📄 social_science

The Influence of ChatGPT Usage, Learning Content, and Digital Competence on Biology Students’ Learning Motivation in the Digital Education Era: A Moderated Effect Analysis Model

This study utilizes Structural Equation Modeling on 366 biology students to demonstrate that while ChatGPT usage and learning content positively influence learning motivation, digital competence acts as a critical moderator that enhances the effectiveness of content and determines the extent to which ChatGPT contributes to student engagement.

Original authors: Ibrahim Ibrahim, Juli Firmansyah, Jalaluddin Jalaluddin, Marwan Marwan

Published 2026-08-07

📖 6 min read🧠 Deep dive

CC BY 4.0

Original authors: Ibrahim Ibrahim, Juli Firmansyah, Jalaluddin Jalaluddin, Marwan Marwan

Original paper licensed under CC BY 4.0 (https://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 the classroom of the future not as a room with chalkboards, but as a vast, digital playground where learning happens through conversation. In this world, there are three main characters trying to figure out how to get students excited about learning. First, there's ChatGPT, a super-smart, tireless AI tutor that can chat with you anytime, answer your questions instantly, and help you untangle tricky concepts. Think of it like a magical encyclopedia that talks back. Second, there's Learning Content, which is the actual stuff you study—the textbooks, videos, and assignments. If this content is boring, even the best AI can't save the day; but if it's fun and relevant, it acts like a rocket booster for your brain. Finally, there's Digital Competence, which is simply how good you are at using technology. It's the difference between someone who can just turn on a computer and someone who can use it to build a spaceship.

For a long time, educators have wondered: Does having a cool AI tutor and good lessons automatically make students want to learn more? Or does it matter how "tech-savvy" the student is? This question is crucial because schools are rushing to add AI tools, but they don't want to waste time on gadgets that don't actually spark a student's curiosity. This study dives into that exact mystery, looking at how these three ingredients mix together to create (or fail to create) learning motivation among university students in Indonesia.

The Study: Mixing AI, Lessons, and Tech Skills

A team of researchers decided to test this recipe by asking 366 university students in Indonesia about their habits. They wanted to see if using ChatGPT, the quality of the lessons, and the students' own tech skills could predict how motivated those students felt about their studies. They used a fancy statistical tool called "Structural Equation Modeling" (think of it as a super-accurate map that shows how one thing pushes another) to crunch the numbers.

Here is what they found, served up with a few analogies to keep things clear:

1. The AI Tutor is a Motivator, Not a Skill-Builder
The study found that using ChatGPT is a huge boost for learning motivation. When students used the AI, they felt more eager and engaged. It's like having a personal cheerleader who is always available to explain a hard concept or help with homework; it makes the whole process feel less lonely and more fun.

However, here is the twist: using ChatGPT did not automatically make students better at using technology in general. The researchers found only a very weak link between chatting with the AI and actually improving "digital competence." It's a bit like playing a video game: you might get really good at that specific game, but it doesn't necessarily mean you've learned how to fix a car or build a computer. Just talking to the AI doesn't magically turn a beginner into a tech wizard unless they are guided to do more than just chat.

2. Good Lessons Are the Real Superpower
The most powerful ingredient in the mix was Learning Content. The study showed that high-quality, interesting, and well-structured lessons had a massive impact on both motivation and digital skills. If the material is good, students get excited, and they also get better at using the tools to explore it. It's like giving someone a delicious, nutritious meal; they get energy (motivation) and the nutrients they need to grow (skills). The researchers found that good content is the strongest driver of all.

3. The "Tech-Savvy" Factor: A Double-Edged Sword
This is where it gets really interesting. The researchers looked at Digital Competence (how tech-savvy the students were) to see if it changed how the other factors worked. They discovered that being good with technology acts like a filter or a lens.

