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Transfer occurs when learners apply knowledge, skills, and ways of thinking beyond the learning experience. The projects below demonstrate how I use inquiry, authentic scenarios, reflection, and blended learning strategies to create opportunities for learners to analyze information, make decisions, solve problems, and adapt their learning in meaningful contexts. Although each project serves a different audience and purpose, they share a common commitment to designing learning that supports authentic application and meaningful transfer.
Designing for Systems Thinking, Inquiry & and Conceptual Transfer
Project Context
Concept-based inquiry offers powerful opportunities for deep learning, yet teachers often face the challenge of balancing learner agency, conceptual rigor, standards alignment, assessment, and classroom implementation. This project focused on designing an upper-elementary inquiry experience that translated concept-based learning theory into a practical, classroom-ready framework.
Instructional Challenge
As inquiry becomes increasingly student-driven, learning can sometimes drift toward disconnected activities or isolated fact collection rather than conceptual understanding and transfer. The challenge was to create an inquiry architecture that preserved student curiosity and voice while supporting systems thinking, interdisciplinary connections, and meaningful conceptual growth.
Design Strategy
Applied backward design, concept-based inquiry principles, UDL, and visible thinking routines. Developed a five-stage inquiry cycle that guides learners from provocation and investigation through synthesis, reflection, and transfer. The framework incorporated scaffolded thinking tools, formative assessment opportunities, adaptable learning pathways, and embedded teacher facilitation supports.
Learner & Systems Thinking
Learners engage in multimodal investigations, collaborative meaning-making, visual analysis, systems mapping, reflection, and discussion. Learning experiences were intentionally designed to support learner agency while making conceptual understanding, evolving thinking, and transfer visible throughout the inquiry process.
Evaluation & Iteration
The framework was refined through multiple design reviews focused on conceptual coherence, cognitive load, accessibility, learner navigation, assessment alignment, and teacher usability. Iterative revisions strengthened the balance between student agency, instructional guidance, and opportunities for conceptual transfer.
Approach: Trauma-Informed Design, Multilingual Learning, Scenario-Based Learning, Mobile-First Learning, Culturally Responsive Design
Delivery: eLearning, Instructor-Led Training (ILT), Job Aids, Community Learning Resources
Tools: Storyline, Canva, Google Workspace, ChatGPT & Claude AI
Designing Prevention Where None Existed
Project Context
Displaced and multilingual communities across the Greater Mekong Subregion face rising exploitation through scam operations, unsafe migration, and trafficking networks, yet structured prevention education designed for vulnerable low-literacy and multilingual learners remains scarce. Designed and developed a blended learning prevention program using Storyline eLearning optimized for low-bandwidth delivery, facilitator-led workshops, and printable reference and support resources.(Deployment in progress.)
Instructional Challenge
Designing prevention education for trauma-affected, low-literacy, multilingual learners across diverse cultural contexts required an approach that was psychologically safe, culturally responsive, and transferable across rapidly evolving exploitation scenarios. The additional challenge was creating a decision-making framework learners could internalize and apply independently in high-pressure, real-world situations without access to additional support.
Design Strategy
Conducted front-end analysis with NGO stakeholders and designed the original S.C.A.M. decision-making framework (Stop, Check, Ask, Move) to operationalize transferable scam-recognition and protective decision-making skills across evolving exploitation scenarios.
Blended Learning Experience
Applied trauma-informed and culturally responsive design principles through scaffolded complexity, non-judgmental feedback, mobile-first Storyline interactions, multimodal supports, and psychologically safe scenario-based learning experiences aligned to CEFR B1–B2 learners.
The program combined self-paced scenario-based eLearning with facilitated case-study workshops. Learners first developed foundational scam-recognition skills through guided practice and feedback before applying the S.C.A.M. framework in collaborative discussions, evidence-based case analyses, and community-focused action planning. Together, these experiences strengthened learner confidence, peer learning, and transfer of protective decision-making skills to authentic situations within their communities .
Evaluation & Iteration
Designed as a scalable blended learning program adaptable across NGOs, multilingual communities, and emerging scam patterns. Iterative revisions focused on accessibility, emotional safety, facilitation effectiveness, learner transfer, and sustainable implementation across diverse community settings.
Designing AI Enablement for Safe Experimentation, Behavior Change & Sustainable Adoption
Project Context
Developed as a self-directed exploration of AI enablement, workplace adoption, and sustainable behavior change. Designed for knowledge workers and first-line managers with access to an approved generative AI tool, the seven-lesson experience focuses on what comes after basic adoption: deciding what is worth testing, judging AI output, working within clear boundaries, and integrating useful practices into everyday work. (Tool mechanics and prompting instruction were intentionally kept outside the scope.)
The Challenge
The core challenge was behavioral. Access to AI does not automatically build the judgment needed to experiment safely, evaluate unreliable output, or recognize when additional review is required. The experience balances experimentation with accountability through a Minimum Safety Gate before task selection, governance that extends beyond data privacy to fairness and people impact, and a systems lens that treats AI-enabled workarounds as potential signals of workflow friction rather than individual failure.
