Stage Presentations (2):
Stage Presentation 1: The Big Three Learning Science Principles That Transform AI Integration
This presentation examines three high-impact, evidence-based practices: cognitive load management, spaced retrieval practice, and productive struggle. It also demonstrates how artificial intelligence (AI) can enhance these principles when used strategically. Educators will see specific examples of AI applications that genuinely improve student learning. Additionally, the presentation highlights examples of AI that can undermine student learning, providing immediate takeaways for classroom implementation using free tools.
Stage Presentation 2: Teaching Generation Alpha: Brain Science Meets Workforce Preparation
Explore what neuroscience teaches us about educating students born between 2010 and 2024, and how to prepare them to be confident participants in an AI-assisted workforce. This session will focus on Generation Alpha's unique characteristics, such as digital fluency, shorter attention spans, and visual learning preferences. It aims to foster mastery of core content while also equipping students with the skills to recognize embedded AI, make intentional choices about its use, and critically evaluate AI-generated content. These transferable skills are essential for success across all career paths.
Playground Stations:
Station 1: Cognitive Load Optimization Lab
Participants will learn to identify sources of extraneous cognitive load, such as cluttered slides, excessive text, and unclear instructions. They will then redesign their lessons using free AI tools like ChatGPT or Gemini to create simplified explanations, visual organizers, and step-by-step scaffolds. By the end of the session, teachers will receive a Cognitive Load Checklist and AI prompt templates to help reduce overwhelm while maintaining an appropriate level of challenge.
Station 2: Spaced Practice & Retrieval Blueprint
This station trains educators to develop spaced practice routines using simple calendar templates and AI-generated quiz questions. This approach enhances long-term memory while minimizing grading workload, based on Ebbinghaus's forgetting curve and Bjork's research on the spacing effect.
Station 3: Productive Struggle Prototyping Workshop
Educators create "productive struggle supports" to keep students engaged without giving them direct answers. This approach specifically targets Generation Alpha students, who may be inclined to seek immediate solutions from AI. The aim is to teach strategies that maintain a sense of challenge while fostering independence and problem-solving skills.
Station 4: Learning WITH AI vs. Creating WITH AI Decision Framework
Participants learn to distinguish helpful from harmful AI use through a practical decision framework: Does this preserve student thinking? Does it provide practice, not answers? Does it build independence? Teachers evaluate three common classroom AI scenarios, redesign activities where AI complements rather than replaces student work, and create student-facing guides for responsible AI use that prepare them for workforce contexts where AI is embedded in everyday tools.
Station 5: Instant Feedback Systems for Formative Assessment
This station demonstrates how to set up feedback loops using free AI chatbots to generate varied practice problems, provide immediate responses to student work, and suggest next steps—allowing teachers to focus on high-value interactions while students receive timely guidance.
Station 6: Generation Alpha Learning Design Adaptation
Educators audit current lessons through a "Generation Alpha lens" examining whether instruction leverages visual/multimodal learning, includes social interaction, connects to real-world relevance, and builds both content knowledge AND AI literacy. Teachers redesign one lesson element using AI to generate contemporary examples, visual supports, or collaborative protocols that work with Gen Alpha strengths while developing deep thinking skills essential for college and career success.
Stations 7-10: Open slots for additional practitioner-led sessions
Potential topics based on presenter proposals include: AI-resistant assignment design, interleaving practice strategies, metacognitive reflection prompts, discipline-specific applications (such as history, math, science, and ELA), the neuroscience of adolescent learning, equity considerations in AI use, and executive function scaffolding for diverse learners.
After this playground, participants will be able to:
1. Reduce cognitive overload in lessons by identifying sources of extraneous load (cluttered slides, excessive text, unclear instructions) and redesigning using free AI tools like ChatGPT or Gemini to create simplified explanations, visual organizers, and step-by-step scaffolds.
2. Distinguish "learning with AI" from "creating with AI" through practical classroom examples: using AI to generate practice problems builds skills, while using AI to complete assignments bypasses learning. Participants will design activities where AI complements student thinking rather than replacing it.
3. Prepare students to be confident participants in the AI-assisted workforce by teaching them to recognize when AI is helping them, even when it's integrated into tools. They should learn to make intentional choices about when to use AI features and critically evaluate AI-generated content. These skills are essential and transferable across all careers.
4. Create "productive struggle supports" such as hint cards or guiding questions that keep students engaged without providing solutions. These strategies are particularly effective for Generation Alpha, who may often default to asking AI for immediate answers.
5. Set up instant feedback loops using free AI chatbots to generate varied practice problems, provide immediate responses to student work, and suggest next steps. This approach allows teachers to focus on high-value interactions while students receive timely guidance.
6. Develop a summer implementation plan by selecting 2-3 specific strategies to pilot in the fall. Options include starting daily retrieval practice, using AI for challenging concepts, or designing assignments that incorporate AI. Ensure the plan consists of clear steps, free tools, and measurable success indicators.
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Tabibian, B., Upadhyay, U., De, A., Zarezade, A., Schölkopf, B., & Gomez-Rodriguez, M. (2019). Enhancing human learning via spaced repetition optimization. Proceedings of the National Academy of Sciences, 116(10), 3988–3993. https://doi.org/10.1073/pnas.1815156116 (MPG.PuRe)
Gkintoni, E., Antonopoulou, H., Sortwell, A., & Halkiopoulos, C. (2025). Challenging cognitive load theory: The role of educational neuroscience and artificial intelligence in redefining learning efficacy. Brain Sciences, 15(2), 203. https://doi.org/10.3390/brainsci15020203 (MDPI)
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Baker, A. E., Galván, A., & Fuligni, A. J. (2025). The connecting brain in context: How adolescent plasticity supports learning and development. Developmental Cognitive Neuroscience, 71, 101486. https://doi.org/10.1016/j.dcn.2024.101486 (PubMed)
Wang, J., & Fan, W. (2025). The effect of ChatGPT on students’ learning performance, learning perception, and higher-order thinking: Insights from a meta-analysis. Humanities and Social Sciences Communications, 12, 621. https://doi.org/10.1057/s41599-025-04787-y (Nature)
Klarin, J., Hoff, E., Larsson, A., & Daukantaitė, D. (2024). Adolescents’ use and perceived usefulness of generative AI for schoolwork: Exploring their relationships with executive functioning and academic achievement. Frontiers in Artificial Intelligence, 7, 1415782. https://doi.org/10.3389/frai.2024.1415782 (Frontiers)
Fleckenstein, J., Liebenow, L. W., & Meyer, J. (2023). Automated feedback and writing: A multi-level meta-analysis of effects on students’ performance. Frontiers in Artificial Intelligence, 6, 1162454. https://doi.org/10.3389/frai.2023.1162454 (PMC)
U.S. Department of Education, Office of Educational Technology. (2023). Artificial intelligence and the future of teaching and learning: Insights and recommendations. https://www.ed.gov/sites/ed/files/documents/ai-report/ai-report.pdf (ed.gov)
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