Boxer - Using AI to group HMW prompts into visual design archetypes for concept development

https://docs.google.com/document/d/15AuP5AOGx4c1WA8aa-5PVSsuNqvCaa3zYvrH69MCCJ8/edit?tab=t.0

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DJAI - Using AI to remix HMWs from different user types to create intersectional prompts

https://docs.google.com/document/d/1j8D76eYFMaMxar6-wMHozQqkYcJMm79Uf2PULGCo5iQ/edit?usp=sharing

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Bolster - Using AI to identify tensions across 'How Might We' questions to drive divergent ideation

https://docs.google.com/document/d/1WNheTp9IXB8D2rvErbu6dQjAi_eZwK7vfOH54TBlzgU/edit?tab=t.0

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Boomerang - A problem solving approach to identify listener pain points and turn them into solution oriented questions

https://docs.google.com/document/d/137eKKTPGazQgLcBOZWEvffxuepg6LTzuKaoLjZx2f1Q/edit?usp=sharing

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Sandwiches - Using AI to cluster Personas into overarching themes

https://docs.google.com/document/d/1Wg5gq-rYMNincWBuekjOlxfwfmXRD73IThX7nicSz2I/edit?usp=sharing

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Rancore - Using AI to Rate and Rank Podcast Discovery Ideas

https://docs.google.com/document/d/1BD7mhGc8O1yccyC20-wkydPrQVW0rmRqCmJObZ8zjiw/edit?usp=sharing

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Fork - This experiment uses AI-generated interviews to create dynamic personas that predict podcast format preferences (e.g., narrative vs. educational) based on listener engagement and habits

https://docs.google.com/document/d/1bz8LmLsi2_y33-M-aqjoA_ybQaX3rQqkdibkLjpbDl4/edit?usp=sharing

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Root - This experiment aims to map emotional drivers from AI-generated interviews and create personas that help generate emotionally resonant podcast recommendations.

https://docs.google.com/document/d/1kgpuENrAeSoQ16iZ0yjkZimzpQ7vcEdbBylPw6-OoJE/edit?usp=sharing

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Emot - This experiment uses AI-generated interviews to create emotionally-driven listener personas that predict podcast preferences based on emotional needs like relaxation or excitement.

https://docs.google.com/document/d/11eBFSXOox7DsRnuLOhNt9D1tqgqlS7-MvPEi-m7iXb4/edit?tab=t.0

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Clone - AI-Powered Persona-Based Recommendations

How can AI generate more personalized podcast recommendations based on different user personas while ensuring alignment with real listener preferences?

https://docs.google.com/document/d/1axVE0h_mrT0eyGsVDZWcff1KTYbnV1leIqZDK3e0mBc/edit?tab=t.0

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