Student Agency to Professional Impact and Growth For All

Outline

I: Time, Content, and Engagement Process
The Problem and the Promise: Saving Time, Gaining Clarity
5 Minutes

Quick Poll & Device-based Activity: 🙋‍♂️
Content: The "Perception Gap" (teacher intent vs. student reality). Introduce the core problem: lack of planning time and lack of student clarity are the biggest blockers. Introduce the solution: AI for planning efficiency + Student Check-in for agency.
Attendees use a quick, device-based poll (e.g., Mentimeter or a single Google Form question) to answer: "How often do you feel completely clear on your lesson's impact before receiving test scores?" (Establishes the need for real-time data).

II: Time, Content, and Engagement Process
10 min

AI-Powered Clarity & Time Savings
Live Demo & Partner Prompting: 💻

Content: Step-by-step demonstration of using AI to rapidly generate the foundation of a lesson: standards-based learning targets, a learning map, key vocabulary, and the "Teacher Learning Pack" (pedagogical strategies, guiding questions, discussion starters). Emphasize time saved (Outcome 1, 3).

Live Demo : Present a complex standard and show the instant, high-quality output using a strategic AI prompt. Partner Activity: Attendees use a provided "Prompt Guide" on their devices to generate a learning target and key vocabulary for a standard they teach. They immediately experience the time savings.

III: Time, Content, and Engagement Process
20 min

Student Agency & Gamification
Simulation & Peer-to-Peer Review: 🤝

Content: How to translate the AI-generated learning targets into a simple, digital student check-in process. Model how this gives students the agency to self-assess (Outcome 2). Discuss how tracking progress against clear targets helps to "gamify" the learning experience (Outcome 5).
Simulation Activity: Attendees access a sample check-in form (using a QR code) and role-play as a student, giving themselves feedback and feedforward thoughts against the clear target. Peer
IV: Discussion: "How does the simplicity of this check-in make it more powerful than a traditional exit ticket?"

V: Time, Content, and Engagement Process
10 min

Part 3: Equity, Customization, & The Learning Thrill
Mini-Design Challenge & Group Share: 🚀

Content: Analyzing the student check-in data. Using the AI tool for equitable customization (instant translation and reading level refinement) to meet diverse learner needs (Outcome 4). Strategies for using the "Teacher Learning Pack" (AI-generated pedagogy) to address the specific needs identified by the students, transitioning learning from "skill work" to "learning thrill" (Outcome 5).
Mini-Design Challenge: Attendees are given a scenario (e.g., student data shows high confusion on a target). In small groups, they must use the AI-generated "Teacher Learning Pack" to select the best pedagogical practice to address the need. Group Share : Two groups share their chosen strategy and the rationale, focusing on the speed of implementation.

VI: Time, Content, and Engagement Process
10 minutes

Synthesis and High-Impact Takeaways
Wrap-up & Commitment: ✅

Content: Final synthesis of the full cyclical process (AI Clarity -> Student Agency -> Data-driven Thrill). Reiterate the key benefit: saving time while increasing impact.
Device-based Activity : QR code/link provided for attendees to download the full data report and sample teacher packs, and learning maps.

VII: Time, Content, and Engagement Process
5 min

Final Q&A.

Outcomes

After this session, participants will be able to:
1. Utilize AI to rapidly generate and customize core instructional tools—including standards-based learning targets, a learning map, and key vocabulary—drastically saving planning time and immediately boosting teacher clarity.

2. Create a "Teacher Learning Pack" using targeted AI prompts that include key pedagogical practices, guiding lesson questions, and student discussion strategies to optimize instruction.

3. Customize all AI-generated content by instantly translating materials into numerous languages or refining the text for lower reading levels, ensuring equitable access and clarity for every learner.

4. Model and implement a digital student check-in process that uses the AI-generated learning targets as the foundation for self-assessment, giving students the agency to track their own progress and drive their learning.

5. Understand how to use the feedback and "feedforward" systems based on student self-assessment data to provide actionable insights on learning status, which helps to "gamify" the learning journey and move it from isolated skill work to genuine learning thrill.

6. Take Away the complete, actionable process to dramatically save planning time, significantly increase clarity, tangibly improve student agency, and fundamentally turn students on to driving their own learning.

Supporting research

1. Sharratt, L. (2019). CLARITY: What Matters MOST in Learning, Teaching, and Leading. Corwin.
Focus: This book provides the foundational research for achieving clarity across all levels of a school system, directly supporting the need for explicit learning intentions and shared accountability (the core of your AI-generated targets).

2. Cupolo, D. (2024). Designing for Learning Thrill. [Insert accessible/public URL here].
Focus: Your core framework that connects the clear design of learning to student agency and the intrinsic motivation required to achieve a "learning thrill".

3. Hattie, J. (2017). Visible Learning: Feedback. Routledge.
Focus: The power of teacher clarity and high-quality feedback (or feedforward) as two of the most significant factors for student achievement, linking directly to the AI-driven planning process.

4. Mithas, S., & Gutt, T. (2020). Digital Transformation and the Power of AI: Leveraging Technology to Drive Innovation and Growth.
Focus: Supports the session's premise that AI can drastically increase efficiency (saving time) and improve the quality of foundational processes (instructional planning).

5. Dweck, C. S. (2006). Mindset: The New Psychology of Success. Random House.
Focus: Supports the value of student self-assessment and agency (the student check-in) in promoting a growth mindset, where students view learning as an iterative process driven by effort and feedback.

6. Fullan, M. (2016). Coherence: The Right Drivers in Action for School Improvement. Corwin.
Focus: Supports the need for a systemic, coherent approach to improvement, which is provided by the clear, aligned AI-generated content used across classrooms.

7. Fisher, D., Frey, N., & Hattie, J. (2018). Developing Assessment-Capable Visible Learners, Grades K–12: Maximizing Skill, Will, and Thrill. ASCD.
Focus: Provides the pedagogical link between student self-assessment, understanding where they are (feedback), and knowing where they go next (feedforward), essential to the student check-in process.

8. Blannin, J., Wood, C., Stubbs, P., et al. / Hattie, J. (2023). Informing professional learning interventions with evidence-based analysis of student feedback: implications for software use and learning clarity. Monash University.
Focus: Directly supports the session's core concept: using student feedback (student voice data) and technology to inform and enhance both professional learning interventions and learning clarity.
9. Goleman, D. (2006). Emotional Intelligence. Bantam.
Focus: Supports the session's inclusion of social-emotional needs in the student check-in data, as self-assessment and reflection are key components of emotional literacy.

10. Sharratt, L., & Fullan, M. (2012). Putting FACES on the Data: What Great Leaders Do! Corwin.
Focus: Supports the practice of using specific student data to drive instructional and leadership decisions, directly challenging the "perception gap" by making the reality of student needs visible.