Lecture IX — Judgment and Presenting

AI-Assisted Programming for PhD Researchers

Dr. Tobias Vlćek

Helmut Schmidt University — September 2026

When Not to Use AI

The Question Is Not “Can It?”

  • It usually can produce something — that was never the question
  • The question is whether you should delegate this
  • Now, in a thesis you will have to defend
  • “It compiles” and “I can stand behind it” are different bars

Do Not Delegate

  • What you are here to learn — the core methods of your field
  • Architectural decisions you will live with for years
  • Anything you cannot verify yourself
  • Statistics you do not actually understand

The Verification Boundary

  • Usable delegation ends where your ability to check ends
  • Self-perception is a bad meter for this
  • METR’s developers felt ~20% faster while being 19% slower (Becker et al. 2025)
  • Growing that boundary outward is your development as a researcher — the goal is calibrated trust

Keeping Your Edge

  • Deliberately code without AI sometimes — small katas, first drafts of core logic
  • Make explain-back a habit: say what the code does before you accept it
  • Confidence in the tool predicts less critical thinking (Lee et al. 2025)
  • The RCT, one last time: how you use it decides what you keep (Shen and Tamkin 2026)

Your Own Guardrails

  • When the goal is learning, ask the agent for hints and next steps, not full solutions
  • That is the exact design that erased the learning harm in the PNAS tutoring RCT (Bastani et al. 2025)
  • The effect is specific to that guarded-tutor study — treat it as a pattern to copy, not a guarantee
  • After this course, you are your own guardrail

Discussion

Two questions for the room:

  • Which task this week would you not delegate again?
  • Where did you accept something you couldn’t explain?

Presenting Your Project

The Shape of a Good 12 Minutes

  • Problem — what and why (2 min)
  • Live demo — show it working (4 min)
  • Process — how you worked with the agent (3 min)
  • Challenges — what broke, what you changed (2 min)
  • Insights — what the room should take away (1 min)

Mirrors the four points you are graded on.

Demo Risk Management

Live demos fail. Have a fallback:

  • Record a 60-second screen capture now, while it works.
  • Test on the projector’s resolution before you present.

Slides, Fast

  • You may of course use AI for the slides too
  • Same rules apply: every claim on a slide is yours
  • Keep it lean — aim for ~8 slides
  • Talk to the demo, not to a wall of text

Reflection Beats Perfection

  • An honest “here is where it went wrong and what I changed”…
  • …scores better than a polished façade
  • It also teaches the room more than a flawless run
  • Your judgment is the thing on display, not the tool’s output

Continue Your Journey

After the Course

Bastani, Hamsa, Osbert Bastani, Alp Sungu, Haosen Ge, Özge Kabakcı, and Rei Mariman. 2025. “Generative AI Without Guardrails Can Harm Learning: Evidence from High School Mathematics.” Proceedings of the National Academy of Sciences 122 (26): e2422633122. https://doi.org/10.1073/pnas.2422633122.
Becker, Joel, Nate Rush, Elizabeth Barnes, and David Rein. 2025. Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity. arXiv. https://doi.org/10.48550/ARXIV.2507.09089.
Lee, Hao-Ping (Hank), Advait Sarkar, Lev Tankelevitch, et al. 2025. “The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers.” Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (Yokohama Japan), April, 1–22. https://doi.org/10.1145/3706598.3713778.
Shen, Judy Hanwen, and Alex Tamkin. 2026. How AI Impacts Skill Formation. arXiv. https://doi.org/10.48550/ARXIV.2601.20245.