INTERNATIONAL JOURNAL OF RESEARCH AND INNO V A T ION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS |V o lume IX Issue XXVI December 2025 | Special Issue on Education

Students’Awareness and Perception of AI Integration in Packaging

Design and Marketing: A Classroom-Based Case Study

Mohd Qadafie Ibrahim¹, Kamarul Amir Mohamed²

Faculty of Industrial and Manufacturing Technology and Engineering, Universiti Teknikal Malaysia

Melaka, Malaysia

DOI: https://dx.doi.org/10.47772/IJRISS.2025.903SEDU0760

Received: 08 December 2025; Accepted: 15 December 2025; Published: 30 December 2025

ABSTRACT

The rapid integration of Artificial Intelligence (AI) tools into creative and commercial industries necessitates a

corresponding shift in higher education curricula. This paper presents a classroom-based case study

investigating engineering students' awareness and perception of AI integration within a "Packaging Design and

Technology" course at a technical university. The project required students to design a sustainable chip package

and execute a TikTok marketing campaign, explicitly mandating the use of AI tools across the entire workflow;

from ideation and graphic design to content creation and campaign strategy. Through surveys, analysis of the

project brief, AI workflow examples, and a review of contemporary literature, this study establishes a

framework for evaluating student AI literacy, ethical awareness, and perceived impact on creativity and

efficiency. Preliminary findings suggest that structured, mandatory integration of AI in project-based learning

is a critical pedagogical approach for bridging the skills gap between academic training and industry demands

in the converging fields of design, engineering, and marketing.

Keywords: AI integration in higher education, AI literacy in students, Ethical awareness in AI, AI enhanced

packaging technology, green innovation in packaging, AI driven content creation

INTRODUCTION

The confluence of design, engineering, and marketing has been profoundly reshaped by the advent of

generative Artificial Intelligence (GenAI). Tools capable of rapid ideation, asset generation, and data-driven

optimization are moving from novelties to essential professional competencies. In the context of packaging, a

discipline that bridges material science (engineering), aesthetics (design), and consumer engagement

(marketing), AI offers unprecedented potential for innovation in sustainability, durability, and market appeal.

To prepare for future professionals, educational institutions must proactively integrate AI literacy into their

core curriculum. This paper examines a specific instance of this integration: a project within the "Packaging

Design and Technology" course at Universiti Teknikal Malaysia Melaka (UTeM). The project served as a

controlled case study to observe and analyze students' engagement with AI. The central research questions

guiding this study are:

A. What is the level of students' awareness regarding the range and application of AI tools relevant to

packaging design and marketing?

B. What are students' perceptions of AI's impact on their creative process, technical efficiency, and ethical

responsibilities within a project-based learning environment?

The subsequent sections will detail the theoretical framework, the case study methodology derived from the

project brief, a discussion of anticipated findings, and the pedagogical implications of this mandatory AI

integration.

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INTERNATIONAL JOURNAL OF RESEARCH AND INNO V A T ION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS |V o lume IX Issue XXVI December 2025 | Special Issue on Education

LITERATURE REVIEW AND THEORETICAL FRAMEWORK

AI in Design and Engineering Education

The literature on AI in design education highlights its role as a co- creator and accelerator rather than a mere

tool [1]. AI systems can rapidly generate design variations, optimize structural integrity, and simulate real-

world performance, such as courier durability testing [2]. Furthermore, AI is increasingly recognized as a

critical enabler for sustainable packaging design, assisting in material optimization, life-cycle analysis, and the

development of biodegradable solutions [6]. Specifically, AI-driven green innovation can promote a

multifaceted strategy for sustainability, integrating operational efficiency and waste reduction [7]. For

engineering students, this exposure is crucial for understanding the future of product development, where AI-

driven simulation and optimization will be standard practice. The project's requirement for students to use AI

for "research, concepting, asset generation, iteration, and analytics" directly aligns with this literature,

positioning AI as an end-to-end workflow component [5].

AI in Marketing and Content Creation Education

In marketing, AI is transforming content strategy, personalization, and campaign execution. The case study's

focus on a TikTok marketing campaign is particularly relevant, as short-form video content is highly amenable

to AI-driven tools for scripting (e.g., ChatGPT), music generation (e.g., Suno.ai), and video editing (e.g.,

CapCut AI) [3]. Educational objectives in this domain must move beyond simple tool usage to encompass

strategic application and KPI-ready planning, as mandated by the project's assessment criteria. Furthermore,

literature emphasizes the need for students to develop robust prompting skills and a strong ethical awareness

regarding intellectual property and bias in AI-generated content [4].

