Sample Projects

AI tools have age restrictions (found in the end user license agreement or other requirements for tool use), so the educator will need to implement the tool use based on input from the students.

Design an AI Tool that Would Help Students Find Lost Items

• User Interface for Reporting/Querying
  • Natural Language Input: Students can verbally describe or type details about their lost item.
  • Example: "I lost my blue water bottle, the one with the stickers, probably near the gym after lunch."
• Structured Data Collection: The AI prompts for key details:
  • Item Type (e.g., backpack, phone, jacket, water bottle)
  • Distinguishing Features (e.g., color, brand, unique markings, stickers)
  • Last Known Location (e.g., classroom, library, gym, specific area)
  • Time Lost (e.g., "this morning," "yesterday afternoon")
  • Student's Contact Information (for notification purposes)
• Centralized Lost & Found Database
  • "Lost" Item Log
    • Stores all reported lost items with their descriptions and student contact info.
  • "Found" Item Log
    • School staff (or potentially other students) can log items they find, including location found and a detailed description.
    • Images of found items could also be uploaded.
• Matching Algorithm
  • Uses keyword matching, fuzzy logic, and potentially image recognition (if images are provided for found items) to cross-reference "lost" reports with "found" items.
  • Prioritizes matches based on item type, unique features, and spatial/temporal proximity.
• Proactive Matching Alerts
  • If a match is found, the AI immediately notifies the student.
  • Example: "Good news, [Student's Name]! I think I found your blue water bottle! A bottle matching your description was found near the gym entrance this afternoon. Please check with Mrs. Chen in the main office to retrieve it."
• Search Guidance
  • If no immediate match is found, the AI offers helpful advice.
  • Example: "I haven't found a match for your [item] yet, [Student's Name]. Keep an eye out in your usual spots or try retracing your steps. I'll keep looking and let you know if anything turns up!"
• Follow-up
  • The AI could periodically check in with the student about the status of their lost item until it's found or marked as unrecoverable.
• Underlying Technology and Integration:
  • Natural Language Processing (NLP): For understanding student queries and generating friendly responses.
  • Machine Learning (ML): To refine safety risk assessment, improve matching accuracy, and personalize interactions.
  • Cloud Infrastructure: For database hosting, processing power, and scalability.

This scenario involves a progression of AI applications, moving from classification to analysis and then to potential mitigation .

Distinguishing Crops from Weeds and Analyzing Related Issues

Step 1: Identifying Crops or Weeds

• Concept: This requires Classification using Machine Learning.
• Potential Tools/Approaches
  • Open-source Python libraries for:
    • Image classification tasks (LSTMs - Long Short-Term Memory networks).
    • Data manipulation and cleaning.
    • Numerical operations.
    • Creating graphs and visuals from your dataset.
  • No-Code/Low-Code Platforms are available for:
    • Dataset management and labeling.
    • Creating machine learning pipelines and models using simple interfaces like drag-and-drop.

Step 2: Analyzing Issues Arising from Classification

• Concept: This involves AI-powered data analysis and natural language processing (NLP) to interpret the implications of the classifications.
• Potential Tools/Approaches
  • Large Language Models (LLMs):
    • Input: A set of labeled images of crops and weeds from step 1 and information about your location like the USDA plant hardiness zone, soil conditions, weather conditions, or other useful data about your location.
    • Prompt: Ask the LLM to analyze potential issues. For example: "What are implications of these weeds for my local crop or farming operations?", "What economic consequences could arise?", "How might these weeds affect my neighbors?" LLMs can synthesize information and identify interconnected problems based on their vast training data.
  • Data Visualization Tools (though not strictly AI, crucial for analysis):
    • Use AI tools to visualize identified plants in various images, potentially distribution of crops and weeds over time, and other statistics around your dataset. This can help in formulating better questions for the AI analysis tools.

