I replaced traditional coding tests with real conversations about how my students solve problems using AI.
Replace coding tests with AI-driven interviews using Google Gemini in Colab. See how this professor evaluates student judgment. Read the full report.
A professor at Universiti Putra, Malaysia, has replaced traditional coding tests with AI-driven interviews using Google Gemini in Colab, focusing on evaluating student judgment and problem-solving skills.
What happened
A professor at Universiti Putra, Malaysia, replaced traditional coding tests with AI-driven interviews using Google Gemini in Colab, assessing students' problem-solving skills and judgment through verbal interviews and AI collaboration.
Why it matters
This approach highlights the importance of human-AI collaboration in education, emphasizing critical thinking and judgment over rote coding skills, which is crucial for AI developers and educators aiming to integrate AI effectively in learning environments.
- AI can be used as a collaborative tool in education to enhance critical thinking.
- Evaluating student judgment over coding syntax is essential for developing human-AI teamwork skills.
- Verbal interviews and AI tools like Gemini help assess and scale student evaluations effectively.
The bigger picture
The method provides a scalable way to evaluate student work while fostering critical thinking and human-AI collaboration, which could influence educational strategies in AI and technology fields.
By integrating AI into assessments, the professor emphasizes the importance of human judgment and collaboration, reshaping how students learn and apply AI in problem-solving.
Technical details
- Google Gemini in Google Colab
- Gemini Canvas
- Gemini Notebook
FAQ
What is the main goal of using Google Gemini in assessments?
The main goal is to evaluate students' judgment and problem-solving skills by having them collaborate with AI, rather than focusing solely on coding syntax.
How does the professor assess student work with AI?
The professor uses Gemini Canvas and Gemini Notebook to evaluate student work at scale, along with verbal interviews to assess students' ability to defend their choices and troubleshoot AI recommendations.
What is the significance of human-AI teaming in this approach?
Human-AI teaming is significant because it shifts the focus from rote coding to critical thinking, teaching students to exercise judgment over AI suggestions and develop lasting human capability.
This summary and analysis were generated with AI from the original article and may contain errors. Read the original for the full story.
Read the full story
Originally published by Google AI Blog on Oct 5, 2026.
Comments
Sign in to join the discussion.