AI for Educators

Project Motivation

I have long believed that educators hold the power to shape the future and drive societal progress. When equipped with the knowledge and support to harness new technologies, they inspire students to think boldly, question deeply, and transform the world for the better. Over the past six years, I have worked towards addressing and closing socioeconomic gaps in education, convinced that thoughtful AI adoption can serve as a true force for meaningful change. Yet I have also seen how easily AI can be misunderstood or misapplied if teachers are left to navigate it alone.

To address this challenge, I created two connected initiatives—a ten-hour professional development (PD) course and a brief, 20-minute e-learning module—that train teachers to integrate AI effectively while preserving a human-centered classroom. Both projects reflect my lifelong commitment to building practical, equitable educational solutions. My goal is to give educators the structure and guidance they need to help every learner feel seen and engaged in an era of rapidly evolving technology.

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My Role and Goals

I directed every stage of these initiatives, drawing from established instructional frameworks and continuous feedback loops with middle school teachers. Through focus groups, surveys, and interviews, I heard their excitement for AI’s possibilities, alongside a desire for targeted support in areas like ethics, bias mitigation, curriculum integration, and time management. With this in mind, I sought to create hands-on, adaptable resources rather than abstract theory alone. Specifically, I aimed to:

1. Elevate classroom readiness by ensuring teachers can confidently introduce AI tools and strategies that match their actual teaching schedules, resources, and diverse student needs.
2. Embed ethical critical thinking by showing educators how to spot and address AI-related biases and privacy concerns while using student-friendly examples to spark broader discussions on responsible technology use.
3. Reduce barriers to adoption by offering flexible, time-conscious formats so that even the most overextended teachers can access meaningful AI training.
4. Advance equity in learning by demonstrating how AI can augment, rather than replace, good teaching, filling gaps in under-resourced schools to pave the way for greater access and opportunity.

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1) Ten-Hour Professional Development Course Design

Overview

Titled "AI for Educators: Bringing Generative AI to Middle School Classrooms", this multi-week program immerses teachers in foundational AI concepts, classroom applications, and ethical frameworks. Over a dedicated four-month period, I refined this curriculum to serve as a practical roadmap for school districts seeking to integrate AI responsibly. Though it has not been formally implemented in schools yet, I have presented the course design at a departmental showcase, where lively dialogue with educators and researchers underscored its potential.

Key Design Frameworks

• ADDIE and Understanding by Design (UbD): I began with broad objectives, like crafting effective AI prompts and evaluating ethical boundaries, then worked backward to structure each lesson and assessment around those goals.
• Bloom’s Taxonomy: I layered instruction from foundational AI literacy skills to complex tasks such as critiquing real-world AI scenarios. This scaffold helps teachers transfer new insights directly to their classrooms.
• Knowledge-Learning-Instruction (KLI) Model: I distinguished conceptual learning (AI capabilities, ethics) from procedural learning (prompt-crafting, lesson planning). This delineation makes it easier for teachers to practice skills in smaller steps.
• Cognitive Load Theory: Content is purposefully chunked, paired with worked examples and immediate feedback, ensuring that new AI concepts don’t overwhelm or distract from essential teaching duties.
• Reflective Practice: Each session closes with short written reflections, peer feedback, or guided group tasks that encourage ongoing collaboration and self-assessment.

Development Process

I spent four months developing and refining the ten-hour professional development course, beginning with a focused needs assessment that captured teachers’ real-world challenges and hopes for AI. In surveys and one-on-one interviews, I asked educators to pinpoint where they felt least prepared, whether in ethical decision-making, prompt design, or navigating rapid technological changes. Their frank responses shaped our learning goals: cultivate AI literacy, foster an open yet critical mindset, and blend new technology seamlessly with established teaching practices.

From there, I mapped a five-week schedule that combined asynchronous learning with in-person workshops. Each session kicked off with a quick reflective prompt to surface any lingering questions or concerns. These reflections flowed into targeted instruction on AI fundamentals, ethics, and practical classroom applications, followed by hands-on exercises like crafting and critiquing sample prompts. Session wrap-ups incorporated both collaborative activities, such as sticky-note brainstorms and individual takeaways, reinforcing a cycle of learn-practice-reflect. To ensure consistency and depth across various contexts, I built in multiple assessment methods, including surveys, quick-turnaround quizzes, and a culminating group lesson-planning project.

