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Create assignment
A lecturer sets the assignment and test configuration.
Submission comprehension assurance
AI can write the assignment. Can the student defend it?
sci2pro Sentinel™ gives students a short, submission-specific comprehension test immediately after they submit their work. Lecturers gain a practical signal for deciding who may require further oral review.
Not AI detection. Evidence of understanding.
Workflow signal
From submission to follow-up
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1. Submission received
Student uploads an essay, report, or project response.
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2. Assessment generated
Sentinel creates questions grounded in that specific submission.
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3. Comprehension checked
Students answer multiple-choice, true/false, and short-response items.
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4. Lecturer reviews the signal
Weak performance can trigger selective oral follow-up where it is genuinely needed.
The problem
A polished submission no longer guarantees that learning happened.
Generative AI can produce convincing academic work quickly. At the same time, AI-detection claims remain unreliable, blanket bans are difficult to enforce, and full oral examination for every student is too expensive for most courses.
Sentinel helps lecturers screen the whole class and concentrate further investigation where it is most needed.
How Sentinel works
A five-step workflow designed for real teaching environments.
Sentinel is a signal, not a verdict. Questions are based on the student’s own submission and results help lecturers decide where oral follow-up may be worthwhile.
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Student submits work
The platform receives the student’s specific essay, report, or draft.
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Questions generated
Sentinel produces short multiple-choice, true/false, and short-response checks.
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Timed assessment completed
Students complete the submission-linked comprehension assessment immediately.
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Selective oral follow-up
Lecturers review scores, explanations, and evidence to choose cases for further discussion.
Before submission
Run a final comprehension check before you submit.
Sentinel Check™ allows students to upload a draft, test their own understanding, review weak areas, and try again before making the official submission. It is optional, private by default, and designed for learning rather than punishment.
Upload draft
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Take practice test
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Review weak concepts
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Study and revise
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Submit with confidence
Join the waitlist for Sentinel Check™
For lecturers
Screen the whole class. Investigate selectively.
- Quickly identify students with weak demonstrated understanding.
- Reserve oral examination for failed or anomalous cases.
- Review questions, answers, scores, and supporting evidence.
- Re-mark attempts when a generated question is defective.
- Reduce the burden of manually examining every student.
For students
Check understanding before submission.
- Identify concepts that require further study.
- Improve confidence in defending submitted work.
- Receive feedback grounded in the student’s own document.
- Treat assessment as part of learning rather than punishment.
- Practise privately before the official submission event.
Clarification
Sentinel does not try to prove who wrote the work.
Not an AI detector
Sentinel does not claim to determine whether a submission was AI-generated.
Not a plagiarism checker
It is not a substitute for similarity analysis or existing integrity workflows.
Not a replacement for judgment
Lecturers remain responsible for interpreting the signal and deciding what follow-up is proportionate.
Sentinel provides structured evidence of whether a student understands the work they submitted.
Pilot design
Help shape the first version of Sentinel.
We are inviting a small group of lecturers and academic programmes to test a barebones MVP in real teaching environments. Early adopters will be selected based on assignment suitability, class size, subject area, willingness to provide structured feedback, and willingness to conduct limited oral follow-up for validation.
Early pilots will evaluate question quality, lecturer usefulness, agreement with oral follow-up, student experience, and willingness to adopt the tool repeatedly.
Who this is for first
- University lecturers and college instructors
- Programme leaders and teaching offices
- Assessment-heavy subjects with defendable written work
- Teams willing to validate outcomes with limited oral review
FAQ
Questions institutions are likely to ask first.
Does Sentinel detect AI-generated work?
No. Sentinel does not attempt to prove authorship or detect AI generation. It assesses whether students can demonstrate understanding of what they submitted.
Are students prevented from using AI?
No. The premise is different: students may use AI if permitted, but they should still be able to explain and defend the submitted work.
What happens if a generated question is poor?
Lecturers can review questions and re-mark or adjust outcomes if a generated item is defective. The MVP is designed to support judgment, not replace it.
Can students practise before submission?
Yes. Sentinel Check™ is the student-facing pre-submission study flow. Students can also use a private personal study mode in the MVP.
Will lecturers see Sentinel Check™ activity?
Assignment-linked practice can be visible in the platform, but the student-only personal study tool is intended as a private readiness check.
Does Sentinel replace oral examinations?
No. It helps reserve oral follow-up for the subset of cases where extra verification appears justified.
Which assignment formats are supported first?
The MVP is best suited to essays, reports, case studies, and similar text-based submissions. Format coverage can expand based on pilot feedback.
Early access
Apply for early access.
We will review applications and invite suitable pilot participants in stages. Acceptance is selective and depends on course context, assignment design, and pilot fit.