Turn Limited Learning Insights into Impact with Gemini in HR

Connect LMS Exports and Google Workspace into a Single Gemini Workspace

The first tactical step is to give Gemini access to the right inputs. Export course data, module structures, assessment scores and completion logs from your LMS (CSV, Excel or via API), and store them in a structured way in Google Drive or Google Sheets. Use consistent naming and date formats so Gemini can recognise relationships across files.

Then, create a dedicated Gemini workspace for HR analytics. When prompting Gemini, explicitly reference the folders or sheets that contain your learning data so it can ingest and reason over them. This avoids the classic “Gemini doesn’t see my data” problem and lays the foundation for reliable insights.

Example prompt to initialise the context:
You are an HR learning analytics assistant.
You have access to the following files in Drive:
- "LMS_Export_Q1_2025.csv" (course completions, timestamps, user IDs)
- "Assessments_Q1_2025.csv" (pre/post scores, module IDs, user IDs)
- "Course_Catalogue_Skill_Tags.xlsx" (course IDs mapped to skill tags)

First, load and summarise the structure of these datasets.
Identify the key fields we can use to link them (e.g., user_id, course_id).
Describe any data quality issues you see.

Expected outcome: Gemini returns a quick schema overview and an initial data quality assessment, so you know whether you can move ahead with deeper analysis or need to fix basics first.

Use Gemini to Map Courses to Skills and Proficiency Levels

If your LMS catalogue is large and inconsistently tagged, manually mapping content to skills can take months. Use Gemini to accelerate this. Export course titles, descriptions and learning objectives, then ask Gemini to propose skill tags and proficiency levels (beginner, intermediate, advanced) based on your competency framework.

Example prompt for skill tagging:
You are helping HR structure our learning catalogue by skills.
Here is our competency framework with key skills and descriptions:
[Paste or link framework]

Here is a table exported from the LMS with columns:
course_id, title, description, learning_objectives

For each course, assign:
- 2-5 primary skill tags from the framework
- A proficiency level (beginner / intermediate / advanced)
Return the result as a table with the new columns added.

Review Gemini’s output with L&D experts, adjust where needed and then re-import the enriched mapping into the LMS or your central skills database. This allows later analytics to answer questions like “Which advanced data skills modules actually move post-test scores?” rather than just “Which data courses are popular?”

Analyse Learning Effectiveness with Pre/Post Assessments and Performance Data

To move beyond completion metrics, combine pre/post assessments with business KPIs where possible. Feed Gemini a dataset linking learner IDs, module completions, assessment scores and, if available, anonymised performance indicators (e.g. sales per rep, error rates, quality scores).

Example analysis prompt:
You are an L&D effectiveness analyst.
Use the following datasets:
- Assessments_Q1_2025.csv (user_id, module_id, pre_score, post_score)
- Completions_Q1_2025.csv (user_id, module_id, completed_at)
- Performance_Q2_2025.csv (user_id, performance_metric_name, value)
- Course_Catalogue_Skill_Tags.xlsx (module_id, skill_tags, proficiency_level)

Tasks:
1) For each module, calculate average score improvement (post - pre).
2) Identify modules with high completion but low score improvement.
3) Explore correlations between module completions and performance metrics
   1-3 months later, controlling for pre_score where possible.
4) Summarise which skills and modules show the strongest link to improved performance.

Expected outcome: a ranked list of modules by effectiveness, flags for low-impact content and evidence you can use to adjust curricula and defend or reallocate L&D budgets.

Predict Dropout Risk and Trigger Targeted Interventions

Gemini can also help identify where learners are likely to drop out of programs and why. Export event-level learning data (logins, time spent per module, failed attempts, pauses between sessions) and use Gemini to build simple rules or even train a lightweight model that flags participants at high risk of non-completion.

Example prompt for dropout analysis:
You are an HR data analyst.
We have the following data from our leadership program:
- Events.csv (user_id, event_type, module_id, timestamp)
- Completions.csv (user_id, completed_program [yes/no])

1) Identify behaviour patterns that differentiate completers from non-completers
   (e.g., time gaps, number of failed quizzes, late-night usage).
2) Propose simple rules we could use as an early warning system.
3) Suggest targeted interventions HR or managers could trigger when a
   participant is flagged as high risk of dropping out.

Once you have these patterns, you can operationalise them: for example, by having HR business partners receive a weekly Gemini-generated report of at-risk participants with suggested interventions like “schedule a manager check-in” or “recommend a shorter microlearning alternative.”

Generate Manager-Ready Insight Reports and Learning Path Suggestions

Managers rarely have time to dive into LMS dashboards. Use Gemini to turn raw analytics into concise, role-specific insight reports and personalised learning path suggestions. Feed Gemini the learning and performance data for a team or department and ask it to produce a summary that a manager can act on in 5 minutes.

Example prompt for manager reports:
You are an HR partner preparing a quarterly learning report for the Sales West team.
Input data:
- SalesWest_Learning.csv (user_id, modules_completed, skills_covered)
- SalesWest_Performance.csv (user_id, quota_attainment, win_rate)
- Skill_Framework.pdf (role-specific target skills for Sales roles)

Produce a concise report:
1) Summarise overall learning activity and key skills strengthened.
2) Highlight 3-5 modules that show the strongest link to improved win rate.
3) Identify top 3 skill gaps vs. target profile for the team.
4) Suggest individualised learning paths for the bottom 20% performers
   (2-3 modules each, focusing on high-impact skills).

Expected outcome: consistent, data-backed manager briefings that translate learning analytics into decisions on coaching, promotions and targeted development.

Embed Gemini Workflows into a Repeatable Monthly Learning Insights Cycle

To make these practices stick, turn them into a monthly or quarterly cycle rather than one-off experiments. Document a simple workflow: export data from the LMS on a set date, store it in a predefined Drive structure, run a series of standard Gemini prompts (possibly via automation), and compile the outputs into HR and business-ready formats.

Where possible, automate the repetitive steps using Google Apps Script or simple integrations, so HR teams mainly review insights rather than wrangle data. Define practical KPIs for your AI-driven learning analytics: reduction in low-impact content, percentage of budget shifted to high-effectiveness programs, time saved on reporting, and improvements in targeted skill indicators over time.

Expected outcomes: within 3–6 months, HR can realistically expect a 20–40% reduction in time spent on manual learning reports, a measurable shift of 10–20% of L&D budget into demonstrably high-impact modules, and clearer evidence linking specific learning investments to skill improvements and performance trends.