💻 computer science

AI in Work-Based Learning: Understanding the Purposes and Effects of Intelligent Tools Among Student Interns

This study of 384 Philippine student interns reveals that AI tools like ChatGPT are primarily used to enhance productivity, communication, and technical tasks during on-the-job training, highlighting moderate ethical confidence and the need for higher education programs to integrate AI literacy and clear usage policies.

Original authors: John Paul P. Miranda, Rhiziel P. Manalese, Sheila M. Geronimo, Vernon Grace M. Maniago, Charlie K. Padilla, Aileen P. De Leon, Santa L. Merle, Mark Anthony A. Castro

Published 2026-04-01

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: John Paul P. Miranda, Rhiziel P. Manalese, Sheila M. Geronimo, Vernon Grace M. Maniago, Charlie K. Padilla, Aileen P. De Leon, Santa L. Merle, Mark Anthony A. Castro

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you just landed your first big internship. You're excited, but also a little nervous. You're sitting at a desk with a mountain of tasks: writing reports, fixing typos, making slides, and figuring out what your boss actually wants you to do.

Now, imagine you have a super-smart, invisible assistant sitting right next to you. This assistant can write emails, fix your grammar, generate ideas, and even help you code. But here's the catch: your boss might not know you have this assistant, and you're not sure if it's "cheating" to use it.

This is exactly what the study "AI in Work-Based Learning" by Miranda and his team is all about. They went into the Philippines and asked 384 student interns: "Are you using AI tools like ChatGPT during your real-world job training, and how does it feel?"

Here is the story of their findings, broken down into simple, everyday concepts.

1. The "Digital Toolbox" (What Tools Are They Using?)

Think of AI tools as different tools in a mechanic's toolbox. The study found that almost every intern had a toolbox, but they all reached for the same "hammer" first.

The Star Player: ChatGPT was the most popular tool (used by nearly half the students). It's like the Swiss Army Knife of the group—good for almost everything.
The Specialists:
- Quillbot & Grammarly: These are the "polishers." They smooth out rough sentences and fix grammar, like a spell-checker on steroids.
- Canva AI: This is the "artist," helping students make pretty slides and posters without needing to be a graphic designer.
The Reality Check: Most students (90%) were using the free versions of these tools. They weren't paying for the "premium" features; they were making do with what was available, much like using a basic map instead of a GPS subscription.

2. The "How-To" Guide (How Do They Use It?)

The researchers asked, "What are you actually doing with these tools?" They found four main ways students used their AI assistants:

The Productivity Booster: When the pile of paperwork is huge, AI helps write reports or polish them up so they look professional. It's like having a co-pilot to help you navigate a stormy sea of data.
The Communication Coach: Interns used AI to draft emails or letters. It's like having a wise friend whisper, "Hey, maybe don't say it that way; try this instead," so you don't accidentally offend your boss.
The Tech Tutor: For students in IT or engineering, AI helped with coding or technical problems. It's like having a senior engineer standing over your shoulder, ready to explain a complex concept instantly.
The "Figure-It-Out" Helper: Sometimes, the instructions from a boss are vague. Students used AI to decode what they were actually supposed to do. It's like using a flashlight in a dark room to find the light switch.

The Pattern: Interestingly, students used these tools mostly for language and information (fixing grammar, finding examples). They used them less for creative design or complex coding. It seems they trusted AI to handle the "boring stuff" but wanted to keep the "creative stuff" for themselves.

3. The "Ethical Tightrope" (Do They Feel Guilty?)

This is the most interesting part. The study found that students are walking a very careful tightrope.

Confident but Cautious: The students felt pretty confident using these tools (about a 3.8 out of 5). They knew AI made them faster and more accurate.
The "Independent Effort" Rule: However, they had a strong moral compass. When a task required them to show what they knew personally (like a test or a specific creative challenge), they put the AI down. They treated AI like a calculator: great for math, but you wouldn't use it to prove you know how to do long division in your head.
The Boss's Reaction: Surprisingly, most supervisors didn't stop them. It was like a parent letting a teenager use a car, as long as they drove safely. The students felt they could use AI, but they had to be smart about when to use it.

4. The Big Takeaway (What Does This Mean for Us?)

The study concludes that AI isn't going to replace these interns; it's becoming their training wheels.

The Good: AI helps students get work done faster and learn how to communicate better. It lets them focus on the big picture instead of getting stuck on small details.
The Risk: If they rely on AI too much, they might forget how to do the work themselves. It's like if you always let your GPS drive you, you might forget how to read a map.
The Solution: Schools and companies need to stop pretending AI doesn't exist. Instead, they should teach students how to use it responsibly.
- Imagine a cooking class: Instead of banning the food processor, the teacher teaches you when to use it and when to chop by hand so you don't lose your knife skills.

In a Nutshell

This paper tells us that student interns in the Philippines are already using AI to survive and thrive in their jobs. They aren't using it to cheat; they are using it as a smart assistant to get the job done.

The future isn't about banning AI in the workplace; it's about teaching the next generation of workers how to be the captain of the ship, with AI as their powerful engine. If schools and bosses give them clear rules and good training, these interns will be ready for the future of work, armed with both their own brains and the best tools technology has to offer.