While this program has yet to be fully implemented in a live school setting, I proactively outlined research protocols to maintain integrity and quality. These measures include randomized trials for comparing different instructional approaches, as well as fidelity checks to confirm that each cohort receives the same core experience. By baking in these evaluative structures, I aimed to balance creativity and experimentation with data-driven insights.

Honed Skills

Designing this multi-week program honed my ability to translate big-picture goals into tangible instructional experiences. I drew heavily on learning science principles, particularly when chunking complex AI topics into digestible segments. By using concrete classroom scenarios, I helped teachers see exactly how an AI-related concept might unfold in their unique environment. My iterative, feedback-oriented approach also pushed me to become more adept at cross-stakeholder collaboration. Balancing administrative priorities, teacher feedback, and expert opinions from AI researchers demanded flexibility and a deep commitment to keeping the curriculum both rigorous and accessible.

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2) Condensed E-learning A/B Study

Purpose and Background

In parallel with the longer PD course, I wanted to see if a concise, 20-minute e-learning module could also meaningfully boost teachers’ AI literacy. Inspired by research on e-learning and the potential for quick but potent interventions, I designed an online experience that highlighted prompt-writing fundamentals, ethical considerations, and interactive practice. The module’s brevity aimed to support the many educators who struggle to carve out time for professional development.

Beyond delivering core AI insights, I also used this e-learning study to explore how different question formats might shape learners’ mastery. The result was a controlled experiment that would compare two approaches to reinforcing the same prompt-design content.

Experimental Design

I recruited 21 graduate students with teaching backgrounds as stand-ins for middle and high school educators. Each participant completed a pre-test measuring their baseline understanding of AI capabilities, limitations, and ethical concerns. Next, they worked through segmented modules that combined short videos, immediate feedback loops, and worked examples.

To test the effect of different question styles, I randomly assigned participants to one of two practice formats. The control group answered multiple-choice questions on prompt quality, while the experimental group ranked the same set of prompts from best to worst. This ranking condition drew on the idea of “productive struggle,” a principle suggesting that tasks requiring deeper thought (like comparative judgment) can help learners internalize the nuances of strong prompt design.

Findings and Insights

Both groups demonstrated substantial gains between the pre- and post-test, suggesting that even a brief, well-structured module can enhance AI literacy. The ranking-based group averaged a 28.4% improvement in post-test scores, compared to 10.5% in the multiple-choice group, though the difference was not statistically significant given the small sample size.

Qualitative feedback reinforced the quantitative results. Participants described the ranking tasks as more challenging yet more rewarding, and they frequently cited the immediate feedback as a key motivator. Several noted how they planned to adapt similar strategies for their own classrooms, reinforcing the module’s practical value.

These findings underscore that short, targeted e-learning interventions, particularly those leveraging elements of comparative judgement, can equip educators to engage with AI more responsibly and confidently.

Honed Skills

Running this e-learning study expanded my capacity to design and interpret mixed-methods research. I became more comfortable analyzing quantitative gains alongside qualitative feedback, ensuring I captured both the efficacy of the intervention and the reasons behind it. Through this process, I sharpened my focus on educational equity by weaving in discussions of bias, data privacy, and responsible AI use. My overarching aim was, and continues to be, creating learning opportunities that are accessible, inclusive, and impactful, especially for under-resourced schools.

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Conclusion

My two connected projects (a multi-week professional development course and a concise e-learning module) reflect a broader philosophy: that teachers need holistic, ethically grounded support, that extends beyond quick tips or trend-driven tech. By weaving learning science together with hands-on classroom perspectives, I set out to create tools and instruction that empower educators in a rapidly shifting AI landscape.

Additionally, this work builds on my six-year drive to narrow socioeconomic divides in education. Every interview with a time-pressed teacher or a hesitant principal reminded me why I remain so invested in thoughtful AI adoption. Technology alone won’t close achievement gaps; intentional design and inclusive implementation will. If AI is to become a bridge rather than a barrier, educators deserve the training and the confidence to guide students responsibly. I hope these projects serve as a meaningful step toward realizing that vision, aligning with the belief that truly impactful AI supports human creativity, compassion, and progress for every learner.