Step 3: Discovering Ways for AI to Fix Issues

• Concept: This is where AI moves from analysis to prescriptive recommendations and potentially autonomous action. This is often the most complex and research-intensive part.
• Potential Tools/Approaches  (more conceptual and research-oriented)
  • LLMs for Solution Brainstorming:
    • Prompt: "Given the identified weeds in the crops, what AI-driven solutions could be proposed to address these problems?"
    • LLMs can suggest solutions like: recommending specific integrated weed management strategies that will likely be successful given your site conditions and weed concentrations, precision agriculture techniques like targeted crop protection tools that differentiate between crops and weeds, smart irrigation systems (AI-controlled water delivery) that waters crops but not weeds, or something entirely novel and not described here.
  • Reinforcement Learning (RL) (Advanced): For complex, dynamic problems where an AI agent needs to learn to make decisions to optimize an outcome, RL could be considered. For instance, an RL agent could be trained to determine the optimal timing and location for deploying crop protection tools as part of an integrated weed management plan, learning over time from weather patterns, crop growth stages, and previous successes or failures. This approach enables AI systems to go beyond static rules and adapt to changing conditions in the field, making precision agriculture more responsive and efficient.

Optimization Algorithms (often integrated with AI): AI methods can be used to find optimal solutions to complex problems. For example, AI-based tools could help optimize irrigation schedules based on sensor data about soil moisture and crop needs, or determine the most efficient routes and timing for drone-based weed surveillance. These techniques can be particularly useful in resource-constrained environments where maximizing yield with minimal input is essential. By integrating optimization algorithms into their AI pipeline, learners can explore how algorithmic thinking directly supports viability, productivity, and cost-effectiveness in modern agriculture.

This scenario involves a progression of AI applications, moving from classification to analysis and then to potential mitigation .

Distinguishing Crops from Weeds and Analyzing Related Issues

Step 1: Identifying Crops or Weeds

• Concept: This requires Classification using Machine Learning.
• Potential Tools/Approaches
  • Open-source Python libraries for:
    • Image classification tasks (LSTMs - Long Short-Term Memory networks).
    • Data manipulation and cleaning.
    • Numerical operations.
    • Creating graphs and visuals from your dataset.
  • No-Code/Low-Code Platforms are available for:
    • Dataset management and labeling.
    • Creating machine learning pipelines and models using simple interfaces like drag-and-drop.

Step 2: Analyzing Issues Arising from Classification

• Concept: This involves AI-powered data analysis and natural language processing (NLP) to interpret the implications of the classifications.
• Potential Tools/Approaches
  • Large Language Models (LLMs):
    • Input: A set of labeled images of crops and weeds from step 1 and information about your location like the USDA plant hardiness zone, soil conditions, weather conditions, or other useful data about your location.
    • Prompt: Ask the LLM to analyze potential issues. For example: "What are implications of these weeds for my local crop or farming operations?", "What economic consequences could arise?", "How might these weeds affect my neighbors?" LLMs can synthesize information and identify interconnected problems based on their vast training data.
  • Data Visualization Tools (though not strictly AI, crucial for analysis):
    • Use AI tools to visualize identified plants in various images, potentially distribution of crops and weeds over time, and other statistics around your dataset. This can help in formulating better questions for the AI analysis tools.

Step 3: Discovering Ways for AI to Fix Issues

• Concept: This is where AI moves from analysis to prescriptive recommendations and potentially autonomous action. This is often the most complex and research-intensive part.
• Potential Tools/Approaches  (more conceptual and research-oriented)
  • LLMs for Solution Brainstorming:
    • Prompt: "Given the identified weeds in the crops, what AI-driven solutions could be proposed to address these problems?"
    • LLMs can suggest solutions like: recommending specific integrated weed management strategies that will likely be successful given your site conditions and weed concentrations, precision agriculture techniques like targeted crop protection tools that differentiate between crops and weeds, smart irrigation systems (AI-controlled water delivery) that waters crops but not weeds, or something entirely novel and not described here.
  • Reinforcement Learning (RL) (Advanced): For complex, dynamic problems where an AI agent needs to learn to make decisions to optimize an outcome, RL could be considered. For instance, an RL agent could be trained to determine the optimal timing and location for deploying crop protection tools as part of an integrated weed management plan, learning over time from weather patterns, crop growth stages, and previous successes or failures. This approach enables AI systems to go beyond static rules and adapt to changing conditions in the field, making precision agriculture more responsive and efficient.

Optimization Algorithms (often integrated with AI): AI methods can be used to find optimal solutions to complex problems. For example, AI-based tools could help optimize irrigation schedules based on sensor data about soil moisture and crop needs, or determine the most efficient routes and timing for drone-based weed surveillance. These techniques can be particularly useful in resource-constrained environments where maximizing yield with minimal input is essential. By integrating optimization algorithms into their AI pipeline, learners can explore how algorithmic thinking directly supports viability, productivity, and cost-effectiveness in modern agriculture.