Tuesday, March 3, 2026
Behind My Back
Behind My Back
Tak kusangka—
the smile was only borrowed light,
while in the shadows
my name was being broken
into cruel syllables.
I stood there,
thinking we were building trust,
while you were building stories—
sharp, glittering lies
thrown quietly at my back.
It hurts—
not because the words are heavy,
but because they came
from a mouth
that once called me friend.
If there is justice in the turning sky,
let karma walk its patient road.
Let every whisper return
to the lips that shaped it.
Let truth rise
like morning—
inevitable and bright.
As for me—
may I be guarded
by unseen hands of mercy.
May every evil intention
dissolve before it reaches my door.
May the traps set in darkness
collapse under their own weight.
O Protector of wounded hearts,
distance me from cruel souls,
from smiling daggers,
from honeyed poison.
If they choose shadows,
let me choose light.
If they throw stones,
let me grow wings.
And one day—
when the wind carries my name again,
may it carry it clean,
untouched,
and stronger than before.
TAM, TTF, and UTAUT
Here is a clear and academic comparison of TAM, TTF, and UTAUT, commonly used in technology adoption research (very relevant to your AI-in-education studies).
1️⃣ Technology Acceptance Model (TAM)
Developed by Fred Davis (1989)
🔹 Core Idea
Users adopt technology if they believe it is:
- Perceived Usefulness (PU) → Improves performance
- Perceived Ease of Use (PEOU) → Easy to use
🔹 Basic Model
PEOU → PU → Attitude → Behavioral Intention → Actual Use
🔹 Strengths
- Simple and easy to apply
- Strong predictive power
- Widely used in educational technology research
🔹 Weaknesses
- Focuses mainly on individual perception
- Ignores social and organizational factors
🔹 Suitable When:
You want to examine students’ or teachers’ acceptance of AI tools, such as AI-supported IBL applications in EFL writing.
2️⃣ Task-Technology Fit (TTF)
Developed by Dale Goodhue & Ronald Thompson (1995)
🔹 Core Idea
Technology will be used and improve performance only if it fits the task.
🔹 Key Components
- Task Characteristics
- Technology Characteristics
- Task-Technology Fit
- Performance Impact
🔹 Focus
👉 Not just acceptance, but whether the technology actually supports the task effectively.
🔹 Strengths
- Strong for performance-based studies
- Suitable for evaluating AI tools in writing, research, or problem-solving tasks
🔹 Weaknesses
- Less emphasis on psychological intention
- More complex measurement
🔹 Suitable When:
You want to analyze whether AI-supported IBL actually fits EFL writing tasks, not just whether students like it.
3️⃣ Unified Theory of Acceptance and Use of Technology (UTAUT)
Developed by Viswanath Venkatesh et al. (2003)
🔹 Core Idea
Integrates 8 previous models (including TAM).
🔹 Four Main Constructs:
- Performance Expectancy
- Effort Expectancy
- Social Influence
- Facilitating Conditions
Moderated by:
- Age
- Gender
- Experience
- Voluntariness
🔹 Strengths
- More comprehensive than TAM
- Includes social and institutional factors
- Strong predictive power
🔹 Weaknesses
- More complex model
- Requires larger sample size
🔹 Suitable When:
You want to study AI adoption in institutional settings, including peer influence, infrastructure, and policy support.
🔎 Side-by-Side Comparison
Aspect | TAM | TTF | UTAUT |
Focus | User perception | Task-technology match | Integrated acceptance model |
Main Question | Do users accept it? | Does it fit the task? | What influences acceptance & use? |
Complexity | Simple | Moderate | Complex |
Best for | Attitude & intention | Performance & effectiveness | Institutional adoption |
🎓 For AI-Supported IBL Research in EFL Writing
If your focus is:
- Students’ intention to use AI tools → TAM
- Whether AI improves writing performance through task alignment → TTF
- Institutional and social adoption factors → UTAUT
- Most comprehensive approach → Combine TAM + TTF or use UTAUT
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