Design Strategy
Structured around one practical performance: designing and initiating a low-risk, measurable AI experiment in authentic work. Each lesson adds a decision required for responsible application, including task selection, observable success measures, output verification, risk classification, workflow analysis, and evidence-based Keep, Change, or Drop decisions. Safety and human judgment are established before experimentation, while Priya serves as a recurring course guide and worked example, modeling the decision process before responsibility shifts to the learner. Scenario-based interactions emphasize judgment, comparison, classification, and application.
Change Enablement & Transfer
COM-B and ADKAR informed the behavior-change architecture. Decision frameworks build capability; approved boundaries, sharing structures, and point-of-work supports create opportunity; and verification triggers, structured reflection, and implementation intentions support motivation and follow-through. A fillable AI Experiment Planner supports planning, evidence collection, and reflection, while a Quick-Reference Aid provides point-of-work guidance. Follow-through extends beyond the initial experiment: Day 7 reviews evidence, Day 30 checks habit consistency, Day 60 examines conditions supporting or blocking continued use, and Day 90 considers what is ready to become routine.
Evaluation & Iteration
Evaluation was scoped to evidence the experience could realistically produce. Scenario-based checks were designed to assess applied judgment, while behavior-based self-ratings and the AI Experiment Planner support reflection on changes in practice over time. The 30/60/90 follow-through is treated as a longitudinal behavioral indicator rather than proof of sustained performance. Iterative review and peer feedback strengthened safety sequencing, people-impact decisions, evaluation claims, and narrative signaling. Generative AI supported testing, critique, and rapid iteration, while instructional architecture, accessibility, risk decisions, and evaluation remained grounded in human design judgment.
Approach: Behavioral Learning Design, Scenario-Based Learning, Competency-Based Learning, Narrative Architecture
Deliverables: Learning Architecture, Competency Framework, Storyboard, Learner Matrix, MVP Prototype
Tools: Claude (AI & Design), Canva
Behavioral Learning Architecture & Persona-Based Systems Design
Project Context
Translated a persona behavioral framework into measurable, scalable learning experience that required a rigorous instructional architecture. Led the ID council and served as learning design consultant on the DPD Framework, converting 250+ pages of behavioral source material into a foundational learning architecture including a curriculum map, KSAs, competency progression systems, learner matrix, evaluation pathways, and a complete storyboard and MVP prototype for Module 1.
Instructional Challenge
Operationalizing abstract behavioral constructs such as Humility, Respect, and Trust into recognizable, applicable workplace scenarios required both a structured competency framework and a narrative-driven instructional architecture that made behavioral transfer feel authentic and relevant across diverse professional contexts.
Design Strategy
Designed scenario-based learning experiences using the Persona Busters narrative concept within a haunted corporate office environment to ground abstract behavioral concepts in recognizable workplace situations. Learning interactions were intentionally scaffolded through hotspots, scenario choices, card-based tools, and reflective journaling to support behavioral recognition, transfer, collaboration, and progressive mastery across five levels.
Evaluation & Iteration
Developed the DPD Learner Matrix to translate behavioral constructs such as Humility, Respect, and Trust into observable engagement, collaboration, and learning-transfer indicators aligned to LTEM and Kirkpatrick-informed evaluation pathways. Designed to support longitudinal growth measurement, scalable implementation, and evidence-informed iteration across future DPD modules.
Visual communication helps learners organize information, recognize patterns, and make meaning from complex ideas. The galleries below showcase instructional graphics, infographics, and learning supports designed to improve clarity, reduce cognitive load, and communicate with purpose across educational and workplace settings.
UNDERSTAND
Complex ideas become actionable when learners can see relationships, systems, and processes clearly. These artifacts demonstrate how visual communication supports comprehension, accessibility, instructional clarity, and consistent learning experiences across digital and instructor-led environments.
CONNECT
Learning transfers most effectively when learners have structures that support reflection, reasoning, and action. These tools were designed to scaffold inquiry, strengthen metacognition, and support independent decision-making across a variety of learning contexts.
APPLY
Scenario-based learning allows learners to practice decision-making within authentic contexts. These examples demonstrate how AI-assisted media was used to create characters, environments, and narrative experiences that support inquiry, reflection, and real-world application.
Multimedia combines visual storytelling, motion, narration, and sound to communicate ideas in ways that support learning, engagement, and retention. These projects demonstrate how I apply the same instructional design principles that guide my learning experiences, including audience awareness, intentional messaging, visual hierarchy, narrative structure, and cognitive clarity, to transform complex ideas into engaging and accessible multimedia experiences.
Communication Focus
Translating Complex Ideas
Project Context
Created as a short instructional explainer introducing the SOAR Method (Situation, Obstacle/Opportunity, Action, Result), a widely used interview response framework. The video combines narration, visual hierarchy, motion graphics, and pacing strategies to simplify a structured communication process into an accessible and memorable learning aid.
Tools:
Camtasia
Pexels and Pixaby
IDOL VO audio
Communication Focus
Storytelling & AI-Assisted Media Production
Project Context
Created as a creative sandbox project to explore rapid multimedia production workflows using AI-assisted content generation, video editing, and visual storytelling techniques. The project demonstrates experimentation with narrative development, media integration, and audience engagement within a lightweight production environment.
ELB Studio AI Toolkit (Beta)
ChatGPT
Google AI Studio
Camtasia
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