Dissertation-in-Practice Concept — _AI: Doing School Differently_
June 27, 2025
dissertation in practice ChatGPT research
1. Purpose
Leverage artificial-intelligence tools not to make today’s schooling faster, but to redesign it around deeper learning moments (“light-bulb moments”) for every student. The dissertation-in-practice will culminate in a district-ready AI implementation blueprint and professional-learning program that helps educators shift from efficiency gains to transformational practice.
2. Problem Framing
- Most districts adopt generative AI for lesson prep, grading or communications—uses that streamline existing routines but rarely change pedagogy.
- Teachers remain over-stretched; students’ passions and agency remain under-served.
- Research on AI in K-12 is expanding, yet guidance on implementation for transformational learning is thin.
3. Literature Review Focus
Do podcast interviews reviewing the literature with the authors themselves. Talk to different folks as it relates to their topic of interest. Then do a summary podcast gathering that info and sharing additional insight.
| Strand | Key Questions |
|---|---|
| Current AI Use in K-12 | How are districts, principals, and teachers deploying generative and predictive AI today? What measurable outcomes (achievement, engagement, workload) are reported? |
| Design Principles for Human-Centered AI | What do cognitive-science and ethics literatures tell us about co-agency, cognitive load, bias, data privacy and student well-being? |
| Change Management & Professional Learning | Which models (T3, SAMR, design-based implementation) best support sustained instructional change? diffusions of innovation. |
Gaps identified here will shape the blueprint’s design requirements.
4. Dissertation-in-Practice Product
| Component | Deliverable |
|---|---|
| AI Implementation Blueprint | • District self-assessment• Vision-setting protocol (AI for Light-Bulb Learning ), Phased rollout plan (policy, infrastructure, Personalized Learning, evaluation) |
| Modular Professional-Learning Series | Three half-day workshops + 3 Minute Masterclasses: 1. Rethinking Learning with AI (mindset & vision) 2. Designing Student-Centered AI Workflows (lesson redesign, prompt engineering) 3. Measuring Impact & Iterating (data dashboards, student voice) |
| Pilot & Improvement Cycles | Co-design with two partner districts; collect mixed-methods data; iterate modules (design-based research approach). I’m still pretty sketchy on this part |
5. Methodology & Research Questions
Design-based research (McKenney & Reeves) will guide iterative design, enactment, analysis and redesign of the training.
RQ1: How does the blueprint influence educators’ beliefs and practices regarding AI-enabled student-driven learning?
RQ2: What barriers/supports emerge during district implementation, and how can the blueprint be refined to address them?
Data: workshop artifacts, pre/post surveys, classroom observations, focus groups, usage analytics.
6. Foundation in Student-Driven Learning (SDL)
The work draws directly on my established 5-Stage SDL Framework—progressing from basic Voice & Choice to fully Student-Driven experiences with structural “white space” for self-directed projects .
Key SDL principles (enrollment, real audience, impact focus) provide the pedagogical lens through which AI tools will be selected and deployed. For example:
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AI tutors free time for afternoon passion-based projects (cf. Alpha School model).
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Generative AI supports rapid feedback, letting teachers coach higher-order inquiry.
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District policies align assessment and scheduling to SDL’s emphasis on self-actualization and authentic audience .
7. Anticipated Contributions
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Practical: Ready-to-use toolkit districts can adopt within a semester.
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Scholarly: Design principles for AI-supported student-driven learning; empirical data on implementation conditions.
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Personal/professional: Positions me—and the program—at the forefront of AI-enabled school transformation.
8. Next Steps
| Task | Date |
|---|---|
| Draft detailed prospectus (& refine RQs) | 15 July 2025 |
| Complete preliminary lit-review matrix | 01 August |
| Secure partner districts & IRB | August–September |
| Pilot Workshop #1 | October |
| Data collection & iterative design cycles | Nov 2025 – Mar 2026 |
| Final write-up & defense | Spring 2026 |
I welcome your feedback on scope, methodology, and deliverables. Once approved, I will flesh out research instruments and begin district outreach.
Thank you for encouraging me to “push the boundaries”—this project truly aligns with my passion for redesigning school around meaningful, student-driven learning enhanced by AI.