Now we need to start planning a framemwork for conversational assessment: mea...
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Now we need to start planning a framemwork for conversational assessment: meaning where a student is assessed by an agent. Let's brainstorm how this might work. First, it should always be done by the most capable agent available, which is GPT-5.2 at current. Second, it may involve multiple agents working together in different roles: one directly interacting with the student, while another evaluates the student replies, keeps track of time, and makes sure the other agent is staying on task. We want a way of storing details specific to each conversational assessment. What should the student know? What are the kinds of questions that the agent should ask? And so on. Questions may be both factual or ask that students express and defend an opinion, in which case the agent should probe for understanding by challenging their opinion and expecting them to defend it or concede as appropriate.
As a first example, I'd like to create one about the turing test. It should have two portions. First, it should ask students to explain the turing test to demonstrate understanding. Second, it should ask the student whether, in their opinion, current AI models pass the turing test or not. This is an open-ended question: either position is fine, but students should be able to explain and support their views.
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Let me explore the codebase to understand the existing architecture before we design this framework.
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Good, I now have a thorough understanding of the existing architecture. Let me ask some clarifying questions before designing the plan.
Sorry. I should mention that some of these assessments will be exposed publicly for students to complete on the main site, while others will only be visible in our on-campus tutoring center. We'll need to add special support for those later since it involves IP restrictions.
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Good to know. That suggests we need a visibility/access model on assessment definitions. Let me ask a couple more questions before I design the framework.
Good context. Let me now design the implementation plan based on everything I've learned.
Now let me read a few critical files to verify the plan aligns with existing patterns before finalizing.
Now I have a complete picture. Let me write the final plan.
The plan covers the full conversational assessment framework:
- Two-agent pipeline: evaluator provides real-time steering to the interviewer after each student message
- Assessment definitions: Markdown+YAML files in
content/assessments/, following the agent file pattern - Configurable: feedback visibility, time limits (hard/soft), retakes - all per-assessment
- Standalone route:
/assessmentswith chat-like UI that reuses existing components - Visibility: public, authenticated, and tutoring-center (stubbed for IP restriction later)
- First assessment: Turing test with factual (explain it) and opinion (do AI models pass?) portions
- 9 implementation phases from types through E2E tests, with clear file list and verification steps
Oh, one thing I wanted to add. Can we allow each evaluation to be augmented with additional information, or maybe that is part of the prompt? This should also only be visible to the evaluator to avoid the interviewer exposing it to the student. For the turing test, that could include the definition, aspects of the definition that are important for students to understand; for the "Does AI pass it" that could include arguments both for and against, along with counterarguments. Does that make sense?
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Yes, that makes perfect sense. Essentially an "answer key" / reference material per portion that only the evaluator sees, so it can accurately judge whether the student's responses are correct and complete, without the interviewer having access to leak answers.
The cleanest approach is an evaluatorContext field on each portion in the YAML definition. For example:
portions:
- id: explain
type: factual
criteria: [...]
evaluatorContext: |
The Turing test was proposed by Alan Turing in 1950 as the "imitation game"...
Key aspects: three participants (interrogator, human, machine),
text-only communication, passing means the interrogator can't
reliably distinguish...
- id: opinion
type: opinion
criteria: [...]
evaluatorContext: |
Arguments FOR current AI passing:
- GPT-5 can fool humans in brief exchanges...
Arguments AGAINST:
- Extended conversations reveal gaps...
Counterarguments for each side: ...
The evaluator prompt builder includes evaluatorContext for accurate assessment. The interviewer prompt builder never receives it, so there's no risk of leaking answers to the student.
Let me update the plan with this.
The plan now includes evaluatorContext as an optional field on each portion. The evaluator receives it for accurate assessment; the interviewer is explicitly excluded from it. The Turing test example shows concrete reference material for both portions — factual definitions for portion 1, and arguments/counterarguments for portion 2.