What mattersShow
- Mohsen Shahini's Kritik is presenting a compelling paradigm shift to higher education assessment
- Operational lens: AI-integrated assessment platform tracking student input sources (direct typing, prompts, LLM usage) for academic evaluation.
- Open the company page to keep the follow-up signal in view.
Mohsen Shahini's Kritik is presenting a compelling paradigm shift to higher education assessment: moving the focus from mere content recall ('Did the student get it right?') to assessing critical thinking and process fluency ('Is the student getting better at thinking?'). This is an evolution of academic evaluation, not just another tech wrapper. The core ingenuity lies in VisibleAI. Instead of participating in the traditional 'detection arms race'—where edtech platforms waste resources trying to flag AI-generated work—Kritik pivots toward radical transparency and integration. VisibleAI doesn't accuse; it tracks. It provides a granular, real-time view inside the student’s workspace, differentiating between text typed directly by the student, prompts submitted to various Large Language Models (LLMs), and content pulled directly from the AI chatbot itself. This technical approach is crucial because it reframes AI usage not as cheating, but as a quantifiable skill. Shahini's vision allows educators to treat AI use itself—the ability to craft effective prompts, integrate diverse sources, or synthesize machine output—as an explicit learning variable. When the process of writing becomes transparent, instructors can grade the efficacy of the thinking process, rather than just the final polished product. The platform’s expansion past detection into collaborative assessment via Kritik360 further strengthens its position. By enabling students to peer-grade and critique each other's use of AI, the system forces a human element back into the loop—a concept Shahini emphasizes throughout. The combined effect is a systemic shift: the tool doesn't replace the teacher; it enhances their observational capacity, allowing them to observe cognitive offloading in action and teach students how to manage AI dependencies responsibly. This model has significant resonance for the Canadian educational landscape. As universities increasingly grapple with the challenge of integrating advanced generative AI into curricula, a system that provides verifiable data on student-AI interaction is immensely valuable. It offers institutions a concrete mechanism—a way to move from panic and prohibition to structured pedagogical integration, helping students build mastery while acknowledging the technological shift.
Get the week’s essential Canadian tech.
Five minutes. One useful email. No noise.
Sources & technical notesShowHide
Where this story is grounded
Use the public signals, research inputs, and editorial framing here to understand how the story was built.
What to evaluate next
This box highlights the systems, workflows, and decisions the article helps you assess.
Stay in the signal after this story.
Follow the company page, then jump into the broader sector hub before you leave the story.
Keep the company context attached as you read the rest of the coverage.
Weekly Canadian tech signals, distilled for operators.
Subscribe to the signalFree weekly briefing • Unsubscribe anytime
A practical checklist for Canadian policy, privacy, procurement, and governance teams who need a quick way to sanity-check AI deployments before they scale.
Open resource