Empowering Teachers and PLC Leaders with AI for Effective Action Research

Outline

Title:
Empowering Action Research with AI: Enhancing Classroom and PLC Effectiveness

Content and Engagement:
Participants will learn how to integrate AI tools into action research, focusing on the use of effective prompting to gather and analyze classroom data without the need for costly AI tools. The session will be hands-on and highly interactive, guiding participants through building custom AI prompts, collecting data, and developing actionable strategies for improvement.

Outline:
1. Introduction to AI and Action Research (10 minutes)
Content:
Overview of the action research cycle (plan, act, observe, reflect) and how AI enhances each stage.
Emphasis on effective prompting using free AI tools for data collection and analysis.
Engagement:
Participants will answer a poll to identify their current challenges in classrooms or PLCs.
Present an example of AI being used to streamline data collection and research.
Process:
Use live polling to identify common challenges and engage the audience right from the start, connecting theory to real-world application.

2. Designing AI-Enhanced Action Research Projects (25 minutes)
Content:
Step-by-step guide to developing action research projects using AI prompts for gathering data and generating insights.
Focus on creating research questions and identifying how AI can support data-driven decision-making.
Engagement:
Hands-on activity: Participants will draft a personalized research question based on their classroom or PLC challenge.
Peer-to-peer discussion groups: Share research questions and get feedback on how to incorporate AI effectively.
Process:
Small group discussions and collaboration through real-time feedback from peers, ensuring participants learn from one another's experiences.

3. Using AI Prompts for Data Collection and Analysis (30 minutes)
Content:
Demonstrate how to use free AI tools to gather and analyze data through effective prompts.
Highlight how AI can identify patterns in student behavior, academic performance, or engagement with minimal manual effort.
Engagement:
Live demo: Walk participants through crafting prompts for data collection and analysis.
Hands-on activity: Participants will input data or scenarios into an AI tool (like GPT-4) and practice generating insights using AI prompts.
Process:
Real-time testing of AI-generated responses to showcase the power of prompt engineering for solving specific educational challenges.

4. Developing the Action Plan and Monitoring Impact (20 minutes)
Content:
Guide participants in creating an AI-driven action plan that uses real-time data to measure success and monitor the impact of interventions.
Focus on setting metrics for success (e.g., student engagement, performance improvements).
Engagement:
Participants will develop an action plan using prompts to guide decision-making and assess outcomes.
Peer review: Participants will share their plans in small groups and provide feedback.
Process:
Structured peer-to-peer interaction where participants critique and refine their action research projects.

5. Reflection and Next Steps (10 minutes)
Content:
Reflect on how AI can enhance action research and make data-driven teaching more accessible and efficient.
Discuss the next steps for implementing AI-enhanced strategies in participants' own classrooms or PLCs.
Engagement:
Participants will share key takeaways through a digital whiteboard (e.g., Padlet) or live chat.
Collect final thoughts on how AI can drive meaningful classroom improvements.
Process:
Reflection activity where participants engage with the group, summarizing what they've learned and how they plan to apply it.

Time Breakdown:
Introduction to AI and Action Research – 10 minutes
Designing AI-Enhanced Action Research Projects – 25 minutes
Using AI Prompts for Data Collection and Analysis – 30 minutes
Developing the Action Plan and Monitoring Impact – 20 minutes
Reflection and Next Steps – 10 minutes

Engagement Tactics:
Live polling and real-time Q&A to gauge participant challenges and keep the content relevant.
Hands-on activities where participants create their own AI prompts and action plans, applying theory to practice.
Peer-to-peer interaction in small group discussions, allowing participants to share ideas and feedback.
Device-based exercises where participants test prompts and AI-generated insights in real time.
Group reflection to summarize learning outcomes and share action steps.

Supporting research

1. "Artificial Intelligence and the Future of Teaching and Learning: Insights and Recommendations" (U.S. Department of Education, Office of Educational Technology)

2. "Action Research for Professional Development: Promoting Professional Learning in Schools" (International Journal of Educational Research)

3. "AI in Education: Challenges and Opportunities for Teaching and Learning" (Learning Policy Institute)

4. "The Role of AI in Data-Driven Teaching: A Guide for Educators" (EdTech Research Group)

5. "Professional Learning Communities at Work: Best Practices for Enhancing Student Achievement" (Solution Tree)