Try for free
AI in Education Podcast

Victoria Hedlund on AI bias: What every teacher needs to know

25 June 2026 45:02 Dan Bowen and Ray Fleming

Listen to episode

About this episode

AI systems are increasingly being used in education, but what happens when those systems introduce hidden bias into learning, feedback, assessment and career advice?

In this episode, we're joined by Victoria Hedlund (AKA The AI Bias Girl), founder of GenEd Labs, former physics teacher, consultant and researcher specialising in AI bias in education.

Victoria shares practical examples of how AI tools can produce different outputs based on gender, names, backgrounds and other personal characteristics. From explanations of how a light bulb works to career guidance and assessment, we explore how seemingly small biases can compound over time and influence attainment, wellbeing and opportunity.

The conversation covers AI tutors, safeguarding concerns, assessment risks, the importance of keeping humans in the loop, and why educators need to adopt a more experimental mindset when working with generative AI.

This is a thought-provoking discussion about fairness, agency and what every teacher needs to know as AI becomes increasingly embedded in education.

For more background, here's two bits of Victoria's writing:

Deep dive on using AI for marking
https://victoriahedlund.substack.com/p/bias-bytes-special-edition-evidence 

Deep dive on Squishmallow Top Trumps
https://www.linkedin.com/posts/victoriamhedlund_biasgirl-biasaware-chatgpt-ugcPost-7452959849623220225-bIWG 

And finally, free downloads from Victoria, including 8 bias-mitigating prompts for teachers, and a growing set of other resources on AI bias in education.
https://genedlabs.ai/downloads 

More AI podcast episodes

Browse all →

Want to find AI jobs?

Join thousands of AI professionals finding their next opportunity

We respect your inbox. Unsubscribe at any time.

© 2026 AI in Education Podcast. All rights reserved.

Common Questions

Frequently asked questions

Quick answers about how DevFound's AI matching, resumes, and referrals work.

DevFound's AI Copilot ingests your profile, goals, and live job data to deliver curated matches in seconds. Every match includes a resume variant, suggested referrals, and interview prep so you can act immediately. The more feedback you provide, the sharper the Copilot becomes.

AI-led job searches shrink the hours spent sifting through boards and formatting resumes. DevFound pairs automation with your personal outreach, so you reserve energy for interviews and negotiation. Traditional networking still matters, but AI gives you a lift before you even send a message.

Modern AI roles expect comfort with production-grade code, data fluency, and practical ML tooling. The strongest candidates pair deep technical chops with storytelling—translating model impact to product, GTM, and exec partners. Continuous learning keeps you ahead as stacks evolve.

DevFound rewards active seekers. Keep your profile fresh, respond to match quality prompts, and enable alerts so you never miss a role. The AI prioritizes companies and teams that align with your feedback, accelerating both introductions and interview invites.

High-density tech hubs continue to host the deepest AI talent pools, yet distributed teams are catching up fast. Use DevFound filters to hone in on onsite, hybrid, or fully remote roles and watch openings expand across time zones.

DevFound aggregates thousands of remote AI openings and flags the nuances—core hours, async culture, and visa needs—up front. The Copilot also recommends how to position your distributed work experience so hiring managers know you can thrive on a remote team.

Still have questions?

Contact Support