Awareness, Perception, and AI Literacy

The mandatory nature of AI integration in this project provides a unique opportunity to study these dimensions,

moving beyond voluntary adoption to observe engagement under a required curriculum structure. This study

adopts a framework centered on three key dimensions of AI literacy as in Table 1.0:

Table 1.0: Key dimensions of AI

Dimension

Definition in Case Study Context

Measurement Proxy in Project

Awareness

Knowledge of the existence, function, Explicit listing of AI tools used (e.g.,

and range of AI tools applicable to the Midjourney, ChatGPT, Suno.ai) and

project's design and marketing tasks.

documentation of their specific application in

the workflow.

Perception

(Efficiency)

Students' subjective evaluation of how Self-reported feedback on time saved, quality

AI impacted the speed, quality, and of AI-generated assets, and ease of iteration.

effort required for project completion.

Perception

(Creativity &

Ethics)

Students'

views

on

AI's

role

in Assessment of "AI use: strategic, ethical,

augmenting or hindering their creative documented" in the final report and analysis

output, and their understanding of of the originality of the AI-generated jingle

ethical use (e.g., originality, attribution).

and video concept.

Case Study Methodology

The case study is situated within the Packaging Design and Technology course, a final-year subject for

engineering students. The project was conducted over five weeks by teams of 3-4 students. The project’s core

challenge was to design a sustainable, courier-durable chip package and market it via a TikTok campaign.

3.1 Survey Instrument and Sample

A total of 25 students, representing approximately 6-8 project teams, participated in the survey. The instrument

utilized a 5-point Likert scale (1=Strongly Disagree, 5=Strongly Agree) to measure the three dimensions of AI

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INTERNATIONAL JOURNAL OF RESEARCH AND INNO V A T ION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS |V o lume IX Issue XXVI December 2025 | Special Issue on Education

literacy established in the theoretical framework (Table 2.0). Then, the standard reliability coefficient formula,

Cronbach’s alpha is used as follows:

푘

훼

푇

Where:

푘= number of items on the scale

휎_푖²= variance of each individual item

2

휎_푇= variance of the total score (sum of all items for each respondent)

Table 2.0: Suggested Survey Questions (N=25)

Dimension

Question

Awareness

Q1: I was aware of most of the AI tools suggested in the project brief

(e.g., Midjourney, Suno.ai) before this project.

Q2: The project significantly increased my knowledge of AI tools

relevant to packaging design and marketing.

Perception (Efficiency)

Q3: Using AI tools significantly reduced the time required for ideation

and content creation.

Q4: AI tools helped me achieve a higher quality of work than I could

have achieved without them.

Perception (Creativity & Ethics)

Q5: AI tools augmented my creativity rather than replacing it.

Q6: I am confident in my ability to use AI tools ethically and

responsibly.

Mandatory AI Integration

The project brief explicitly defined the scope of AI use, transforming the project into a quasi-experimental

setting for AI integration. The key AI requirements as in Table 3.0. This structured approach ensures that all

students, regardless of prior experience, were compelled to engage with a diverse set of AI tools, providing rich

data on their awareness and perception.

Table 3.0: Key AI requirements

Project Stage

AI Application Requirement

Example Tools Cited in Brief

Ideation

Concept

& Leverage AI for research and early visual ideas Midjourney, DALL·E, Perplexity

for packaging shapes or materials.

Graphic Design

Use AI to generate logo and color options, AI Logo/Color Generators

followed by manual refinement.

Marketing

Campaign

Use AI for scripting, music/jingle creation, and ChatGPT,

video editing. AI/Runway

Suno.ai/Udio,

CapCut

Assessment

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Final report must address "AI use: strategic, N/A

ethical, documented."

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INTERNATIONAL JOURNAL OF RESEARCH AND INNO V A T ION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS |V o lume IX Issue XXVI December 2025 | Special Issue on Education

RESULTS

Initial analysis confirmed a high baseline awareness of AI tools in packaging design, with 96.0% of

respondents indicating they had heard of them. The survey findings are summarized in Table 4.0, based on the

mean scores of the Likert-scale items.