For Good Lessons: Being tech-savvy made the lessons work even better. Students who already knew how to use digital tools were able to squeeze more juice out of high-quality content, making them even more motivated. It's like having a high-performance engine in a sports car; if you have the skills to drive it, the car goes faster.
For ChatGPT: Surprisingly, being very tech-savvy actually weakened the boost that ChatGPT gave to motivation. The researchers suggest that students who are already very comfortable with technology might not need the AI as much to feel excited. They might already have their own ways of staying motivated, so the AI doesn't add as much extra spark for them as it does for students who are less confident with tech.

4. The Big Surprise: Skills Don't Equal Motivation
One of the most important things the study ruled out is the idea that "if you are good with computers, you will love learning." The data showed no statistically significant link between digital competence and learning motivation. In plain terms, the statistical test (with a p-value of 0.843) confirmed that simply being a tech whiz does not directly translate into a higher desire to study. Motivation comes from the experience of learning (the AI help and the good lessons), not just from having the skills to use the tools.

The Bottom Line

The study concludes that while AI tools like ChatGPT are fantastic for getting students excited, they aren't a magic wand for teaching tech skills. To truly light a student's fire, you need great content first. And while being good with technology helps students get the most out of good lessons, it doesn't guarantee they will be motivated to learn in the first place.

The researchers are careful to say that these findings are based on a specific group of students and a snapshot in time, so we can't say for sure that this applies to every single student everywhere. But the message is clear: Don't just hand students a cool AI tool and hope for the best. Give them great lessons, help them understand how to use the tools deeply, and remember that being a tech expert doesn't automatically make someone a motivated learner. The magic happens when the right tools meet the right content.

Technical Summary: The Influence of ChatGPT Usage, Learning Content, and Digital Competence on Biology Students' Learning Motivation

Note on Scope: While the title references "Biology Students," the study's methodology and results sections specify that the population consisted of undergraduate students from the Faculty of Teacher Training and Education at Universitas Syiah Kuala, covering majors in Physical Education, English Education, Psychology, and Mathematics. The following summary reflects the actual data and scope presented in the paper.

1. Problem Statement and Research Objectives

The study addresses a gap in existing literature regarding the empirical impact of Generative AI (specifically ChatGPT) on student learning motivation. While prior research has described the capabilities of AI tools, there is limited understanding of how specific usage patterns, the quality of learning content, and students' digital competence interact to influence motivation. Furthermore, the role of digital competence as a moderating variable in these relationships remains under-investigated.

The research aims to:

Assess the impact of ChatGPT usage and learning content on students' digital competence and learning motivation.
Determine if digital competence influences learning motivation directly.
Investigate whether digital competence moderates the effects of ChatGPT usage and learning content on learning motivation.

2. Methodology

Research Design:
The study employs a quantitative research design utilizing Structural Equation Modeling with Partial Least Squares (SEM-PLS) via SmartPLS software. This approach was selected to handle complex causal relationships and moderating effects within a moderate sample size.

Population and Sampling:

Population: Undergraduate students (semesters 4–8) at the Faculty of Teacher Training and Education, Universitas Syiah Kuala.
Sample: 366 respondents selected via stratified random sampling to ensure proportional representation across gender, academic year, and study majors (Physical Education, English Education, Psychology, and Mathematics).
Demographics: The sample included 55% female and 45% male respondents, with the majority (50%) aged 21–23.

Instrumentation:
Data were collected using a structured questionnaire with items measured on a 5-point Likert scale. The instrument assessed four latent constructs:

ChatGPT Usage (X1): Frequency, duration, and manner of use.
Learning Content (X2): Perceived relevance, quality, and interaction intensity.
Digital Competence (X3): Technical skills, confidence, and adaptability.
Learning Motivation (X4): Intrinsic and extrinsic motivational drivers.

Validity and Reliability:
The measurement model demonstrated robust validity and reliability. All factor loadings exceeded 0.70 (except one at 0.608, which was retained based on overall construct validity). Cronbach's Alpha values ranged from 0.739 to 0.888, Composite Reliability (CR) values exceeded 0.834, and Average Variance Extracted (AVE) values were above the 0.50 threshold for all constructs. Discriminant validity was confirmed using the Fornell-Larcker criterion.

