TheAIprojectTheAIProject
TheAIprojectTheAIProject
TheAIprojectTheAIProject
The flexibility of LLMs make them ideal for creating custom lesson plans. This is a concept using AI to teach 8-12 yr old kids leadership skills, manage teams and practice empathy.
The flexibility of LLMs make them ideal for creating custom lesson plans. This is a concept using AI to teach 8-12 yr old kids leadership skills, manage teams and practice empathy.
Challenge
Challenge
There is a growing gap in leadership skill development among children and teens. As schools focus intensely on academic STEM performance and standardized testing, vital soft skills, including leadership, often remain on the periphery.
There is a growing gap in leadership skill development among children and teens. As schools focus intensely on academic STEM performance and standardized testing, vital soft skills, including leadership, often remain on the periphery.
Solution
Solution
Through the use of ML and LLMs we create custom scenarios and feedback loops that teach specific skills around leadership. LeaderQuest takes 8-12 year old kids on a vast journey with interactive encounters where they learn empathy, problem solving and manage team dynamics.
Through the use of ML and LLMs we create custom scenarios and feedback loops that teach specific skills around leadership. LeaderQuest takes 8-12 year old kids on a vast journey with interactive encounters where they learn empathy, problem solving and manage team dynamics.
AI integration
The following screens are a prototype of game flow and high fidelity UI. The goal is to capture a sense of adventure and wonder that is custom built for the player.
The following screens are a prototype of game flow and high fidelity UI. The goal is to capture a sense of adventure and wonder that is custom built for the player.
The following screens are a prototype of game flow and high fidelity UI. The goal is to capture a sense of adventure and wonder that is custom built for the player.
Section
World Map
Description
(A001) After onboarding and character creation, the LLM generates map regions to challenge player strengths and weaknesses.
Section
World Map
Description
(A001) After onboarding and character creation, the LLM generates map regions to challenge player strengths and weaknesses.
World Map
(A001) After onboarding and character creation, the LLM generates map regions to challenge player strengths and weaknesses.
Section
Map Region
Description
(A001) Each region is based on a leadership skill. The player will have between 4-8 encounters as they travel through the region. Visited encounters are scored.
Section
Map Region
Description
(A001) Each region is based on a leadership skill. The player will have between 4-8 encounters as they travel through the region. Visited encounters are scored.
Map Region
(A001) Each region is based on a leadership skill. The player will have between 4-8 encounters as they travel through the region. Visited encounters are scored.
Section
Encounter
Description
(B001) (B002) LLM creates encounter scenario that presents a character (antagonist) with a scenario the player needs to help or solve for. LLM generates dialogue options the player can choose from.
Section
Encounter
Description
(B001) (B002) LLM creates encounter scenario that presents a character (antagonist) with a scenario the player needs to help or solve for. LLM generates dialogue options the player can choose from.
Encounter
(B001) (B002) LLM creates encounter scenario that presents a character (antagonist) with a scenario the player needs to help or solve for. LLM generates dialogue options the player can choose from.
Section
Choosing Dialogue
Description
Player chooses LLM generated dialogue option that they think will help solve the antagonists issue. Dialogue choices also have animated emoji indicators to help player understand mood associated with the dialogue.
Section
Choosing Dialogue
Description
Player chooses LLM generated dialogue option that they think will help solve the antagonists issue. Dialogue choices also have animated emoji indicators to help player understand mood associated with the dialogue.
Choosing Dialogue
Player chooses LLM generated dialogue option that they think will help solve the antagonists issue. Dialogue choices also have animated emoji indicators to help player understand mood associated with the dialogue.
Section
Score card
Description
(C001) LLM analyzes the performance of the player and gives a report about the style of the players leadership, what did well and what they can improve on.
Section
Score card
Description
(C001) LLM analyzes the performance of the player and gives a report about the style of the players leadership, what did well and what they can improve on.
Score card
(C001) LLM analyzes the performance of the player and gives a report about the style of the players leadership, what did well and what they can improve on.
Section
Flagging
Description
Flagging provides user feedback to the LLM to generate better choices. The flag button will enter into an edit mode at anytime during the game by players or guardians.
Section
Flagging
Description
Flagging provides user feedback to the LLM to generate better choices. The flag button will enter into an edit mode at anytime during the game by players or guardians.
Flagging
Flagging provides user feedback to the LLM to generate better choices. The flag button will enter into an edit mode at anytime during the game by players or guardians.
Character designs will vary depending on the tastes of the player. Also skill level will change how they interact with the player.
Character designs will vary depending on the tastes of the player. Also skill level will change how they interact with the player.
Character designs will vary depending on the tastes of the player. Also skill level will change how they interact with the player.
UI elements were generated by a mix of Stable Diffusion, Midjourney and good ol’manual design.
UI elements were generated by a mix of Stable Diffusion, Midjourney and good ol’manual design.
UI elements were generated by a mix of Stable Diffusion, Midjourney and good ol’manual design.
My overall performance for this AI class and the final project earned me a certification and letter of recommendation. Every assignment received a perfect score resulting in a recommendation from the class instructor Robert Redmond (ex IBM).
My overall performance for this AI class and the final project earned me a certification and letter of recommendation. Every assignment received a perfect score resulting in a recommendation from the class instructor Robert Redmond (ex IBM).
My overall performance for this AI class and the final project earned me a certification and letter of recommendation. Every assignment received a perfect score resulting in a recommendation from the class instructor Robert Redmond (ex IBM).
Alex Olmsted
©2024
Alex Olmsted
©2024
Alex Olmsted
©2024