Item response theory (IRT) is a statistical model used to analyze the relationship between test items and test takers. It is not a type of language testing technology.

Computer-based testing (CBT) offers several advantages over traditional paper-based testing, including more efficient and accurate scoring, a more engaging and interactive experience for test takers, and the ability to deliver tests to remote locations.

Adaptive testing is a type of language testing that uses computer technology to adapt the difficulty of the test items to the ability level of the test taker. This is done by administering a series of items to the test taker, and then using the test taker's responses to select the next item that is most appropriate for their ability level.

Adaptive testing offers several advantages over traditional fixed-form testing, including more accurate assessment of the test taker's ability, reduced testing time, and a more engaging and interactive experience for test takers.

Adaptive testing has several disadvantages compared to traditional fixed-form testing, including higher development and administration costs, potential difficulties in ensuring reliability and validity, and increased difficulty for test takers to prepare for the test.

Technology can be used in language testing to improve efficiency and accuracy, provide a more engaging and interactive experience for test takers, and make it easier to deliver tests to remote locations.

There are several challenges associated with using technology in language testing, including cost, reliability, and validity.

There are several ethical considerations that need to be taken into account when using technology in language testing, including privacy, security, and bias.

AI can be used in language testing to develop more accurate and reliable tests, provide real-time feedback to test takers, and automate the scoring of tests.

There are several challenges associated with using AI in language testing, including cost, reliability, validity, and bias.

Some of the future trends in language testing technology include increased use of AI, more personalized and adaptive testing, and greater use of technology to deliver tests to remote locations.

Item response theory (IRT) is a statistical model used to analyze the relationship between test items and test takers. It is not a type of language testing technology that uses AI.

NLP can be used in language testing to develop more accurate and reliable tests, provide real-time feedback to test takers, and automate the scoring of tests.

NLP has several disadvantages in language testing, including high development and implementation costs, potential difficulties in ensuring reliability and validity, and increased difficulty for test takers to prepare for the test.

ML can be used in language testing to develop more accurate and reliable tests, provide real-time feedback to test takers, and automate the scoring of tests.

ML has several disadvantages in language testing, including high development and implementation costs, potential difficulties in ensuring reliability and validity, and increased difficulty for test takers to prepare for the test.