Learning Design Lead

About BlueCat

BlueCat focuses on teaching children aged 8 to 12 to critically evaluate artificial intelligence by encountering scenarios where an AI system behaves confidently but incorrectly. These scenarios engage children to explore AI vulnerabilities through five empirical educational stages: Briefing, Encounter, Investigate, Decide, and Reflect.

Role Overview

This senior position requires leading the evaluation of learning effectiveness and refining the educational experience. The role emphasizes two key responsibilities: overseeing educational measurement instruments and sustaining external educational partnerships.

Key Responsibilities

• Validate and take ownership of pre- and post-learning measurement tools to assess learning outcomes.
• Design and facilitate learning sessions, analyze results, and adapt learning designs accordingly.
• Create or assist in developing new learning scenarios within the established five-stage framework.
• Define learning goals and sequence progression across scenarios.
• Design AI vulnerabilities within scenarios that are plausible and enable children to discover errors independently.
• Ensure each learning scenario allows multiple authentic discovery paths, so insights arise from the child’s reasoning.
• Review all educational content for accuracy, age suitability, inclusiveness, and accessibility.
• Develop facilitator guides, teacher materials, and resources for parents.
• Build and maintain relationships with curriculum partners, the Ministry of Education, and schools to support pilot programs and eventual deployment.
• Represent BlueCat’s educational approach at external initiatives, partnerships, and events.
• Collaborate with product and technical teams to translate learning designs into actionable specifications.

Requirements

• Degree in Education, Curriculum Development, Instructional Design, Educational Psychology, Child Development, or a related field.
• Proven experience in teaching or designing curriculum for children aged 8 to 12, with specific examples of lessons and children impacted.
• Demonstrated expertise in designing learning assessments, pre/post intervention measurement tools, rubrics, or evaluating educational interventions.
• Ability to simplify complex concepts such as uncertainty, bias, false confidence, and automation dependence for children aged 8 to 12.
• Strongly preferred experience includes inquiry-based or Socratic teaching methods, digital learning product development, published or practical work in AI literacy, media literacy, or critical thinking, and conducting research or pilot programs with children.