Fix Generic L&D: Use Gemini to Build Truly Personal Learning Paths

Connect HRIS and LMS to Build a Unified Skill Profile

Start by integrating your HRIS (roles, tenure, performance indicators, career goals) and LMS (courses, completions, assessments) into a unified profile that Gemini can access via APIs or a data layer. The model needs a holistic picture of each employee to recommend relevant learning paths.

Map at least the following data points: current role, target role or development interests, existing certifications, completed courses, manager feedback indicators (where available), and language preferences. Then expose this as structured context in prompts or system instructions to Gemini when generating learning paths.

System instruction to Gemini:
You are an L&D copilot for HR.
Use the following structured data to propose a 12-week learning path:
- Current role: {role}
- Target role / development focus: {goal}
- Key skills required: {skills_from_framework}
- Completed trainings: {completed_trainings}
- Performance signals: {performance_data}
Generate a sequenced plan with:
- 2-3 core topics per week
- Content recommendations from the LMS by ID
- Short rationale per recommendation
- 1 measurable outcome per week.

Expected outcome: HR can generate consistent, role- and skill-based learning paths in seconds instead of manually piecing together content for each individual.

Use Gemini to Auto-Tag Content with Skills and Levels

Personalization fails if your content library is poorly tagged. Use Gemini to analyse existing e-learning modules, PDFs, slide decks and videos, and propose tags for skills, proficiency levels, duration, and format. This gives the model a much richer foundation for matching content to individual needs.

For text or transcript-based content, batch process with prompts like:

Prompt to Gemini:
You are a learning content analyst.
For the following course description and transcript, output JSON with:
- primary_skills (max 3)
- secondary_skills (max 5)
- difficulty_level (beginner/intermediate/advanced)
- estimated_duration_minutes
- recommended_audience_roles
Course content:
{course_text_or_transcript}

Then write these tags back into your LMS metadata. Expected outcome: higher-quality content metadata that dramatically improves the accuracy of Gemini’s learning path recommendations.

Generate Adaptive Microlearning and Quizzes from Core Content

Once core paths are defined, use Gemini’s content generation capabilities to create microlearning units and quizzes tailored to each learner’s level. For example, generate different question sets for beginners vs. advanced employees on the same topic, based on previous assessment results stored in the LMS.

Use prompts like this when an employee finishes a module:

Prompt to Gemini:
You are an assessment designer.
Create 8 quiz questions on the topic: {topic}.
Adapt the difficulty to this learner level: {beginner|intermediate|advanced}.
Requirements:
- mix of multiple choice and scenario-based questions
- mark the correct answer
- 1-2 questions must apply to this job role: {role}
Return as JSON that the LMS can render.

Expected outcome: employees get right-sized challenges, and managers see more meaningful indicators of actual understanding and skill progression.

Implement “Next Best Step” Recommendations Inside the LMS

Instead of burying AI recommendations in a separate dashboard, surface Gemini’s next best learning step directly inside your LMS or intranet. After each completed course, call Gemini with the learner’s updated profile and course history to suggest the next module, practice assignment, or reflection prompt.

Configuration sequence: 1) Trigger a backend call to Gemini whenever a learner completes a course. 2) Pass: updated completion list, assessment scores, declared career goals, and required skills for the role. 3) Ask Gemini to return: one primary recommendation, two alternative options, and a short explanation per option. 4) Display those recommendations in a simple widget on the LMS home screen.

Example Gemini request payload (simplified):
{
  "role": "Team Lead Customer Service",
  "goals": ["improve coaching skills"],
  "required_skills": ["coaching", "conflict management"],
  "completed_courses": ["coaching_basics_101"],
  "assessment_scores": {"coaching_basics_101": 0.78}
}

Expected outcome: continuous, low-friction personalization that keeps learners engaged and progressing without HR manually curating next steps.

Track Skill Progress and Feedback Loops with Gemini Analytics

Use Gemini not only to recommend content but also to analyse outcomes and refine your learning strategy. Combine LMS data (completions, quiz scores), HR data (promotion and internal mobility events), and, where appropriate, manager feedback to estimate skill progression over time at team and role level.

Regularly ask Gemini to summarise patterns and risks:

Prompt to Gemini:
You are an L&D analytics assistant.
Using the aggregated data below, identify:
- 3 skills where employees progress slower than expected
- 3 learning paths with strongest impact on promotions or performance
- content that is frequently recommended but rarely completed.
Suggest adjustments to paths and content mix.
Data:
{aggregated_anonymized_metrics}

Expected outcome: a data-informed L&D roadmap where you iteratively improve learning paths based on real behavior and results, rather than intuition alone.

Design a Focused Pilot Before Rolling Out Company-Wide

Don’t launch personalization for every role at once. Choose 1–3 critical target groups (e.g. frontline managers, sales reps, or new hires) and design a Gemini-powered pilot that you can run for 8–12 weeks. Limit the scope: a defined skill set, a curated content subset, and clear KPIs such as path completion rate, time-to-proficiency, or internal mobility.

Use the pilot to validate: integration feasibility with your HRIS/LMS, quality of Gemini-generated paths, employee and manager satisfaction, and measurable performance signals. Reruption’s AI PoC offering is structured exactly for this: quickly building and testing a working prototype with real data and users, then turning proven elements into a scalable solution.

Expected outcomes if executed well: 30–50% reduction in manual L&D curation time for the pilot group, higher course completion rates, and clearer visibility of which learning experiences contribute most to role readiness and internal mobility—providing a strong case for broader rollout.