Beyond Engagement: How AI and Hope Ignite True Student Agency

5 min Introduction: The Apathy Problem. The moderator frames the national decline in student motivation and the premise that hope is the necessary precursor to agency. Moderator (You) 15 min Hope Science: The Foundation of Motivation. Deep dive into the cognitive assets of hope (willpower + pathways) and how stress/trauma erodes it. Discussion of the Building H.O.P.E. framework. Cathleen Beachboard (Hope Science, SEL) 20 min Agency in Practice: AI and Curriculum Strategy. Discussion on breaking down systemic barriers (pacing, compliance) using AI to create instructional choice. Focus on EduProtocols, and flexible unit design amplified by AI. Christina Miramontes and Bill Bass (Curriculum and Practical Application) 15 min Action Blueprint: Taking the Conversation Back to Your District. Panel guides attendees through creating a simple, scalable plan: identifying key stakeholders, drafting initial communication, and securing buy-in using the hope science data. Moderator/You (AI, Agency Research) & Panelists (Systemic Change) 5 min Audience Q&A and Final Takeaway. Panelists respond to crowd-sourced questions. Attendees receive a "District Conversation Starter" resource. All Panelists

Beachboard, C. (2024). Building H.O.P.E.® Framework. (Book/Framework) The core psychological model defining hope as a cognitive asset and a precondition for achievement and well-being. Snyder, C. R., et al. (1991). The Will and the Ways: Development and Validation of the Children's Hope Scale. Foundational research defining hope in terms of willpower (agency) and pathways (planning), linking it directly to goal achievement. Mollick, E., & Mollick, L. (2023). The Practical Guide to Using AI in the Classroom. (University of Pennsylvania) Framework used to translate AI tools into practical, agentic roles for students (Teammate, Coach, Mirror). Mitra, D. L. (2018). Student Voice and Transformative Curriculum. (Academic Framework) Supports the need to move from passive learning to models where students have ownership over their learning and curriculum (Agency-Based Learning). U.S. Department of Education, Office of Educational Technology. (2023). Artificial Intelligence and the Future of Teaching and Learning. (Report) Contextualizes the national imperative for using AI to create equitable, personalized, and engaging learning opportunities. Heifetz, R. (1994). Leadership Without Easy Answers. (Academic Text) Provides the leadership framework for addressing adaptive challenges—like student apathy—that require shifting values and systems rather than just technical solutions. Doctoral Research (Miami University, 2026). The Impact of Generative AI Use on Student Agency: A Primary Data Study. (Original Research Data) The moderator's completed, original research provides unique data on the link between AI tools and empowered student learning. Hattie, J. (2009). Visible Learning: A Synthesis of Over 800 Meta-Analyses Relating to Achievement. (Academic Framework) Supports the focus on feedback and self-reflection as high-impact instructional strategies that AI can amplify to build agency. National Research Council. (2015). Guide to Implementing the Next Generation Science Standards (NGSS). Provides the curriculum context for Christina Miramontes' expertise, emphasizing inquiry-based learning and student-driven investigation. ISTE Standards for Students: Empowered Learner (1.1). The session is directly focused on helping students leverage technology to take an active role in choosing, achieving, and demonstrating competency in their learning goals.