The results show a clear pattern of strong agreement across most items (Mean average 4.0), with the highest

scores indicating a positive impact on Understanding (4.24), Creativity Benefit (4.20), and Ease of Integration

(4.20). This strongly supports the hypothesis that mandatory, structured AI integration is an effective

pedagogical tool and is perceived by students as a major boost to workflow speed and practical utility.

The lowest mean score across all items is for the statement "AI-generated designs should be disclosed to

clients" (Mean=3.80, 60% agreement). This score, while above the neutral point of 3.0, is notably lower than

the other agreement rates, suggesting that ethical norms regarding transparency in the design process are still

forming.

Table 4.0: Survey Results (Mean Score on 5-point Likert Scale, N=25)

Stages

Survey Item (Abbreviated)

Mean

% Agree/ Strongly Agree

Pre-Project Attitudes

Creativity benefit

4.20

84%

Job disruption

3.92

4.00

4.12

4.04

4.08

4.00

4.24

3.92

4.20

4.04

3.80

68%

84%

88%

80%

72%

88%

84%

88%

60%

Career readiness

Copyright concerns

Efficiency

In-Class Experience

Creative influence

Human–AI balance

Effective use

Post-Project Reflection

Understanding improved

Creativity enhanced

Ease of integration

Future confidence

Transparency practice

Ethical Concern

Reliability Analysis

Reliability analysis indicates that both composite scales derived from the survey exhibit strong internal

consistency. The in-class experience scale (four items capturing effective use, workflow efficiency, positive

creative influence, and human–AI balance) achieved Cronbach’s α = 0.919, which is commonly interpreted as

excellent reliability. The post project attitude scale (four items capturing improved understanding, creativity

enhancement, ease of integration, and future confidence) yielded Cronbach’s α = 0.827, indicating good

reliability. These coefficients suggest that the items within each composite are measuring the same underlying

construct with minimal random errors, and that the resulting composite scores are suitable for use in inferential

analyses and for reporting summary statistics.

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INTERNATIONAL JOURNAL OF RESEARCH AND INNO V A T ION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS |V o lume IX Issue XXVI December 2025 | Special Issue on Education

Table 5.0: Reliability Results (Cronbach’s α)

Coefficients group

Cronbach’s α

Experience

0.919

Post-attitude

0.827

Results of the open-ended responses

The results in Table 6.0 of the open-ended responses (qualitative findings) from the survey (which remain

relevant to the themes of the new quantitative data) provide rich context, revealing three dominant themes:

Table 6.0: Qualitative Findings

Dominant themes

Findings

AI as a Co-Pilot, not a

Replacement

Students consistently framed AI as a supportive tool for the initial, iterative

phases of design. Students want AI taught as co‑creative support, emphasizing

skills (prompting, evaluation) and fundamentals of design rather than

substitution. This aligns with the high mean score for Creative Influence (4.04)

and Human-AI Balance (4.08).

The Necessity of Human Most students used AI to accelerate ideation (logos/colors/layout options) but

Refinement

insisted final creative decisions came from human refinement. A minority

worried about erosion of critical thinking if over‑relying on AI outputs. While

acknowledging the speed of AI-generated assets, students emphasized the need

for manual refinement. This suggests that the value of the designer shifts from

pure creation to curation, refinement, and critical application of AI outputs.

Future Role: Critical

Thinking and Curation

The project altered the students' view of their future roles, shifting the focus

from technical execution to higher-order skills. Students anticipate a shift to

human‑led orchestration, with AI as accelerator; they highlight the need to

guard quality and sustain human judgment. This is supported by the high

agreement on Career Readiness (4.00) and Future Confidence (4.04).

DISCUSSION

The findings provide compelling evidence for the pedagogical value of mandatory AI integration in design and

engineering curricula. However, the comparatively lower score for transparency practices (Mean = 3.80)

highlights a critical area for improvement. While students readily adopt AI for efficiency and creativity, ethical

documentation and disclosure remain uncertain. This reflects broader industry debates on intellectual property

and transparency in AI-assisted creative work.