1. Problem Statement

While Artificial Intelligence (AI) tools are increasingly integrated into academic settings, their application during On-the-Job Training (OJT)—a critical component of Philippine higher education—remains underexplored. Existing literature focuses heavily on classroom learning or highly specialized technical simulations (e.g., medical or engineering scenarios). There is a significant gap in understanding:

How student interns in the Philippines actually utilize AI tools in real-world workplace environments.
The specific purposes for which these tools are employed across various task categories.
The perceived effects of AI usage on student confidence, ethical decision-making, and supervisory support.
Whether current OJT programs adequately prepare students for the ethical and practical integration of AI in the workforce.

2. Methodology

The study employed a descriptive-exploratory research design utilizing a quantitative survey approach.

Participants: A total of 384 undergraduate student interns from various public and private higher education institutions in the Philippines. Participants were selected via purposive sampling, limited to those currently engaged in or recently completed OJT who had experience using intelligent tools.
Instrument: A structured, self-administered online questionnaire comprising five sections:
1. Demographics: Age, sex, academic program, OJT mode (on-site/online/hybrid), and device usage.
2. Tool Usage Behavior: Types of tools used, frequency, and subscription status (free vs. paid).
3. Task-Based Usage: Rated on a 5-point Likert scale (1 = Never, 5 = Very Often) covering tasks like report writing, grammar correction, coding, and communication.
4. Perceived Effects: Assessments of confidence, skill adequacy, ethical concerns, and supervisory support.
5. Guidance & Attitudes: Behaviors regarding independent effort and selective usage.
Data Analysis:
- Descriptive Statistics: Frequency counts, percentages, and means to profile respondents and usage patterns.
- Factor Analysis: Principal Component Extraction with Varimax rotation was used to identify underlying dimensions of AI usage purposes. Items with factor loadings $\ge$ 0.50 were retained.
- Reliability: Cronbach's alpha was computed for all constructs, with all values exceeding the 0.70 threshold, confirming internal consistency.
- Software: IBM SPSS v.25.

3. Key Contributions

The study makes several distinct contributions to the field of AI in Education (AIED) and Work-Based Learning:

Contextual Specificity: It provides the first comprehensive data on AI usage specifically within the Philippine OJT context, bridging the gap between academic AI use and professional workplace application.
Taxonomy of Usage: It categorizes AI tool usage into four distinct functional purposes (Productivity, Communication, Technical Support, and Autonomous Execution), moving beyond a generic "usage" metric.
Ethical & Behavioral Insight: It highlights the "cautious but confident" approach of interns, revealing that students often self-regulate AI use based on task requirements (e.g., avoiding AI for independent tasks), a nuance often missed in broader adoption studies.
Policy Framework: It offers evidence-based recommendations for integrating AI literacy into OJT curricula and establishing workplace guidelines for responsible AI use.

4. Key Results

A. Demographics and Tool Prevalence

Demographics: The sample was nearly balanced (197 male, 187 female), with the majority aged 22. Most (90.7%) completed OJT on-site.
Dominant Tools: ChatGPT was the most widely used tool (48%), followed by Quillbot (16.9%), Canva AI (15.2%), and Grammarly (10.5%).
Access: 90.1% of users relied on free versions; only 9.9% had premium subscriptions.
Frequency: 45.3% used AI 1–2 times per week; 34.4% reported no AI usage during OJT.

B. Task-Based Usage Patterns

Students primarily used AI for language-related and information-seeking tasks:

Highest Usage: Fixing grammar/sentence structure ( $M=2.80$ ), searching for examples ( $M=2.82$ ), and understanding work instructions ( $M=2.79$ ).
Lower Usage: Technical tasks like coding ( $M=2.70$ ) and creative tasks like design ( $M=2.59$ ) or report polishing ( $M=2.56$ ).
Consistency: Usage patterns remained consistent whether tasks were performed during or outside OJT hours.

C. Factor Analysis (Four Purposes)

Exploratory factor analysis identified four main dimensions of AI utility:

General Productivity and Report Writing: Focus on polishing reports, grammar, and understanding instructions.
Communication and Content Drafting: Drafting emails, creating slides/posters, and finding sample answers.
Technical Assistance and Code Support: Specific to coding and technical troubleshooting.
Autonomous Task Execution: Using AI to understand instructions and complete tasks independently.

D. Perceived Effects

Confidence: Students reported moderate-to-high confidence ( $M=3.81$ ) and agreed that AI helped improve speed and accuracy ( $M=3.76$ ).
Ethical Behavior: Students showed ethical discernment, moderately agreeing that they avoided AI when independent effort was required ( $M=3.14$ ).
Supervisory Environment: Most interns felt supervisors did not prohibit AI use ( $M=3.60$ ), and they applied tools selectively ( $M=3.42$ ).

5. Significance and Implications

The study concludes that AI tools are currently acting as supportive assistants rather than replacements for student interns in the Philippines. However, the reliance on free tools and the lack of structured guidance pose risks to skill development and ethical consistency.

Educational Reform: Higher Education Institutions (HEIs) must integrate AI literacy and onboarding into OJT programs. This includes training students not just on how to use tools, but on when to use them ethically.
Workplace Policy: Clear, task-specific guidelines are needed for supervisors and interns to define acceptable AI use (e.g., AI for drafting vs. human verification for final output).
Equity: There is a need to address the digital divide, ensuring interns in rural or resource-limited settings have equitable access to AI resources.
Future Research: The authors call for longitudinal studies to measure the impact of AI usage on long-term skill acquisition and workplace readiness, as well as qualitative research to understand the decision-making processes behind AI adoption in different industries.

In summary, the paper argues that while AI is effectively enhancing productivity and confidence among Philippine interns, a structured framework for responsible AI integration is essential to ensure these tools foster, rather than hinder, professional competence.