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Page 2 of 7 - AI Writing Overview Submission ID trn:oid:::10159:73314475
0% detected as AI Caution: Review required.
The percentage indicates the combined amount of likely AI-generated text as It is essential to understand the limitations of AI detection before making decisions
well as likely AI-generated text that was also likely AI-paraphrased. about a student’s work. We encourage you to learn more about Turnitin’s AI detection
capabilities before using the tool.
Detection Groups
1 AI-generated only 0%
Likely AI-generated text from a large-language model.
2 AI-generated text that was AI-paraphrased 0%
Likely AI-generated text that was likely revised using an AI-paraphrase tool
or word spinner.
Disclaimer
Our AI writing assessment is designed to help educators identify text that might be prepared by a generative AI tool. Our AI writing assessment may not always be accurate (it may misidentify
writing that is likely AI generated as AI generated and AI paraphrased or likely AI generated and AI paraphrased writing as only AI generated) so it should not be used as the sole basis for
adverse actions against a student. It takes further scrutiny and human judgment in conjunction with an organization's application of its specific academic policies to determine whether any
academic misconduct has occurred.
Frequently Asked Questions
How should I interpret Turnitin's AI writing percentage and false positives?
The percentage shown in the AI writing report is the amount of qualifying text within the submission that Turnitin’s AI writing
detection model determines was either likely AI-generated text from a large-language model or likely AI-generated text that was
likely revised using an AI-paraphrase tool or word spinner.
False positives (incorrectly flagging human-written text as AI-generated) are a possibility in AI models.
AI detection scores under 20%, which we do not surface in new reports, have a higher likelihood of false positives. To reduce the
likelihood of misinterpretation, no score or highlights are attributed and are indicated with an asterisk in the report (*%).
The AI writing percentage should not be the sole basis to determine whether misconduct has occurred. The reviewer/instructor
should use the percentage as a means to start a formative conversation with their student and/or use it to examine the submitted
assignment in accordance with their school's policies.
What does 'qualifying text' mean?
Our model only processes qualifying text in the form of long-form writing. Long-form writing means individual sentences contained in paragraphs that make up a
longer piece of written work, such as an essay, a dissertation, or an article, etc. Qualifying text that has been determined to be likely AI-generated will be
highlighted in cyan in the submission, and likely AI-generated and then likely AI-paraphrased will be highlighted purple.
Non-qualifying text, such as bullet points, annotated bibliographies, etc., will not be processed and can create disparity between the submission highlights and the
percentage shown.
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Page 3 of 7 - AI Writing Submission Submission ID trn:oid:::10159:73314475
Application Paper
Student’s Name
Institutional Affiliation
Course Code and Name
Professor
Date
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Application Paper
Scenario 1: Sub-par Peer Performance
If one realizes that they have decided to assign something to a coworker that they could
have done but did not, it is important to take care of the situation clearly and empathetically.
Wrench's ideas of feedback and expectation management teach that feedback is good. Feedback
should be specific, not personal: The way a person comments on the work is not good enough; one
would find it more effective to respond to the missing part, "the report was missing data analysis
section," for instance. "Next time can you, please focus on adding that?"
Communicate what you expect your peer to do for the expectation management concept.
If you need to state your expectations with the initial request clearly, then learn from it and change
your approach next time to make your goals more precise than before. It means no more excuses
about it not being good enough because you have a more explicit benchmark of success, so it
removes ambiguity around what constitutes success. It allows your coworkers to understand how
to meet your standards. It also helps to lower frustration from either side.
Now, you need to learn to balance the feedback so as not to do the work for your coworkers.
That is why we need empowerment. Instead of simply correcting the task, you can suggest what
they have to do differently, giving them tools or resources that will enhance their productivity.
You can also ask: 'Do you have any support or resource issues to help you meet the
expectations?' This model lets your coworkers work with their name, or their brand, behind their
work, and if you trust them enough to know that they can only get better and care about their work,
it means they own it.
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Moreover, collaboration requires attention. One might instead suggest a follow-up meeting
to go over the task together, as it asks for a team effort. This also means you are not nitpicking;
you really want to work together to achieve the best outcome. This feedback is delivered as a
reprimand, and if left unchecked, a culture of hurt feelings remains. But delivered as one of
collaboration, we all grow together.
Scenario 2: Training Challenge
You will be working to host a training course that communicates with team members who
speak English, Spanish, and everything in between. Wrench explores what it takes to tackle
language barriers and achieve cultural competence in training: It is equitable that both groups
receive the same quality of training.
To achieve all this, bilingual training material that can be supported by bilingual team
members or professional translators to help explain or otherwise interpret the important points
during the training session is needed. This ensures that no one misses critical information and that
it's just as accessible to everyone.
Furthermore, workers must be bilingual, speak English and Spanish, and talk to other
employees in their familiar language. Trainers communicate differently and have different
expectations and learning preferences based on their culture. For example, if team members are
Spanish speakers, one must consider the formality needed and the level of authority they need
while in training. This is why culturally trained and competent professionals would have an idea
of this and approach each group on their communication preferences. For example, they could
include examples or references within their learning that are culturally pertinent to each group to
help people relate to the teaching and become more engaged.
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Visual aids, like diagrams, can further aid in reducing the language gap while delivering
the training in verbal content through non-verbal communication techniques. This will remove the
language barrier and guarantee that no participant fails to understand the session's main points. In
addition, it is beneficial to allow both groups to attend and ask questions in the language they
commonly use as it will help retain equal engagement of all team members and, consequently,
avoid misunderstanding.
Lastly, one can create an inclusive space where both groups can comfortably share their
language or ideas while working together. Equally important is encouraging team members to use
small group discussions, wherein each member pairs up with someone who speaks the other
language. This helps ensure the training meets the linguistic challenge and is fertile ground for
working simultaneously and learning about each other.
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References
Wrench, J. S. (2013). Workplace communication for the 21st century: Tools and strategies that
impact the bottom line [2 volumes]. Bloomsbury Publishing USA.
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