Pedagogical stance: AI as a co‑creative partner

Across recommendations, students consistently frame AI as a supportive, co‑creative tool rather than a

replacement. This suggests coursework should prioritize prompt literacy, output evaluation, and design

fundamentals (color, typography, composition), positioning AI as an ideation catalyst while reinforcing human

judgment for convergence and finalization. The quotes emphasize teaching that keeps creativity with the

student, with AI handling breadth and speed. This aligns with your quantitative findings where efficiency and

creative influence show high agreement (≥84–88%), indicating that hands‑on AI use reinforces positive,

practical views of AI in design. There for, Modules should combine prompting strategies, critique sessions of

AI outputs, and manual refinement labs to reinforce creativity and critical judgment

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INTERNATIONAL JOURNAL OF RESEARCH AND INNO V A T ION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS |V o lume IX Issue XXVI December 2025 | Special Issue on Education

Process integration: AI speed for exploration, human for decisions

Students describe a clear process demarcation: AI for rapid exploration (logos, palettes, layouts) and human

refinement for better fit, nuance, and brand logic. They stress that the final design requires manual adjustments

to meet project goals and user needs. A minority voice warns that over‑reliance can blunt critical thinking.

From survey, post‑project “ease of integration” and confidence are high (means ≈4.20 and 4.04), while

disclosure agreement (60%) is comparatively lower, signaling emerging professional norms about

transparency. Thus, educators should teach a two-stage pipeline, one use AI for divergence, and human

refinement for convergence.

Meanwhile embedding rubrics that assess refinement quality, rationale, and transparency.

Professional identity: human leadership in an AI‑driven industry

Open‑ended reflections show students envision their future selves as directors of AI, not competitors. They

emphasize critical thinking, problem‑solving, and quality assurance as distinctly human strengths, with AI

automating repetitive tasks. Notably, job‑threat and copyright concern did not correlate with experience,

suggesting these are ambient risk perceptions rather than outcomes of classroom experience. Implication is that

curricula should prepare students for hybrid roles that combine creative direction, ethical evaluation, and client

communication.

Quality & ethics: guardrails for disclosure and originality

Although students see AI as enhancing efficiency and creativity, several comments caution about quality and

critical thinking erosion. Given the lower consensus on client disclosure, curricula should explicitly address

when/how to disclose AI use, copyright implications, and originality checks (e.g., similarity reviews, source

auditing). Embedding ethical checkpoints at key milestones (concept selection, refinement, final delivery) can

normalize these practices and reduce ambiguity.

CONCLUSION

This study demonstrates that structured, mandatory AI integration in project-based learning enhances students’

awareness, efficiency, creativity, and confidence in packaging design and marketing. Reliability analyses

confirm the robustness of the measurement instruments, supporting the validity of findings. Importantly, while

students perceive AI as a valuable co-creative partner, ethical norms regarding transparency remain

underdeveloped.

To prepare students for AI-driven industries, curricula must move beyond theoretical discussions and embed

project-based learning that mandates strategic and ethical use of diverse AI tools. Such approaches transform

students into critical, AI-literate professionals capable of leveraging technology to address complex challenges

in design, engineering, and marketing.

RECOMMENDATIONS

This study highlights the need to position Artificial Intelligence as a co‑creative partner in design education.

Educators should emphasize prompt literacy, evaluation of AI outputs, and manual refinement, ensuring that

students develop both technical proficiency and critical creative judgment.

AI integration should follow a two‑stage pipeline: rapid exploration through AI tools, followed by human

refinement to ensure brand fit, usability, and ethical standards. This approach balances efficiency with

originality and critical thinking.

Professional identity training must be prioritized. Students should be trained to lead AI systems through critical

thinking, ethical evaluation, and disclosure practices, preparing them to articulate the unique value of human

creativity in client-facing contexts.

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INTERNATIONAL JOURNAL OF RESEARCH AND INNO V A T ION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS |V o lume IX Issue XXVI December 2025 | Special Issue on Education

Quality and ethics should be embedded throughout the curriculum. Originality checks, copyright awareness,

and transparency norms must be normalized through workshops and assessment rubrics that explicitly evaluate

refinement quality, justification of design decisions, and documentation of AI use.

Finally, practical modules such as prompting labs, critique sessions, and client-simulation exercises should be

incorporated. Future research should employ advanced measurement techniques to strengthen the

generalizability of findings across cohorts and semesters.

REFERENCES

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118.

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5. Ulfy, M. A., Haque, A., & Huda, M. N. (2024). Integration Of Artificial Intelligence in Biodegradable

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