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Service-Learning and AI

Boise State Service-Learning is at the forefront of integrating AI into planning and implementation.

What is AI Service-Learning?

Artificial Intelligence (AI) Service-Learning is an educational approach that integrates artificial intelligence (AI) tools and methodologies with service-learning principles. This framework combines hands-on AI application with real-world community engagement, where students work on projects that apply AI to address specific community needs or issues. Through AI service-learning, students can deepen their understanding of AI tools and technologies while developing critical thinking, ethical reasoning, and social responsibility.  Students can apply AI in a way that benefits local organizations, communities, and/or underserved populations. (Sass, 2024)

AI Service-learning is broken down into distinct phases that creates a process of collaboration, reflection and improvement.  Here’s a step-by-step process that integrates these components:

Phase 1: Community Needs Analysis

  1. Establish Partnerships: Begin by identifying and connecting with community organizations, leaders, and stakeholders to understand their current challenges and needs. Generative AI can assist with this.  
  2. Conduct Preliminary Research: Collect data about the community, including demographic, economic, and social information, to get a contextual understanding. Generative AI can assist with this. 
  3. Document Findings: Summarize key findings, including specific needs and possible AI solutions, and share them with both community members.  Confirm that these are the correct needs identified. 

Phase 2: AI Literacy and Training

  1. Assess Baseline Knowledge: Conduct a brief survey or assessment to determine the AI knowledge levels of both students and community members.
  2. Develop Training Materials: Create accessible training resources, such as workshops, online tutorials, or in-person sessions, covering the basics of AI, data literacy, and the ethical implications of AI applications.
  3. Provide Hands-On Training: Organize interactive sessions where participants can explore AI tools, experiment with datasets, and understand basic concepts like machine learning, natural language processing, or data privacy.
  4. Evaluate Understanding: Assess participants’ learning through quizzes, reflections, or skill demonstrations, ensuring they’re ready to proceed to the project phase.
  5. Encourage Ongoing Learning: Offer optional advanced resources or learning modules for those interested in diving deeper, ensuring continuous engagement with AI topics.

Phase 3: Project-Based Learning

  1. Define Project Goals: Collaborate with community partners to define clear project objectives, focusing on tangible outcomes that address the needs identified in Phase 1.
  2. Develop a Project Plan: Outline project timelines, tasks, roles, and milestones. Ensure each team member understands their responsibilities and deliverables.
  3. Implement and Iterate: Begin work on the AI solution, encouraging teams to iterate based on feedback from stakeholders. Schedule regular check-ins to monitor progress, address challenges, and ensure alignment with community goals.

Phase 4: Reflective Practice

  1. Establish Reflection Points: Set up regular reflection sessions at key points in the project, such as after completing training, midway through the project, and at the end.
  2. Incorporate Ethical Reflections: Include prompts about ethical challenges, such as data privacy or bias, and ask participants to consider how they are addressing these issues within the project.

Phase 5: Evaluation and Feedback

  1. Define Evaluation Criteria: Establish specific metrics for assessing both student learning outcomes and community impact, such as AI tool usability, community satisfaction, and student skill development.
  2. Analyze Results: Review data collected from surveys, assessments, and project deliverables to evaluate the overall success of the project.
  3. Identify Areas for Improvement: Use the feedback to identify strengths and weaknesses in both the project design and the collaboration process. Focus on areas such as communication, technical skills, and community engagement.
  4. Share Findings and Celebrate Success: Present the findings to both students and community members, highlighting successes, challenges, and recommendations for future projects. This celebration fosters a sense of achievement and encourages ongoing collaboration.
  5. Plan Next Steps: Based on the evaluation, decide on potential follow-up projects, adjustments in training or project design, and any additional support that could strengthen future AI service-learning initiatives.

By following this process, you ensure a structured approach that emphasizes both learning and impact, while creating a sustainable model for integrating AI service-learning into community engagement projects.

Models for Community-AI Collaboration

  • Participatory Design Model: Engage community members directly in the design and development of AI tools, ensuring that solutions meet actual needs and are user-friendly.
  • Service Provider Model: Position AI tools as resources that serve existing community organizations by enhancing their capacity to deliver services.
  • Capacity-Building Model: Educate community members on AI, enabling them to eventually maintain and even develop AI solutions independently.
  • Consultative Model: Act as AI consultants, providing expertise to community partners while they retain control over decision-making and implementation