Participate and share: Interactive session 
Streaming Session
Recorded Session
Participant outcomes in this session are as follows:
Learn bias mitigation techniques and natural language processing models that promote diversity and inclusion.
Develop an acute linguistics approach to effectively utilizing a Large Language Model platform
Develop sample lesson plans and instructional strategies for responsibly integrating AI through various prompts, including idea, feedback, exploratory, segmented, instructional, and the building blocks to generate a Super Prompt.
Leave equipped with concrete plans and strategies to integrate AI equitably to meet the needs of all learners.
Recognize common educational and infrastructure challenges around equitable access to AI technologies.
Enhance their own digital fluency in AI systems and ethical implementation.
Introduction (5 mins)
Pre-assessment of AI knowledge, more specifically, Large Language Models
AI Landscape (10 mins)
Overview of major AI systems and capabilities
Bias Mitigation (15 mins)
Content: Techniques to mitigate bias in the results and promote inclusion
Process: Small group discussion, hands-on activities
Outcomes: Learn bias mitigation techniques using natural language processing models
Hands-On Prompting (30 mins)
Content: Crafting effective prompts for Large Language Model Systems
Process: Small groups draft prompts for different instructional strategies
Outcomes: Develop sample prompts and strategies for integrating AI responsibly. Acquire an acute linguistics approach to utilizing large language models. This section will include scenario-based context.
Ethics Conversation (10 mins)
Content: Responsible and equitable implementation
Implementation Planning (15 mins)
Content: Develop plan for classroom integration
Process: Individual reflection and goal setting
Outcomes: Leave with concrete plans to integrate AI equitably using prompts and strategies learned.
Wrap-Up (5 mins)
Artificial Intelligence and the Future of Teaching and Learning, United States Department of Education
Mitigating Bias in Artificial Intelligence: An Equity Fluent Leadership Playbook, Haas School of Business, University of California, Berkeley
Artificial Intelligence in Education: Addressing Ethical Challenges in K-12 Settings. Selin Akgun and Christine Greenhow
AI For Educators, Matt Miller
Race After Technology: Abolitionist Tools for the New Jim Code, Ruha Benjamin
Weapons of Math Destruction, Cathy O'Neill
Algorithms of Oppression, Safiya Noble
Unmasking AI, Joy Boulamwini
The Algorithmic Justice League, website
Coded Bias, movie
The Coded Gaze, documentary
Return
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