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As AI fundamentally shifts how we work and create, it is also completely redefining how we teach and learn.

The Minds & Machines Hackathon invites participants to create AI-powered solutions for teaching and learning to help shape the future of education and workforce development. With a focus on leveraging data science,  machine learning,  and AI, participants will create tools and models to tackle real-world issues in the classroom.

Ready to build the future of education?

Register to participate

 

We are also seeking industry mentors and education experts to guide project development, judges to evaluate real-world impact, and community sponsors to fuel our builders and back our awards. Contact Madeline Burton for more information on how to get involved.

Event Structure

  • Duration: 48-hour hackathon
  • Location: This is a hybrid event. Join us online or at the Warnock Engineering Building.
  • Team Size: 3-5 members per team, including at least 2 students and 2 Utahns
  • Registration Closes: August 31, 2026
  • Enablement Kickoff: September 1, 2026
  • Project Proposals Due (optional): September 8, 2026
  • Competition Dates: September 15-17, 2026
  • Hackathon Kickoff: September 15, 2026, 3:00 pm
  • Project Presentations: September 17, 2026 3:00-5:00 pm
  • Awards Ceremony: September 18, 2026, 10–11 am

Eligibility & Support

This event is targeted toward Utah college students, but all are welcome. No coding experience needed! Out-of-state registrants will be teamed with at least two Utahns.

Each team will be supported by subject matter experts (SMEs) to support projects both in terms of domain expertise and technical expertise. AWS Solution Architects will be available for consult throughout the event. Teams will meet with their domain SME prior to the hackathon to help scope out the project. SMEs will also be available during the event to support emerging questions. Interested SMEs should email Madeline Burton.

Awards

Cloud credit and cash prizes will be awarded to the top three teams:

  • 1st Place: $1,500 cash, $1,000 AWS credit
  • 2nd Place: $1,000 cash, $500 AWS credit
  • 3rd Place: $500 cash, $500 AWS credit

AWS Enablement and Pre-Planning Sessions

In the weeks prior to the hackathon, Amazon Web Services (AWS) is offering virtual enablement sessions to provide you with the skills you need to participate in the hackathon. RSVPs are required to ensure there’s enough interest (at least 20 participants) to hold each session.

​Workshops will be held on Tuesday and Thursday afternoons and will include lectures, hands-on training, and Q&A with relevant experts.

Prerequisites: None. All tools and environments are provided. A laptop with a web browser is all that is needed. Attendance at Sep 3 is recommended before Sep 8 but not required.

Workshop environment: Building with Amazon Bedrock

  • Get introduced to the hackathon structure, explore real-world challenges in education, and discover what is possible with cloud-powered AI. Meet your organizers and mentors, dive deep into the three challenge tracks (Teach, Learn, and Collaborate), and get a high-level introduction to AWS services.

    What participants will experience:

    • Orientation to the hackathon, communication platforms, and team mechanics
    • Deep dives into the challenge tracks led by SMEs
    • High-level introduction to AWS Cloud and Generative AI capabilities. 
    • Introduction to Amazon Bedrock and the concepts of foundation models.

    After this session, participants will be able to:

    • Navigate the event schedule, logistics, and get-help resources smoothly
    • Identify an educational challenge and select their target track(s)
    • Define a high-level project concept to prepare for hands-on building in Session 2

    Please note that registering for this enablement session does not automatically register you for the main event.

  • Learn the core tools for building AI applications. Through hands-on exercises, participants will interact with foundation models, learn prompt engineering techniques, and build an interactive chatbot with a web interface. No prior AWS or coding experience required.

    What participants will experience:

    • Introduction to generative AI and Amazon Bedrock
    • Send prompts to AI models and observe how different instructions change the output
    • Connect an AI model to external data using function calling
    • Build a multi-turn chatbot with a Streamlit web interface
    • Track breakout: apply text generation to your challenge track (Teach, Learn, or Collaborate)

    After this session, participants will be able to:

    • Call AI foundation models through the Bedrock API
    • Write effective prompts and control model behavior
    • Build a basic interactive AI application with a web front-end

    Please note that registering for this enablement session does not automatically register you for the main event.

  • Build an AI application grounded in real documents. Participants will create a knowledge base from course materials, build a chatbot that answers questions and cites its sources, and configure responsible AI guardrails. The session concludes with track-specific building time.

    What participants will experience:

    • Introduction to Retrieval-Augmented Generation (RAG)
    • Upload documents, create a knowledge base, and ask questions against it
    • Build a chatbot that answers from your documents and cites sources
    • Configure AI safety guardrails (content filters, denied topics)
    • Track breakout: start building your hackathon prototype

    After this session, participants will be able to:

    • Build AI applications grounded in real-world documents
    • Implement responsible AI safeguards
    • Architect an application pattern suitable for their hackathon project

    Please note that registering for this enablement session does not automatically register you for the main event.

  • Synthesize the technical skills and domain insights gathered from previous workshops to solidify your team's hackathon strategy. Collaborate with your teammates to draft a structured one-page project proposal. Receive direct feedback from AWS Solutions Architects to prepare for the beginning of the hackathon.

    What participants will experience:

    • A collaborative session to draft a detailed one-page overview of your proposed project.
    • One-on-one check-ins with technical coaches to map out how your project can easily connect to AI tools.

    After this session, participants will be able to:

    • Produce a clear, simple one-page project proposal detailing your AI-driven educational solution.
    • Understand the basic flow of how your application will use Amazon Bedrock to bring your ideas to life
    • Coordinate development tasks effectively to begin building immediately at hackathon kickoff

    Please note that registering for this enablement session does not automatically register you for the main event.

How Enablement Sessions Connect to Hackathon Challenge Tracks

  • Teach: Generate lesson plans, rubrics, and feedback grounded in curriculum standards
  • Learn: Build AI tutors that answer from course materials and adapt to the student
  • Collaborate: Create assistants that summarize group work and coordinate team projects

Challenge Tracks

Teams will work on projects aligned with one or more of our three challenge tracks to develop innovative data-driven solutions.

Track 1:
Teach
Build tools that combat administrative burnout, generate dynamic lesson plans, provide real-time grading insights, and help educators design personalized learning materials at scale.
Track 2:
Learn
Prototype adaptive AI tutors, gamified study agents, or multimodal learning tools that tailor complex subjects to an individual student's pace, language preference, and learning style.
Track 3:
Collaborate
Design intelligent platforms that optimize group projects, facilitate cross-disciplinary peer mentoring, or use AI to bridge communication gaps between students, parents, and schools.

Key Considerations for Responsible Solutions & Judging Criteria

  • Ensure the data sources are reliable and have sufficient historical records for training AI models.

  • Address privacy and ethical concerns, especially if using data that could identify individuals or bias grading or learning.

  • Encourage teams to include members with diverse expertise, such as computer scientists, social scientist, and educators.

  • Involve local communities and stakeholders to ensure the solutions are practical and address real-world needs.

  • Consider how the solutions can be scaled and sustained over time, including potential partnerships with local governments or organizations.

  • Potential to make a meaningful difference in teaching and learning.

  • Originality and creativity in the approach and solution design.

  • Integration of advanced AI/ML techniques and data sources.

  • Quality of the final presentation, including clear articulation and demonstration of the solution.

  • Practicality and scalability of the proposed solution in real-world applications.

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