Promega Instructional Design Internship · May-August 2026

Adaptive Learning Assistant

An AI-assisted learning prototype that lets adult learners test out of content they already know, focus on remaining gaps, and get course-grounded support from an AI mentor.

AI product designWeb applicationLearning technology
4

Modules in the pilot course

80%

Accuracy required to test out of a module

3

AI response tags: course-grounded, test-related, and human handoff

Working internship prototype · Internally tested with instructional designers · No target-learner impact data

My Role

Pain Point

Long fixed learning paths do not fit busy adult learners.

Promega's Scientific and Sales Training Services (SSTS) team supports global employees and distributor teams with product and sales training. Learners often have crowded schedules, so a one-size path can ask them to repeat content they already understand. The product opportunity was to make training more efficient without weakening confidence in mastery.

Product Strategy

Assess first, route deliberately, support on demand.

Assessment results determine the path. The AI mentor supports explanations without deciding which modules a learner can skip.

Adaptive learning workflow from pre-test, routing, assigned sections, post-test, mastery decision, consult report, escalation, and completion.

System Design

Two systems shared one learner profile.

The prototype paired a rules-based engine with a course-grounded AI mentor. That division let the product use generative AI where it helped learning, while keeping assessment, routing, and completion decisions explainable.

Shared Learner Profile
  • Learner intake: name, role, and year at Promega
  • Assessment record: pre- and post-test scores
  • Course state: the current module each learner is on
  • Module timestamps: when learners open and complete content
Rule-Based EngineGrades assessments, routes modules, and determines completion.

I designed the assessment logic and per-module mastery rules so routing decisions stayed consistent and auditable.

Course-Grounded AI MentorExplains concepts, coaches learners, and protects assessment answers.

I used reviewed course content summaries to guide the AI, blocked requests for test answers, and directed out-of-scope questions to a human expert.

Assessment design

The routing rule depends on the quality of the questions.

I developed an assessment blueprint with five questions per module, parallel pre- and post-tests, and question-level feedback. Instructional designers and an SME reviewed the questions and course summaries.

Translate evidence into a path

The prototype uses an 80% module threshold: four correct answers out of five make that module optional review. Lower scores assign the module as required content. The course content itself was existing Promega material delivered through Review 360.

Recognize the limits of the rule

One answer changes a module score by 20 percentage points. Pilot feedback about guessable questions therefore matters to routing accuracy. The threshold is a prototype rule, not evidence that the assessment predicts workplace performance.

Prototype Walkthrough

How the product decisions appear to learners

These screens show the learner journey from intake to course completion and how each product decision appeared in the web application.

Learner setup and diagnostic screens
01

Create a profile and diagnose prior knowledge

Learners enter their name, role, and years at Promega to log in and access the course. Their progress is saved so they can return later after logging in again. The pre-course check then identifies what they already know.

Learner intake creates the profile used throughout the experience.
Pre-course assessment with capillary electrophoresis questions and answer options.
The diagnostic maps 20 questions to four course modules.
02

Turn assessment evidence into a learning plan

Immediate answer feedback supports transparency, while module-level scores determine which content is required and which becomes optional review.

Pre-test answer review showing correct answers and explanations.
Question-level feedback explains both correct and incorrect responses.
Pre-test summary showing module scores, mastery status, and assigned learning plan.
This summary at the end of the pre-test shows the accuracy score for each module. The 80% mastery rule then converts those results into required and optional modules.
03

Focus learning on identified gaps

The course panel keeps required modules, progress, records, and mentor support together. Course content is delivered through an embedded Review 360 experience.

Course panel showing required and optional modules, locked post-test, records, and AI mentor.
The main workspace makes the assigned path and post-test requirements visible.
Introduction to Capillary Electrophoresis module displayed in Review 360.
The existing Promega course became the learning content inside the adaptive path.
04

Make AI behavior visible and accountable

Every mentor response is labeled so learners can see whether it is grounded in the course, redirected away from test answers, or flagged for human support.

AI mentor response labeled Grounded in the course.
Course-grounded explanation.
AI mentor response redirecting a request for post-test answers.
Assessment-answer protection.
AI mentor response flagged for a human because the question is outside the course scope.
Human escalation for out-of-scope questions.
Post-test access after required modules
05

Gate the post-test by required work

The system unlocks the post-test only after all assigned modules are complete. The mentor recognizes the learner's progress and remains available for review.

Course panel with required modules completed, post-test unlocked, and mentor coaching visible.
Completion state, post-test access, and mentor context stay synchronized.
06

Respond to both post-test outcomes

The system branches at the 80% mastery threshold. Learners who pass receive confirmation and completion records. Learners below the threshold receive review guidance, a mentor summary, and notice that a trainer or subject-matter expert will follow up.

Pass pathway · 80% or higher

Post-test passed screen showing a 90 percent score, answer feedback, and mentor response.
The pass state combines assessment evidence with continued mentor support.
Post-test summary with module scores, mentor summary, and record downloads.
Passing learners receive module scores, a mentor summary, and downloadable records.

Support pathway · Below 80%

Post-test result below the 80 percent threshold with a supportive review message.
The learner sees a clear, supportive message that the mastery threshold was not met.
Post-test summary showing a 55 percent score, modules to review, mentor guidance, and downloadable records.
Module-level results guide review while the mentor explains the human follow-up pathway.

Testing & Insights

The pilot validated feasibility and clarified the next product questions.

Internal instructional design testers confirmed that the core flow worked, while surfacing important usability and learning-design improvements. There was no time to revise after the pilot; the findings below were not implemented during the internship.

The prototype worked end to end, including module access, learner persistence, and mentor responses.

Assessment quality needed refinement because some questions felt guessable and answer lengths sometimes hinted at the correct option.

Learners needed clearer guidance around what to do next, when they were done, and how the mentor should be used.

Internal testers encountered the intended refusal behavior, but strict refusals sometimes created friction. This pilot did not establish reliability across all possible questions.

Opening course modules outside the main app made it harder to use the mentor while studying.

Project Scope

As a prototype, the project showed feasibility before learner-impact measurement.

What this project demonstrated

The prototype showed that adaptive routing, guarded AI support, learner persistence, and completion reporting could work together in a single learning experience.

What would need more evidence

This project is a prototype and has not been applied to real learners. There is no target-group data yet, so learning-impact claims would need a structured learner pilot.

Reflection

Balancing AI flexibility with product accountability.

The most important product decision was not simply adding an AI tutor. It was deciding where AI should help and where it should not decide: generative support for explanations and coaching, fixed product rules for mastery, routing, and completion.