Kritik Adds Visible AI Tools to Make Student Assessment More Transparent

Mohsen Shahini, a founder with experience at Top Hat, has engineered Kritik to address the core pedagogical tension between advanced AI use and measurable critical thinking. The system does not merely attempt to police student usage; rather, it reframes the learning objective from 'Is the answer correct?' to 'How is the student improving their thinking process?' This shift in focus represents a significant evolution in educational assessment.

The centerpiece is VisibleAI. Instead of functioning as a typical 'humanizer' detection tool, it provides full visibility into the writing process itself. Students work within this controlled environment with access to multiple LLMs, and the system meticulously tracks three critical data streams: direct student input, utilized prompts given by the user, and the volume of content sourced from AI chatbots. This level of granular transparency moves assessment away from a binary pass/fail judgment and toward an evaluative process that assesses process as part of the grade. Shahini notes this makes the expectation clear: students are expected to use AI; their mastery in doing so becomes the measurable skill.

This model is particularly resonant for the Canadian academic landscape. Universities often grapple with maintaining pedagogical rigor while acknowledging technology’s deep integration into student life. By making the interaction with AI visible, Kritik gives educators a diagnostic tool rather than just an accusation. It allows institutions to design learning outcomes that explicitly value sophisticated prompt engineering and the critical skill of synthesizing LLM output—skills increasingly demanded by modern employers in tech and knowledge economies.