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Exploring the Adoption Intention in Using AI-Enabled Educational Tools Among Preservice Teachers in the Philippines: A Partial-Least Square Modeling

This study utilizes PLS-SEM to analyze data from 563 Filipino pre-service teachers, revealing that internal motivational and emotional factors, particularly performance expectancy and hedonic motivation, are the primary drivers of their intention to adopt AI-enabled educational tools, outweighing the influence of external institutional factors.

Original authors: Vanessa B. Sibug, Emerson Q. Fernando, Almer B. Gamboa, Roque Francis B. Dianelo, Agnes R. Regala, Joseph Alexander Bansil, Jan Henry B. Sunga, Vernon Grace M. Maniago, John Paul P. Miranda

Published 2026-05-01

📖 4 min read☕ Coffee break read

CC BY 4.0

Original authors: Vanessa B. Sibug, Emerson Q. Fernando, Almer B. Gamboa, Roque Francis B. Dianelo, Agnes R. Regala, Joseph Alexander Bansil, Jan Henry B. Sunga, Vernon Grace M. Maniago, John Paul P. Miranda

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 are training to become a teacher. You are about to enter a classroom for your first real practice (your "practicum"), and you have a new, powerful toolbox in front of you: AI educational tools. These are smart programs that can help you make lesson plans, grade papers, or create fun quizzes.

But here's the big question: Will you actually use them?

This paper is like a detective story that investigates why some future teachers in the Philippines are excited to use these AI tools, while others might hesitate. The researchers asked 563 student teachers to fill out a survey and then used a special mathematical model (a "recipe" for understanding human behavior) to figure out what drives their decisions.

Here is what they found, explained simply:

1. The Two Main Engines: "Will it help me?" and "Is it fun?"

The study found that two things are the biggest drivers for using AI, acting like the two engines on a rocket ship:

Performance Expectancy (The "Superpower" Factor): This is the belief that the tool will actually make the teacher's job easier or better. If a student teacher thinks, "This AI will help me grade faster and make my lessons look amazing," they are very likely to use it. It's like buying a high-tech vacuum cleaner because you know it will clean your house better than a broom.
Hedonic Motivation (The "Joy" Factor): This is simply about having fun. If the tool feels playful, entertaining, or enjoyable to use, teachers want to use it. It's the difference between doing a chore and playing a video game. If the AI feels like a fun toy rather than a boring assignment, adoption goes up.

2. The "Ease of Use" Misconception

You might think, "If a tool is easy to use, people will definitely use it." The study found this isn't exactly true for these teachers.

The Finding: How "easy" a tool is (Effort Expectancy) did not directly make teachers want to use it.
The Twist: However, "ease of use" is heavily influenced by three internal feelings:
- Confidence: "Do I believe I can handle this?" (Computer Self-Efficacy).
- Fear: "Am I scared I'll break it?" (Computer Anxiety).
- Playfulness: "Does this feel like a game?" (Computer Playfulness).
The Analogy: Think of a new video game. Even if the controls are simple (easy to use), you won't play it if you are terrified of the buttons or feel stupid trying to learn. But if you feel confident and think the game is fun, you'll want to play, even if it's a little tricky.

3. The Surprising "Peer Pressure" Effect

Usually, we think if our friends or bosses tell us to do something, we will do it. The study found the opposite for AI.

The Finding: Social Influence (what peers or supervisors say) actually had a negative effect.
The Analogy: Imagine a teacher is told, "You must use this AI because the principal said so." Instead of feeling motivated, they might feel forced or rebellious. It's like being told to eat a specific vegetable by a parent; sometimes, the pressure makes you less likely to want to eat it because you feel your freedom is being taken away. They want to use the tools because they want to, not because they were told to.

4. What Didn't Matter Much

The study looked at other factors, but they weren't the main drivers:

Institutional Support: Having good internet or computers (Facilitating Conditions) didn't automatically make teachers want to use AI. You can have a Ferrari in the garage, but if you don't want to drive it, it just sits there.
Price: Since most of these tools are free or low-cost, the "value for money" wasn't a huge deciding factor.
Habit: Just because they used technology before didn't guarantee they would use AI specifically.

The Bottom Line

The paper concludes that to get future teachers to use AI, we shouldn't just build better computers or force them to use it. Instead, we need to focus on the inside of the teacher:

Show them the magic: Prove that AI actually helps them do their job better (Performance).
Make it fun: Ensure the tools are engaging and enjoyable (Hedonic).
Build confidence: Help them feel capable and reduce their fear of technology.
Stop the pressure: Let them choose to use it because they see the value, not because a boss told them to.

In short, for these future teachers, internal motivation is the key, not external rules or infrastructure.

1. Problem Statement

The study addresses a critical gap in teacher education: understanding the specific factors influencing pre-service teachers' behavioral intention to adopt AI-enabled educational tools (ETTs) during their practicum (student teaching) phase. While educational technology is increasingly integrated into classrooms, effective adoption depends not just on infrastructure but on the psychological and contextual factors driving future educators.

Existing literature often lacks specific insights into how pre-service teachers navigate AI-specific tools (e.g., generative AI for lesson planning, automated grading systems) within the unique constraints of a practicum. The authors aim to identify whether internal motivational/cognitive factors or external institutional factors are more predictive of adoption intention in the Philippine context.