Bias Control:
Procedural remedies (anonymity, clear item wording) and statistical tests (Harman's single-factor test) were employed to mitigate common method bias.

3. Key Results

The structural model analysis yielded the following findings:

Direct Effects:

ChatGPT Usage $\rightarrow$ Learning Motivation: A strong, highly significant positive effect ( $\beta = 0.422, p < 0.001$ ). Frequent use of ChatGPT significantly enhances student motivation.
Learning Content $\rightarrow$ Learning Motivation: The strongest direct effect in the model ( $\beta = 0.550, p < 0.001$ ). High-quality, relevant, and interactive content is the primary driver of motivation.
Learning Content $\rightarrow$ Digital Competence: A significant positive effect ( $\beta = 0.171, p < 0.05$ ). Engaging content contributes to the development of digital skills.
ChatGPT Usage $\rightarrow$ Digital Competence: A marginally significant positive effect ( $\beta = 0.146, p = 0.067$ ). While ChatGPT usage tends to improve digital competence, the effect is relatively weak.
Digital Competence $\rightarrow$ Learning Motivation: Not Supported. The relationship was not statistically significant ( $\beta = -0.005, p = 0.843$ ). Possessing digital skills alone does not directly translate to higher learning motivation.

Moderating Effects:

Digital Competence moderating ChatGPT Usage $\rightarrow$ Motivation: A significant negative moderating effect ( $\beta = -0.091, p = 0.032$ ). Higher digital competence weakens the positive relationship between ChatGPT usage and motivation. The authors interpret this as a compensatory effect: students with high digital skills may rely less on ChatGPT for motivation, as they possess other self-regulated learning capabilities.
Digital Competence moderating Learning Content $\rightarrow$ Motivation: A significant positive moderating effect ( $\beta = 0.132, p = 0.024$ ). Higher digital competence strengthens the relationship between learning content and motivation. Students with better digital skills are more capable of leveraging high-quality content to boost their motivation.

Model Fit:

The model explains 81.3% of the variance in Learning Motivation ( $R^2 = 0.813$ ), indicating high predictive power.
The model explains only 8.7% of the variance in Digital Competence ( $R^2 = 0.087$ ), suggesting that other factors (e.g., prior experience, infrastructure) play a larger role in determining digital competence than the variables included in this study.

4. Key Contributions and Claims

The paper claims the following contributions to the field of digital education:

Role of AI in Motivation: The study empirically confirms that ChatGPT is a significant driver of learning motivation, likely due to its ability to provide immediate, personalized feedback and support autonomy.
Primacy of Content Quality: The research highlights that while AI tools are beneficial, the quality and design of learning content remain the most critical factors for student engagement and motivation.
Nuanced Role of Digital Competence:
- The study challenges the assumption that digital skills automatically lead to higher motivation, finding no direct significant link.
- It establishes digital competence as a critical moderator rather than a direct predictor. It acts as a "filter" or "amplifier": it enhances the benefits of good content but may diminish the reliance on AI tools for motivation among highly skilled students.
Limitations of AI for Skill Development: The marginal significance of ChatGPT on digital competence suggests that mere exposure to AI is insufficient for comprehensive digital skill development; guided use and broader digital literacy strategies are required.

5. Significance and Implications

The authors conclude that the integration of AI tools like ChatGPT is effective for enhancing student motivation, but this effectiveness is conditional. The study emphasizes that:

Pedagogical Design Matters: Technology must be integrated with high-quality, interactive learning content to maximize motivational outcomes.
Skill vs. Motivation: Improving digital competence is necessary for accessing tools, but it is not a substitute for motivational drivers. Motivation is more strongly influenced by the learning environment and content relevance than by technical proficiency alone.
Contextual Dependency: The impact of technology varies based on the student's digital competence level. Educators should recognize that highly competent students may derive different motivational benefits from AI tools compared to those with lower competence.

The paper concludes that future educational strategies should focus on the synergistic integration of high-quality content, strategic AI use, and the development of digital competence, rather than relying on technology as a standalone solution for motivation.