Apr 2, 2026
This week’s episode dives into a wave of new research shaping
how AI is actually being used in education.
We explore what works (and what doesn’t) when it comes to
AI-generated feedback, including why blended, “hybrid” feedback may
be the most effective approach - and why more feedback doesn’t
always lead to better outcomes.
The conversation then turns to one of the most important emerging
issues: bias in AI systems. From subtle differences in tone to
stereotyping based on student characteristics, the research
highlights why educators need to be cautious about the data they
provide AI tools.
“If you use AI to write feedback, it does not treat
every student the same way equally.”
We also talk about the growing evidence around AI tutors - where
they outperform humans, where they fall short, and what actually
drives meaningful learning gains.
Along the way, we tackle major questions around detection, student
use, teacher workload, and whether AI can ever replace human
connection.
The big takeaway? AI is powerful. And how we design, guide, and use
it in education matters more than ever.
Research Papers discussed this week
AI for Feedback
AI and Bias
AI Tutors
AI Detection
Teacher Workload
Student use