552.
Digital Media Production
Introduction to eLearning Project
The topic of this micro eLearning project is the integration of Artificial Intelligence (AI) into classroom instruction. The module is designed to help educators understand the practical applications of these emerging technologies and use them as pedagogical assets to modernize instructional delivery, increase student engagement, and improve academic performance.
The target learners are District 1 middle school educators, most of whom have more than 15 years of teaching experience and proven success with traditional instructional methods. While they are familiar with online teaching tools, few have experience with AI. This module is tailored to their background, providing accessible, scenario-based training that respects their expertise while equipping them with new skills.
The context in which this micro eLearning will be used is District 1’s professional development program. The module will be implemented during scheduled training sessions and accessed through the district’s learning management system (LMS). It is designed as a microlearning experience, allowing teachers to complete short, focused lessons asynchronously, followed by collaborative peer-review sessions to apply their learning in practice.
The intended learning outcomes of the module are threefold: first, educators will be able to apply AI tools to redesign lesson plans that enhance student engagement and support personalized learning; second, they will evaluate the impact of AI integration on student performance using provided metrics and rubrics; and third, they will design classroom activities using AI tools that promote higher-order thinking skills such as analysis and synthesis.
The completed micro eLearning module can be accessed at the following URL:
https://691a98403d12abfedc4a8262--splendorous-monstera-e07f8c.netlify.app/
Micro eLearning Production
The analysis phase of this project began with identifying a critical performance gap in District 1’s 8th grade schools, where student outcomes had fallen below national averages despite significant investments in Artificial Intelligence (AI) technologies. The module was developed to bridge this gap by equipping educators with practical strategies to integrate AI into instruction. A needs analysis revealed that the target audience, middle school educators with over 15 years of experience, were highly skilled in traditional methods but lacked exposure to emerging technologies. Therefore, the training was designed to respect their expertise while providing accessible, hands-on practice with AI tools.
In the design phase, I created a storyboard in PowerPoint to map learning objectives, lesson flow, and instructional strategies. Each module section was explicitly tied to one or more objectives, such as redesigning lesson plans, evaluating the impact of technology integration, and designing higher-order thinking activities. The design decisions were guided by Bloom’s taxonomy to scaffold objectives from application to evaluation and design. A microlearning format was chosen to respect teachers’ limited time, with short, focused modules that emphasized practical application. Multimedia elements such as audio, video, and animation were incorporated to model AI affordances and increase engagement, while scenario-based learning was used to foster reflection and transfer of knowledge.
During development, I employed a range of technologies to bring the storyboard to life. Authoring tools such as Articulate Rise, Adobe Captivate, and Storyline were used to create interactive demos and scenario-based videos. Canva Education and Genially supported the design of infographics and interactive visuals, while Google Forms and Microsoft Forms facilitated evaluation surveys. Dashboard simulations were embedded to allow teachers to explore sample student data. The published module is available at the provided Netlify link, and screenshots of the storyboard and interactive dashboards were used as artifacts to illustrate the development process.
The pilot test and revision phase involved peer feedback, which highlighted the need for stronger alignment between activities and Bloom’s higher-order domains, as well as simplifying technical jargon for educators unfamiliar with AI. Based on this feedback, I added guided practice prompts with step-by-step scaffolding, simplified definitions of AI, and incorporated concrete classroom examples. Accessibility was also enhanced by ensuring all videos included captions and transcripts.
Implementation will occur during District 1’s professional development workshops, with delivery through the LMS. Teachers will complete modules asynchronously over a two-week period, followed by a synchronous peer-review session where they share redesigned lesson plans. This blended approach ensures flexibility while fostering collaboration and accountability.
Evaluation will be conducted through pre- and post-assessments to measure changes in teacher confidence and student engagement. Student performance metrics such as test scores and project quality will be tracked over one semester. Teacher feedback surveys and analytics dashboards will provide ongoing evaluation data, ensuring the module’s effectiveness and informing future iterations.
Reflecting on this process, technology played a dual role as both the medium and the message. By embedding AI tools within the module itself, educators experienced firsthand how these technologies can transform instruction. Authoring tools enabled the creation of interactive, scenario-based learning experiences that would be impossible in static formats, while technology ensured scalability and consistent quality across multiple schools. One key lesson learned was that brevity with intentionality defines microlearning; every activity must be purposeful and directly tied to an objective. Experienced educators value efficiency, so designing short, high-impact modules required stripping away excess theory and focusing on practical application.
I am proud of how the module balances respect for teacher expertise with innovation. Rather than overwhelming educators with technology, the design scaffolds their learning and demonstrates how AI can enhance, not replace, their professional judgment. The alignment between objectives, activities, and assessments reflects purposeful instructional design. If I could change one aspect, I would incorporate more collaborative elements earlier in the module. While peer review is included at the end, embedding opportunities for teachers to co-design AI activities during the learning process would foster deeper engagement. This could be achieved by integrating breakout discussions or shared lesson repositories within the LMS.
