Design Process

To rescue the accreditation and truly serve the industry, I have worked as "AI Learning Experience Design Consultant" and gave a complete strategic structural remediation rooted in Learning Experience Design (LXD) Framework including:

• Instructional Design: Determining Course Content

• Digital Design: Crafting the Look & Feel

• eLearning Development: Making Interactions

Working closely with the Founder & CEO of AIMICI and industry Subject Matter Experts (SMEs), I spearheaded the development of a structured curriculum remediation, ensuring that all pedagogical elements met both industry standards and accreditation requirements.

Visual | End-to-End Design Process: Instructional Design + Digital Design + eLearning Development

Instructional Design

My role was to translate AIMICI’s subject-matter expertise into a measurable, accreditation-aligned, cognitively progressive learning system.

Content Writing Strategy

Based on the provided AIMICI lesson content, I developed structured narrative scripts for each module in alignment with:

• AIMICI Content Writing Guidelines

• Innovate UK Bridge AI Learning Design Accreditation Criteria

Rather than simply editing content, I:

• Re-sequenced concepts for progressive cognitive load management

• Embedded signalling and reinforcement techniques

• Applied plain-English optimisation without diluting technical accuracy

• Designed for neurodiversity-aware readability and accessibility

The objective was to maximise clarity, reduce ambiguity, and ensure that learners from varied professional backgrounds within the Film & TV ecosystem could engage with complex AI concepts confidently.

Visual | Content Writing Workflow: Google Docs (Gemini + NotebookLM) & MURAL

Strategic Framework Alignment

Following the release of the UK Government AI Skills Framework (October 2025), I conducted a full curriculum audit and strategic remapping exercise.

This required:

• Cross-referencing all modules against updated national AI capability descriptors

• Identifying gaps between existing learning objectives and new framework competencies

• Repositioning modules to reflect applied and workplace-relevant AI literacy

The redesign elevated the programme from passive knowledge acquisition to performance-oriented capability development. The emphasis shifted from “What is AI?” to “How do I critically evaluate and responsibly implement AI within my professional context?”

Visual | Learning Outcomes Mapping: Alignment between LOs + Contents + UK Government AI Skills

Learning Outcomes Architecture & Cognitive Progression

I rewrote and tiered the Learning Outcomes using Bloom’s Taxonomy as a structural backbone.

The progression was intentionally differentiated:

• Introduction Course: From remembering and understanding foundational AI concepts

• Advanced Course: Toward analysing, evaluating, and creating AI-informed solutions

This cognitive elevation ensured vertical alignment between course levels and avoided redundancy between beginner and advanced tracks.

Visual | Learning Objectives Mapping through Blooms Taxonomy

Constructive Alignment & Platform-Level Mapping

A key design priority was maintaining constructive alignment across all levels of the learning ecosystem.

I ensured:

• Lesson-Level Learning Outcomes aligned directly with Articulate Rise content blocks and micro-interactions

• Section-Level Learning Outcomes mapped to knowledge checks and scenario-based interactive elements

• Course-Level Learning Outcomes measured through cumulative quizzes and applied assessments

This structure guaranteed that every interactive element had an explicit pedagogical function, rather than serving as superficial engagement.

The result was:

• Transparent performance expectations for learners

• Measurable competency progression

• Accreditation-ready documentation

• A defensible assessment architecture

Digital Design

As a senior Learning Experience Designer, I approached digital design not as a production task, but as a strategic layer of the learning architecture. Every visual asset was developed to reinforce cognitive clarity, brand coherence, accessibility, and instructional intent.

Visual System & AI-Driven Asset Production

I designed and produced all characters, illustrations, and iconography through an iterative AI-supported workflow grounded in advanced prompt engineering. Rather than generating isolated visuals, I built a controlled visual system aligned with the AIMICI Learning HUB Look & Feel framework.

Each iteration was evaluated against:

• Visual language consistency

• Colour palette integrity

• Accessibility and contrast standards

• Cross-module scalability

• Cognitive load principles

This ensured that all assets functioned as instructional tools — not decorative elements — and maintained strict alignment with the defined brand and UX ecosystem.

Visual | Icon Design Workflow: Branding Analysis + Prompt Development + Iterative Production with AI

Character Design Strategy

Drawing on my experience as a Google Data Center Community AI Fellow, I developed an inclusive character system representing key roles within the Film & TV Production ecosystem.

Character design decisions were mapped against:

• AIMICI Brand Guidelines

• Innovate UK Bridge AI Learning Design Accreditation Criteria

• ScreenSkills competency frameworks

The goal was not simply representational diversity, but role-based authenticity. Each character was designed to:

• Reflect industry-recognizable functions

• Support scenario-based learning

• Increase learner identification and engagement

• Reinforce professional realism

All assets were developed using Google Labs tools with structured high-fidelity prompting to ensure stylistic and narrative consistency across modules.

