design assessments in the age of AI
What "Terms of Trade" Actually Is Terms of trade (ToT) quantify the value of a nation's exports in relation to its imports. Simply put, it is the rate at which you exchange what you sell to the world for what you purchase from it. Terms of Trade Export Prices Import Prices Terms of Trade Import PrRead more
What “Terms of Trade” Actually Is
- Export Prices
- Import Prices
- Terms of Trade
- Import Prices
- Export Prices
The Theory: The “Optimal Tariff” Argument
- Assume your nation is big enough in global trade to make a difference in world prices (such as the U.S., EU, or China).
- You put a tariff on imports — 10%, for example.
- Foreign exporters have increased obstacles to selling into your market.
- To maintain their commodities competitive, they may reduce their export prices.
Your terms of trade are better.
Why It Only Works for “Large” Economies
- A small economy (such as Nepal or Costa Rica) can’t; world prices are determined by much bigger markets. Any tariff it levies simply increases local prices and penalizes its own citizens.
- A big economy (such as the U.S., China, or the EU) can shape world demand sufficiently that foreign producers may pass on some of the tariff by reducing prices.
That’s why this concept is referred to as the “optimal tariff” — it’s the tariff that optimizes the welfare of a country by enhancing its terms of trade just sufficient to cover the loss of efficiency from restricting trade.
But There’s a Catch: Retaliation
- This reprisal negates any initial gain due to improved terms of trade and usually leads to a trade war, lowering world welfare for all.
- Throughout the U.S.–China trade war (2018–2020), both countries applied tariffs to shield their own industries and enhance bargaining leverage.
- Rather than enhancing terms of trade, both countries incurred greater import prices, dislocated supply chains, and reduced growth.
- Economists subsequently calculated the alleged “gains” from better trade terms as entirely offset by losses to consumers and exporters.
Contemporary Complexity: Global Value Chains
- Years ago, nations primarily exchanged finished goods: one country sold cars, another textiles. Nowadays, production is splintered across borders — a product can travel 5–6 countries before it is delivered to consumers.
- Placing a tariff on “imports” usually means levying taxes on components and materials your industries require. That increases costs for manufacturers at home, undermines exports, and can deteriorate your terms of trade instead of enhancing them.
The Human Angle: Winners and Losers
- Consumers pay more — they lose purchasing power.
- Protected industries win in the short term, with less foreign competition.
- Exporters usually lose when trading nations retaliate.
How to Design Tests in the Age of AI In this era of learning, everything has changed — not only the manner in which students learn but also the manner in which they prove that they have learned. Students today employ tools such as ChatGPT, Grammarly, or math solution AI tools as an integral part ofRead more
How to Design Tests in the Age of AI
In this era of learning, everything has changed — not only the manner in which students learn but also the manner in which they prove that they have learned. Students today employ tools such as ChatGPT, Grammarly, or math solution AI tools as an integral part of their daily chores. While technology enables learning, it also renders the conventional models of assessment through memorization, essays, or homework monotonous.
So the challenge that educators today are facing is:
How do we create fair, substantial, and authentic tests in a world where AI can spew up “perfect” answers in seconds?
The solution isn’t to prohibit AI — it’s to redefine the assessment process itself. Let’s start on how.
1. Redefining What We’re Assessing
For generations, education has questioned students about what they know — formulas, facts, definitions. But machines can memorize anything at the blink of an eye, so tests based on memorization are becoming increasingly irrelevant.
In the AI era, we must test what AI does not do well:
Attempt replacing the following questions: Rather than asking “Explain causes of World War I,” ask “If AI composed an essay on WWI causes, how would you analyze its argument or position?”
This shifts the attention away from memorization.
2. Creating “AI-Resilient” Tests
An AI-resilient assessment is one where even if a student uses AI, the tool can’t fully answer the question — because the task requires human judgment, personal context, or live reasoning.
Here are a few effective formats:
Have students record how they utilized AI tools ethically (e.g., “I used AI to grammar-check but wrote the analysis myself”).
Choose students for the competition based on how many tasks they have been able to accomplish.
Example: “You are an instructor in a heterogeneously structured class. How do you use AI in helping learners of various backgrounds without infusing bias?”
Thinking activities:
Instruct students to compare or criticize AI responses with their own ideas. This compels students to think about thinking — an important metacognition activity.
3. Designing Tests “AI-Inclusive” Not “AI-Proof”
it’s a futile exercise trying to make everything “AI-proof.” Students will always find new methods of using the tools. What needs to happen instead is that tests need to accept AI as part of the process.
Mark not only the result, but their thought process as well: Have students discuss why they accepted or rejected AI suggestions.
Example prompt:
This makes AI a study buddy, and not a cheat code.
4. Immersing Technology with Human Touch
Teachers should not be driven away from students by AI — but drawn closer by making assessment more human-friendly and participatory.
Ideas:
Human element: A student may use AI to redo his report, but a live presentation tells him how deep he really is.
5. Justice and Integrity
Academic integrity in the age of AI is novel. Cheating isn’t plagiarizing anymore but using crutches too much without comprehending them.
Teachers can promote equity by:
Employing AI-detecting software responsibly — not to sanction, but to encourage an open discussion.
It builds trust, not fear, and shows teachers care more about effort and integrity than being great.
6. Remixing Feedback in the AI Era
Example: Instead of a “AI plagiarism detected” alert, give a “Let’s discuss how you can responsibly use AI to enhance your writing instead of replacing it.” message.
7. From Testing to Learning
The most powerful change can be this one:
AI eliminates the myth that tests are the sole measure of demonstrating what is learned. Tests, instead, become an act of self-discovery and learning skills.
Teachers can:
Final Thought
Not to be smarter than AI. To make students smarter, more moral, and more human in a world of AI.
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