Instructional design and product QA intern

Career Planet is an AI-driven startup focused on enhancing instructional design through innovative learning experiences. They are seeking a Graduate Intern to assist the product team in researching and validating AI outputs while aligning instructional materials with learning theories.

Responsibilities:

• Instructional design auditing
• Audit AI outputs against established frameworks such as Bloom’s Taxonomy and Gagné’s Nine Events of Instruction
• Evaluate the instructional content generated, check for a logical, scaffolded learning structure that makes sense for a human learner
• QA testing
• Systematically test the platform functionality to identify UI/UX friction points for instructional design end users
• Identify "andragogical bugs": instances where the AI provides instructionally unsound advice or skips critical training steps. Then document these for the product and technical team
• Prompt engineering support
• Work with the Product Manager to test and tweak the system prompts that guide the AI, helping to improve the "voice" and instructional quality of the assets generated in the platform
• Research and data validation
• Identify and curate high-quality, validated datasets and documentation to inform the AI’s backend: sourcing peer-reviewed content and Open Educational Resources (OER)
• Cross-reference and validate AI-generated content against authoritative sources to check for factual accuracy and prevent "hallucinations" in high-stakes training topics
• Reference management: contribute to organizing a centralized library of sources that the AI can use to cite its work, helping end users trust every asset generated

Requirements:

• Currently enrolled in or recently completed a graduate program related to Instructional Design, Educational Technology, or Learning Science
• Fluent in instructional design frameworks such as ADDIE
• Experienced with (or willing to learn) bug-tracking and project management software
• Skilled researcher capable of navigating databases (e.g., Google Scholar) and distinguishing between 'popular content' and 'validated data.'
• Practical experience with LLMs (ChatGPT, Claude, Gemini) and a strong interest in how AI can be applied to instructional content design and development
• Authorized to work in the United States