Visual | Character Design Workflow: Persona Development + Prompt Development + Iterative Production with AI

Process Illustrations & Production Cycle Visualisation

To support systems thinking and procedural clarity, I designed visual scenes covering the full Film & TV production lifecycle:

• Development

• Pre-Production

• Production

• Post-Production

• Distribution

These illustrations were intentionally structured to:

• Visualize workflow dependencies

• Clarify stakeholder roles

• Reduce ambiguity in complex processes

• Support scaffolded learning progression

In parallel, I developed a cohesive icon set aligned with AIMICI’s brand system. Icons were designed for rapid cognitive recognition, UI clarity, and cross-platform usability.

Visual | Process Illustration Design Workflow: Content Analysis + Prompt Development + Manual Re-Production

Explanatory Video Design

For selected eLearning sections, I transformed industry constraints, assumptions, fears, and aspirational drivers into short-form explanatory videos.

This process included:

• Narrative scripting grounded in learner personas

• Tone calibration for UK professional standards

• AI-supported voice production using ElevenLabs

• Post-production editing in Adobe Premiere Pro and Camtasia

Rather than focusing solely on information delivery, these videos were designed to:

• Normalize industry challenges

• Address emotional barriers to AI adoption

• Build psychological safety

• Frame AI as an augmentation tool rather than a threat

All voiceovers and narrative structures were reviewed to ensure cultural and professional alignment with UK sector expectations.

Visual | An example for explanatory video

eLearning Development

The development phase translated the instructional architecture and visual system into a scalable, accessible, and accreditation-ready digital learning environment.

All visual assets were systematically converted into educational videos, interactive blocks, and multimodal learning components using:

• Articulate Rise (primary authoring environment)

• Articulate Storyline (advanced interaction scenarios)

• Adobe Premiere Pro (video post-production)

• Camtasia (screen-based demonstrations and edits)

The development workflow prioritised consistency, accessibility, production efficiency, and rapid iteration cycles.

Visual | Articulate Storyline Taxonomy Strategy: Course > Sections > Lessons > Blocks

Authoring Tool Decision & Delivery Strategy

Considering the limited budget and compressed timeline, I contributed to the critical decision-making process between SanaLearn, Articulate Storyline, and Articulate Rise.

Following a comparative evaluation across:

• Development speed

• Scalability

• Accessibility features

• Review and iteration workflow

• Accreditation documentation readiness

Visual | From Project MURAL: Modules Screenshots and Learner Journey Example

Articulate Rise was selected as the primary authoring tool. This decision enabled the delivery of first-release versions of both the Introduction and Advanced eLearning modules, comprising 20+ lessons, within a one-month production window, ready for the first evaluation cycle.

The choice supported:

• Rapid prototyping

• Modular scalability

• Efficient stakeholder feedback loops

• Reduced technical overhead

Accessibility Integration & Compliance

Accessibility was addressed as an integrated production layer rather than a post-production correction. In response to the initial evaluation report provided by ScreenSkills, I worked closely with the team to operationalise all identified Accessibility Requirements across modules.

Enhancements included:

• Subtitles for Video Content

• Captions for Process-Based GIF Illustrations

Subtitles for Video Content

All original video narrations were transcribed and embedded with synchronised subtitles to support inclusive learning and flexible consumption.

Captions for Process-Based GIF Illustrations

For the animated GIFs illustrating workflows, journeys, and step-by-step instructions, alternative text-based explanations were added adjacent to each visual.

This ensured:

• Readability without motion dependency

• Compatibility with screen readers

• Reduced cognitive strain

• Equal access for visually impaired learners

Colour Contrast & Visual Accessibility

When generating AI-assisted visual assets, I used customised prompt parameters to maintain accessible contrast ratios aligned with digital accessibility standards.

Additionally, I leveraged new accessibility features within Articulate Rise to ensure text readability and visual hierarchy integrity across devices.

Competency-Level Based Learning Architecture

Visual | From Project MURAL: Competency-Related Personalised Learning Pathways

I played an active role in integrating the AI-powered AIMICI Companion into the learning ecosystem.

During the Advanced Module design, particular emphasis was placed on enabling differentiated learning journeys tailored to distinct professional roles within the Film & TV production landscape.

This involved:

• Mapping role-specific competencies

• Structuring adaptive progression pathways

• Aligning AI support interventions with learner proficiency levels

The result was a competency-aware learning journey where professionals from varied backgrounds could navigate the course in ways relevant to their operational realities.

Transparency in AI Use & Ethical Visibility

Throughout the eLearning production lifecycle, I documented the use of AI tools across content creation, visual generation, scripting, and editing stages.

To maintain transparency and responsible AI practice, I adopted the framework developed by the Dubai Future Foundation titled Visual Standards for Research and Publications.

Visual | AI Usage in my Design Workflow through "Human-Machine Collaboration in Research and Publications"

Using their Icons for Human–Machine Collaboration (HMC) system, I made visible:

• Where AI contributed to output

• Where human editorial oversight intervened

• Where final decision authority remained

This approach strengthened:

• Ethical design accountability

• Stakeholder trust

• Accreditation defensibility

• Responsible AI modelling for learners