenticity and authorship verification persist

in AI-mediated learning environments.

Level of Students' Confidence

Table 7. Level of Students' Confidence in Submitting Works

INDICATOR

WEIGHTED

MEAN

INTERPRETATION

3.10

3.01

2.92

3.01

Confident/Agree

Agree

ACADEMIC SELF-EFFICACY

DETECTION ANXIETY

INSTITUTIONAL TRUST

GRAND MEAN

Agree

Confident/Agree

Table 7 presents the level of students’ confidence in submitting academic works in an AI-influenced learning

environment. The grand mean of 3.01, interpreted as “Confident/Agree,” indicates that students generally remain

confident when submitting their outputs despite concerns associated with AI use and detection systems. Among

the indicators, Academic Self-Efficacy obtained the highest weighted mean of 3.10, suggesting that students

generally believe in their capability to complete academic tasks and produce acceptable outputs. This finding

aligns with Chen (2025) and Giray et al. (2025), who emphasized that AI tools can enhance student engagement,

confidence, and perceived competence when used to support learning and academic work.

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Meanwhile, Detection Anxiety recorded a weighted mean of 3.01, interpreted as “Agree,” indicating that

students still experience concern or apprehension regarding the possibility of their work being flagged as AI-

generated. This result supports the findings of Domingo (2025) and Albino et al. (2025), who noted that AI-

related scrutiny and fear of false accusations contribute to anxiety among students, particularly those who rely

on AI-assisted tools for writing support. The finding suggests that even confident students remain cautious about

how their submissions may be interpreted by faculty and detection systems.

Similarly, Institutional Trust obtained the lowest weighted mean of 2.92, though still interpreted as “Agree.”

This indicates that students moderately trust institutional policies and faculty judgment regarding AI-related

assessment practices, but some uncertainty remains. The result reflects the observations of Toquero (2026) and

Jala et al. (2026), who found that limited institutional guidance and inconsistent AI policies in higher education

contribute to uncertainty among students and educators alike. Overall, the findings suggest that while students

maintain confidence in their academic abilities, concerns regarding AI detection and institutional fairness

continue to shape their submission experiences.

This indicates that students consistently provided high ratings toward these indicators, evidenced by the grand

mean of 3.01 interpreted as "Agree/Confident." Academic Self-Efficacy achieved the highest mean of 3.10,

showing students feel confident in their output quality when using AI tools.

Impact of AI on the Academic Environment

Table 8. Perceived Impact of AI Tools

INDICATOR

WEIGHTED INTERPRETATION

MEAN

2.84

Agree

ALGORITHMIC

SURVEILLANCE

2.97

Agree

LINGUISTIC

FLATTENING

Table 8 presents the perceived impact of AI tools on the academic environment, particularly on student behavior

and writing practices. The grand mean of 2.91, interpreted as “Agree,” indicates that respondents generally

perceive AI technologies and detection systems as having a noticeable influence on how students prepare and

submit academic work. This suggests that the presence of AI-related monitoring and detection mechanisms has

begun shaping not only assessment practices but also students’ behavioral and linguistic choices in academic

settings.

Among the indicators, Linguistic Flattening obtained the higher weighted mean of 2.97, indicating that students

tend to simplify or alter their writing styles to avoid being flagged by AI detection software. This finding implies

that students may intentionally reduce stylistic complexity, creativity, or advanced language use out of fear that

sophisticated writing could be misidentified as AI-generated. The result supports the findings of Marzuki et al.

(2023) and Llausas et al. (2024), who observed that AI-assisted writing technologies and detection systems

influence students’ writing patterns and reshape their linguistic expression. The finding also reflects the concept

of the “performance of compliance,” wherein students adapt their outputs not solely for learning purposes but to

satisfy algorithmic expectations and avoid suspicion.

Meanwhile, Algorithmic Surveillance obtained a weighted mean of 2.84, also interpreted as “Agree,” suggesting

that students are aware of and affected by the increasing use of AI monitoring and detection technologies in

academic institutions. This indicates that students perceive AI detection systems as a form of surveillance that

influences their academic behavior and submission practices. The result aligns with the observations of Guan et

al. (2021) and Jinowat et al. (2026), who noted that AI-mediated educational environments can create heightened

vigilance, anxiety, and behavioral adjustment among both teachers and students. Overall, the findings suggest

that AI tools are reshaping the academic environment by influencing how students write, present, and regulate

their academic outputs.

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Overall Level of Faculty Trust

Table 9. Grand Weighted Mean of Faculty Trust

VARIABLE

WEIGHTED

MEAN

INTERPRETATION

2.63

Agree / Trusting

OVERALL

TRUST

LEVEL

Table 9 presents the overall level of faculty trust in evaluating student outputs within an AI-assisted academic

environment. The weighted mean of 2.63, interpreted as “Agree/Trusting,” indicates that faculty members

generally maintain a positive level of trust despite the growing challenges associated with AI-generated content

and detection systems. This suggests that educators still believe in their ability to exercise professional judgment

and uphold academic standards even as AI tools increasingly influence teaching and assessment practices.

