Featured Module: Mastering the Evaluation Paragraph: Part 1—Sentence-Level Thinking
AI-Assisted Critical Academic Writing Microlearning Course
An AI-assisted client project that transforms recurring critical-writing needs into reusable, bite-sized pre-training for Lumist Education’s one-on-one learners.
Project Type: Client project for Lumist Education
Tools: Articulate Rise/Chatgpt/Canva
My Role: Learning Content Developer & AI Video Producer
My Contribution: Course structure, learning objectives, storyboard, script, video production, and evaluation
Project Context：
Lumist Education provides personalized one-on-one tutoring for Chinese students studying overseas. The learners I supported ranged from high school seniors to college seniors, with lessons tailored to each student’s academic needs.
The Need：
Over time, academic writing and critical thinking emerged as common challenges across learners. Many students had limited experience with evaluative writing and often confused evaluation with description or summary. Instructors therefore needed to repeatedly introduce the same foundational concepts during individual sessions, leaving less time for personalized practice and feedback.
Target Learner：
Chinese international students from Grade 12 through undergraduate study who are developing English academic-writing skills and have limited prior experience with critical and evaluative writing.
Learning Solution
The learning solution was a supplementary critical-writing course composed of bite-sized modules addressing common academic-writing and critical-thinking challenges.
This case study highlights one module from the course: Mastering the Evaluation Paragraph: Part 1—Sentence-Level Thinking. The seven-minute video introduces the difference between description and evaluation through clear explanations, guided examples, and a repeatable thinking framework.
Students can complete the modules independently before or alongside their one-on-one lessons, building foundational knowledge while allowing instructors to focus on personalized practice, feedback, and application to the student’s coursework.
How I use AI
I used generative AI throughout the design process to analyze, develop, and evaluate the learning experience—not simply to generate a video.
My prompts combined five elements: project context, learner profile, expert role, task constraints, and evaluation criteria. For example, I asked AI to act as an experienced instructional designer, academic-writing educator, or target learner depending on the task.
Scripts — Each lesson started with a tight outline (hook, one core concept, guided example, quick check). AI drafted the narration; I rewrote it for spoken delivery and timed it against a words-per-minute target so each video hit its planned length.
Storyboards — Two-column boards mapping narration to visuals. AI brainstormed how to visualize abstract concepts; I selected, refined, and tagged every visual by purpose (explain, emphasize, example) — decorative ones cut.
Assessments — AI drafted items aligned to each objective; I validated them through a B1-B2 English Level learner persona to catch confusion and misalignment before finalizing.
I treated AI outputs as recommendations rather than final decisions. I reviewed the academic accuracy, instructional logic, examples, and learner fit before approving or revising the content.