2. Methodology

Research Design and Participants

Design: Descriptive cross-sectional study.
Participants: 563 pre-service teachers from teacher education institutions in Central Luzon, Philippines.
Demographics: Predominantly female (71.4%), mean age 21.06. Specializations included Physical Education (50%), Elementary (24.5%), and Secondary (25.4%).
Context: Participants were currently enrolled in practicum courses. The study contextualized AI tools to include platforms like Canva, Slidesgo, Quillbot, ChatGPT, Gemini, Copilot, Quizizz, Kahoot!, and Gradescope.

Theoretical Framework

The study utilizes an extended version of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2).

Core Constructs: Performance Expectancy (PE), Effort Expectancy (EE), Hedonic Motivation (HM), Social Influence (SI), Facilitating Conditions (FC), Price Value (PV), and Habit (HB).
Extended Constructs: The model integrates Computer Self-Efficacy (CSE), Computer Anxiety (CA), and Computer Playfulness (CP) as antecedents to EE and HM.
Additional Predictors for PE: Extrinsic Motivation (EM), Job Fit (JF), Relative Advantage (RA), and Outcome Expectations (OE).

Instrument and Analysis

Data Collection: Online survey (Google Forms) using a 4-point Likert scale.
Analysis Technique: Partial Least Squares Structural Equation Modeling (PLS-SEM).
Validation:
- Reliability: Cronbach's alpha and Composite Reliability (CR) exceeded 0.70.
- Validity: Convergent validity confirmed via Factor Loadings (>0.70) and Average Variance Extracted (AVE > 0.50). Discriminant validity confirmed via the Fornell-Larcker criterion.
Hypotheses: 12 primary hypotheses were tested regarding direct and indirect relationships between the constructs and Behavioral Intention to Use (BITU).

3. Key Results

The PLS-SEM analysis yielded the following significant findings:

A. Direct Predictors of Behavioral Intention (BITU)

Performance Expectancy (PE): The strongest positive predictor ( $\beta = 0.302, p < .001$ ). Teachers who believe AI improves their teaching performance are more likely to adopt it.
Hedonic Motivation (HM): The second strongest positive predictor ( $\beta = 0.292, p < .001$ ). Enjoyment derived from using the tools significantly drives adoption.
Effort Expectancy (EE): Not significant ( $\beta = -0.013, p = .830$ ). Perceived ease of use did not directly influence the intention to use AI tools in this context.

B. Antecedents of Effort Expectancy (EE)

Although EE did not predict BITU directly, it was significantly shaped by:

Computer Self-Efficacy (CSE): Positive influence ( $\beta = 0.469$ ).
Computer Playfulness (CP): Positive influence ( $\beta = 0.387$ ).
Computer Anxiety (CA): Surprisingly, a small but significant positive influence ( $\beta = 0.059$ ), suggesting that higher anxiety was associated with higher perceived effort (or the relationship is complex in this specific sample).

C. Antecedents of Performance Expectancy (PE)

PE was significantly predicted by:

Extrinsic Motivation (EM): Strongest driver ( $\beta = 0.487$ ).
Outcome Expectations (OE): ( $\beta = 0.236$ ).
Job Fit (JF): ( $\beta = 0.162$ ).
Relative Advantage (RA): ( $\beta = 0.071$ ).

D. Contextual and Habitual Factors (Non-Significant or Negative)

Social Influence (SI): Showed a significant negative effect ( $\beta = -0.096, p = .014$ ). External pressure from peers or supervisors actually reduced the intention to use AI tools, likely due to perceived threats to autonomy.
Facilitating Conditions (FC): No significant effect ( $\beta = -0.029$ ). Institutional support alone does not drive adoption.
Price Value (PV) & Habit (HB): Showed only marginal effects ( $p > 0.05$ ), indicating cost and prior routine are not primary drivers for this demographic.

4. Key Contributions

Refinement of UTAUT2 in AI Context: The study validates the UTAUT2 framework for AI adoption but highlights a deviation: Effort Expectancy is not a direct driver of intention in this specific context, challenging standard assumptions where ease of use is paramount.
Identification of the "Social Influence Paradox": The finding that Social Influence has a negative impact is a critical contribution. It suggests that for pre-service teachers, top-down or peer-pressure mandates may hinder voluntary adoption of AI, emphasizing the need for autonomy.
Psychological vs. Institutional Drivers: The research provides empirical evidence that internal factors (motivation, enjoyment, self-efficacy) significantly outweigh external factors (infrastructure, institutional mandates, cost) in the adoption of AI by future teachers.
Contextual Specificity: It offers the first large-scale PLS-SEM analysis of AI adoption specifically among Filipino pre-service teachers during their practicum, a demographic often overlooked in global AI education studies.

5. Significance and Implications

For Teacher Education Programs:
- Curricula should shift focus from merely providing infrastructure (Facilitating Conditions) to fostering intrinsic motivation and enjoyment.
- Training should emphasize personal relevance (Job Fit) and outcome expectations to boost Performance Expectancy.
- Programs must address Computer Anxiety and build Self-Efficacy through hands-on, low-stakes exploration rather than mandatory mandates.
For Policy and Administration:
- Administrators should avoid coercive "social influence" tactics. Adoption strategies should be voluntary and self-directed.
- Investment in AI tools should prioritize features that offer relative advantage and extrinsic motivation (e.g., saving time, improving grades) rather than just ease of use.
Theoretical Impact: The study suggests that in the context of emerging AI technologies, the "fun" factor (Hedonic Motivation) and the belief in utility (Performance Expectancy) are the primary gatekeepers for adoption, while traditional barriers like "ease of use" are secondary once basic competence is established.

In conclusion, the paper argues that successful AI integration in teacher education relies less on building better infrastructure and more on cultivating a psychological environment where future teachers feel confident, autonomous, and personally rewarded by using these tools.