Ani Ai Training Ani Ai Training

Summary

MPI Inc. launched a new internal AI tool, “Ani,” designed to answer questions and cite files from a curated company database. During testing, stakeholders found that employees struggled to get started and understand Ani’s benefits—despite existing video resources.

After analyzing the issue, I identified a lack of interactivity and relatable content in the original training. To address this, I proposed a story-based eLearning experience where learners interact directly with Ani, gaining hands-on practice and a clearer understanding of how she can support daily tasks.

The goal was to help employees across departments use Ani effectively and improve task efficiency and accuracy by 20%. The client approved the solution and moved forward with development.

The Process

Needs analysis

Needs Analysis

To kick off development, I collaborated with a Subject Matter Expert (SME) to gain a deeper understanding of MPI’s internal AI tool and the anticipated needs of employees. Together, we defined the project’s overall learning goal and established a way to measure success: a pre- and post-assessment focused on department-specific questions that Ani could help answer.

The pre-assessment would be completed without AI support, while the post-assessment would allow learners to use Ani. By comparing completion times, we could demonstrate improvements in information retrieval efficiency. We also identified key topics about Ani that would be essential for employees to understand her value and capabilities.

Storyboard

Text-based Storyboard

After completing the needs analysis, I developed a Design Document outlining goals, objectives, deliverables, milestones, and the plan for implementation and evaluation. With approval, I moved into storyboarding, scripting Ani’s narration to guide learners through the course with clear objectives, real-world examples, and practice opportunities. I collaborated closely with the SME to ensure accuracy and alignment with Ani’s capabilities.

To boost engagement, I designed Ani with a conversational tone, applying Mayer’s Personalization Principle for relatable narration and the Signaling Principle through animations and visual cues. Real interface screenshots added context, helping learners visualize how Ani appears and functions in their browsers.

Styleguide Wireframes

Design Style Guide

After the storyboard was approved, I designed the style guide and wireframes to visually align the course with MPI’s brand while creating a modern, engaging experience. I used MPI’s style guide as a foundation and explored fonts, color palettes, and button styles in Figma to craft a clean, consistent design.

To represent Ani, I sourced a professional-looking AI character and background from Freepik that matched the company’s visual tone. I also created interactive elements with clear hover and selected states to guide user interaction.

Using wireframes, I mapped out various layout options based on the storyboard content. Once the design was finalized, I combined the visuals and narration into a PowerPoint prototype, which I then built out in Articulate Storyline 360 to add interactivity.

Prototype Prototype2

Develop Prototype

I used Articulate Storyline 360 to transform my original PowerPoint into an interactive, story-driven learning experience. The prototype included core instructional elements, interactive features, and realistic scenarios that introduced learners to Ani AI and how to use her effectively.

Learners began with a title screen, selected an audio preference, and were guided by Ani through the course objectives. Key sections included an embedded screen share demo and an interactive slide where learners could access Ani directly for hands-on practice. Smooth transitions and animations helped create an engaging, immersive experience.

After sharing the prototype with stakeholders, I received helpful feedback, mostly positive—which led to small adjustments for accuracy and additional animations to boost engagement. Once finalized, I moved into full development with a solid, validated design.