However, the moderate level of trust reflected in the result also implies that faculty confidence is not absolute

and may still be affected by concerns regarding authenticity, authorship verification, and the reliability of AI

detection technologies. The finding aligns with Khlaif et al. (2024), Lee et al. (2024), and Plattner et al. (2024),

who reported that educators continue to experience uncertainty in confirming the originality of student work in

AI-mediated learning environments. Overall, the result suggests that while faculty members remain generally

trusting, the integration of AI in education continues to challenge traditional perceptions of academic integrity

and evaluative certainty.

Significant Relationship Analysis

The Pearson correlation analysis revealed an r=0.1448, indicating a very weak positive relationship between the

frequency of AI detector use and faculty trust in student submissions. However, the obtained p-value of 0.366

shows that the relationship is not statistically significant. This means that the frequency with which professors

use AI detection tools does not significantly influence or predict their level of trust toward students. The result

suggests that faculty trust remains relatively independent of reliance on AI detection technologies.

The scatterplot further supports this finding by showing no clear linear relationship between the two variables,

which is consistent with the interpretation of “no correlation.” Although some faculty members may frequently

use AI detectors, this does not necessarily correspond to lower or higher levels of trust in student work. The

finding implies that faculty trust may instead be shaped by other factors such as professional experience,

institutional policies, assessment practices, or personal perceptions of academic integrity rather than solely by

the use of AI detection systems. This supports the literature of Shahid et al. (2024) and Sultan et al. (2025),

which emphasized that faculty attitudes toward AI are influenced by broader psychological and institutional

factors rather than technology use alone

Significant Difference Analysis

Table 10. ANOVA Results for Differences in Perception

DEMOGRAPHIC F-

P-

INTERPRETATION

GROUP

VALUE VALUE

0.81 0.492

Not Significant

YEARS OF

EXPERIENCE

Table 10 presents the ANOVA results examining whether significant differences exist in faculty perceptions

when grouped according to years of teaching experience. The computed F-value of 0.81 with a corresponding

p-value of 0.492 indicates that the result is not statistically significant. Since the p-value is greater than the

standard significance level of 0.05, the null hypothesis is accepted, suggesting that faculty perceptions do not

significantly differ based on years of experience.

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This finding implies that both less experienced and more experienced faculty members share relatively similar

perceptions regarding AI-related assessment issues, trust, and academic integrity concerns. The result suggests

that the challenges posed by AI technologies in education are experienced broadly across faculty groups

regardless of professional tenure. This aligns with the findings of Lee et al. (2024) and Opele et al. (2024), which

showed that concerns regarding AI-generated work and authenticity are common among educators across

different academic backgrounds and levels of experience.

Furthermore, the result may indicate that exposure to AI technologies and the challenges associated with AI-

assisted student outputs have become widespread enough that years of experience no longer create substantial

differences in perception. Instead, faculty attitudes may be influenced more strongly by institutional context, AI

literacy, and professional readiness rather than length of teaching service alone, as supported by Shahid et al.

(2024) and Wu et al. (2025).

CONCLUSION

The study concluded that faculty members generally maintain confidence and trust in evaluating student

submissions despite the increasing integration of artificial intelligence in academic environments. Respondents

demonstrated positive perceptions in terms of perceived authenticity, authorial verifiability, and evaluative

certainty, indicating that educators still rely on their professional judgment when assessing student work.

However, concerns regarding the authenticity and originality of submissions remain evident, particularly due to

the growing sophistication of AI-generated content and the limitations of AI detection technologies. Faculty trust

was found to remain moderately positive, suggesting that while AI has introduced challenges to academic

integrity and assessment practices, educators continue to uphold confidence in their evaluative capabilities.

The findings also revealed that students generally remain confident in submitting their academic work,

particularly in terms of academic self-efficacy. Nevertheless, detection anxiety and moderate institutional trust

indicate that students experience apprehension regarding AI detection systems and the fairness of institutional

assessment practices. The study further established that AI tools significantly influence the academic

environment, particularly through linguistic flattening and perceptions of algorithmic surveillance. These

findings suggest that students may intentionally modify or simplify their writing styles to avoid being flagged

by AI detectors, reflecting behavioral adjustments shaped by AI-mediated academic monitoring.

Furthermore, the correlation analysis showed no significant relationship between the frequency of AI detector

use and faculty trust in student submissions, indicating that faculty trust is independent of reliance on AI

detection tools. Similarly, the ANOVA results revealed no significant differences in perceptions when grouped

according to years of teaching experience, suggesting that concerns and attitudes toward AI in education are

shared across faculty members regardless of professional tenure. Overall, the study concludes that AI has

substantially reshaped academic assessment, student behavior, and faculty perceptions, highlighting the need for

balanced institutional policies, improved AI literacy, ethical assessment frameworks, and supportive educational

practices that promote both academic integrity and responsible AI use.

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