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Each of the following sentences presents a verb in a simple tense form.
Identify the sentence that is incorrect.
In English, the letters TH may represent two different sounds: /θ/ (voiceless) and /ð/ (voiced).
Choose the alternative in which all the words contain the voiced TH sound (/ð/).
Before reading, a student looks at the title, images, and keywords to understand what the text is about and its general meaning.
This reading behavior focuses on getting the main idea quickly rather than details. Which strategy is being used?
In formal English, the Future Continuous can express polite certainty or scheduled events rather than ongoing actions.
Choose the sentence that correctly illustrates this:
Choose the correct option to complete the sentence.
I ______ my keys yesterday, but I ______ them this morning.
Fill in the blanks with the appropriate articles.
He is ______ honest man and works as ______ engineer.
Select the adjective form that best completes the sentence.
This street is ______ than the one we walked on yesterday.
Complete the sentence using the correct reflexive pronoun.
He fixed the computer all by ______.
Choose the correct modal verb to the following sentence.
You ______ arrive before 9 a.m., it’s required by the company rules.
Choose the prefix that correctly forms the opposite of the word below.
The opposite of ‘possible’ is ______.
Read the text to answer the question.
A recent Executive Order by President Biden emphasized the link between racial equity, education, and artificial intelligence (AI). It stated that the Federal Government must both pursue educational equity and eliminate bias in the design and use of new technologies, such as AI.
The U.S. Department of Education’s report Advancing Digital Equity for All defines digital equity as the condition in which individuals and technological communities capacity needed have the for full participation in society and the economy.
Concerns about racial equity and bias are central to the debate on AI in education. AI systems rely on datasets, and when these datasets are non-representative or contain biased patterns, the resulting models may behave unfairly. Such systematic unfairness in automated decisions is known as algorithmic bias, which can lead to discrimination and undermine equity at scale.
Bias is intrinsic to how AI algorithms are trained on historical data. When these biases sustain unjust or discriminatory practices in education, they must be identified and addressed. For instance, algorithms used for admissions, early intervention, or exam monitoring should be regularly evaluated for evidence of unfair bias, not only during design but also as they are deployed in real educational contexts.
U.S. Department of Education, Office of Educational
Technology. (2023). Artificial Intelligence and the Future of
Teaching and Learning: Insights and Recommendations.
Washington, DC: U.S.
Read the text to answer the question.
A recent Executive Order by President Biden emphasized the link between racial equity, education, and artificial intelligence (AI). It stated that the Federal Government must both pursue educational equity and eliminate bias in the design and use of new technologies, such as AI.
The U.S. Department of Education’s report Advancing Digital Equity for All defines digital equity as the condition in which individuals and technological communities capacity needed have the for full participation in society and the economy.
Concerns about racial equity and bias are central to the debate on AI in education. AI systems rely on datasets, and when these datasets are non-representative or contain biased patterns, the resulting models may behave unfairly. Such systematic unfairness in automated decisions is known as algorithmic bias, which can lead to discrimination and undermine equity at scale.
Bias is intrinsic to how AI algorithms are trained on historical data. When these biases sustain unjust or discriminatory practices in education, they must be identified and addressed. For instance, algorithms used for admissions, early intervention, or exam monitoring should be regularly evaluated for evidence of unfair bias, not only during design but also as they are deployed in real educational contexts.
U.S. Department of Education, Office of Educational
Technology. (2023). Artificial Intelligence and the Future of
Teaching and Learning: Insights and Recommendations.
Washington, DC: U.S.
Read the text to answer the question.
A recent Executive Order by President Biden emphasized the link between racial equity, education, and artificial intelligence (AI). It stated that the Federal Government must both pursue educational equity and eliminate bias in the design and use of new technologies, such as AI.
The U.S. Department of Education’s report Advancing Digital Equity for All defines digital equity as the condition in which individuals and technological communities capacity needed have the for full participation in society and the economy.
Concerns about racial equity and bias are central to the debate on AI in education. AI systems rely on datasets, and when these datasets are non-representative or contain biased patterns, the resulting models may behave unfairly. Such systematic unfairness in automated decisions is known as algorithmic bias, which can lead to discrimination and undermine equity at scale.
Bias is intrinsic to how AI algorithms are trained on historical data. When these biases sustain unjust or discriminatory practices in education, they must be identified and addressed. For instance, algorithms used for admissions, early intervention, or exam monitoring should be regularly evaluated for evidence of unfair bias, not only during design but also as they are deployed in real educational contexts.
U.S. Department of Education, Office of Educational
Technology. (2023). Artificial Intelligence and the Future of
Teaching and Learning: Insights and Recommendations.
Washington, DC: U.S.
Read the text to answer the question.
A recent Executive Order by President Biden emphasized the link between racial equity, education, and artificial intelligence (AI). It stated that the Federal Government must both pursue educational equity and eliminate bias in the design and use of new technologies, such as AI.
The U.S. Department of Education’s report Advancing Digital Equity for All defines digital equity as the condition in which individuals and technological communities capacity needed have the for full participation in society and the economy.
Concerns about racial equity and bias are central to the debate on AI in education. AI systems rely on datasets, and when these datasets are non-representative or contain biased patterns, the resulting models may behave unfairly. Such systematic unfairness in automated decisions is known as algorithmic bias, which can lead to discrimination and undermine equity at scale.
Bias is intrinsic to how AI algorithms are trained on historical data. When these biases sustain unjust or discriminatory practices in education, they must be identified and addressed. For instance, algorithms used for admissions, early intervention, or exam monitoring should be regularly evaluated for evidence of unfair bias, not only during design but also as they are deployed in real educational contexts.
U.S. Department of Education, Office of Educational
Technology. (2023). Artificial Intelligence and the Future of
Teaching and Learning: Insights and Recommendations.
Washington, DC: U.S.
Read the text to answer the question.
A recent Executive Order by President Biden emphasized the link between racial equity, education, and artificial intelligence (AI). It stated that the Federal Government must both pursue educational equity and eliminate bias in the design and use of new technologies, such as AI.
The U.S. Department of Education’s report Advancing Digital Equity for All defines digital equity as the condition in which individuals and technological communities capacity needed have the for full participation in society and the economy.
Concerns about racial equity and bias are central to the debate on AI in education. AI systems rely on datasets, and when these datasets are non-representative or contain biased patterns, the resulting models may behave unfairly. Such systematic unfairness in automated decisions is known as algorithmic bias, which can lead to discrimination and undermine equity at scale.
Bias is intrinsic to how AI algorithms are trained on historical data. When these biases sustain unjust or discriminatory practices in education, they must be identified and addressed. For instance, algorithms used for admissions, early intervention, or exam monitoring should be regularly evaluated for evidence of unfair bias, not only during design but also as they are deployed in real educational contexts.
U.S. Department of Education, Office of Educational
Technology. (2023). Artificial Intelligence and the Future of
Teaching and Learning: Insights and Recommendations.
Washington, DC: U.S.
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