Stop Irrelevant Courses: Use ChatGPT to Personalise Learning in HR
HR teams invest heavily in training, yet employees are still pushed into generic courses that don’t fit their role or skill level. This page shows how to use ChatGPT to analyse your learning catalogue, align content with roles and skills, and generate targeted learning paths that actually improve on-the-job performance.
Inhalt
The Challenge: Irrelevant Course Content
Most HR and Learning & Development teams know the pattern: you roll out a new training catalogue, enroll whole populations into “mandatory” courses, and then watch engagement flatline. Employees sit through generic content that ignores their role, starting level, and career goals. The result is bored learners, wasted training hours, and little impact on real performance.
Traditional approaches to L&D were built for a different era. Competency matrices live in spreadsheets, not in systems. Course catalogues are updated annually, not dynamically. Learning paths are defined by job grade or function, not by actual skills or business priorities. Even when you have modern LMS or LXP platforms, personalisation often stops at job title and department, while the content itself remains static and detached from current skills gaps.
The business impact is significant. Budgets are locked into licences and content libraries that are barely used. Managers struggle to see how training links to KPIs like productivity, quality, or sales. High performers tune out, and struggling employees stay under-skilled because the learning offer doesn’t meet them where they are. Over time, this erodes trust in HR-led development initiatives and puts you at a competitive disadvantage in talent retention and upskilling.
Yet this challenge is absolutely solvable. With the right use of AI, especially tools like ChatGPT, HR can move from static, one-size-fits-all courses to adaptive, role-specific learning journeys that reflect real work and current skills frameworks. At Reruption, we’ve helped organisations build digital learning products and AI-powered tools that make this shift from generic to targeted. Below, you’ll find practical guidance on how to use ChatGPT to clean up irrelevant content and redesign L&D so it finally fits your people and your business.
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Our Assessment
A strategic assessment of the challenge and high-level tips how to tackle it.
From Reruption’s work building digital learning platforms and AI-powered tools, we’ve seen that the problem of irrelevant course content is less about catalogues and more about how information is structured. ChatGPT for HR learning becomes powerful when it can read your roles, competencies, and existing materials and then help you rebuild them around skills and outcomes instead of generic topics. The key is to treat ChatGPT as an engine for skills intelligence and content adaptation, not just as a nicer way to write course descriptions.
Anchor Everything in a Skills and Role Framework First
Before using ChatGPT to fix irrelevant courses, HR needs a clear view of what “relevant” means. That comes from a robust skills and role framework. Define target competencies for key roles, desired proficiency levels, and business-critical skills clusters. Even a 70% complete framework is more useful than a perfect one that never ships.
Once that foundation exists, you can direct ChatGPT to analyse each course against target roles and skills: Which modules support which competencies? Where are the gaps or overlaps? This turns ChatGPT from a generic text generator into a structured skills alignment assistant, giving you strategic control over what should stay, be updated, or be retired.
Treat ChatGPT as a Co-Designer, Not a Black Box
Strategically, HR should position ChatGPT in L&D as a co-designer that accelerates expert work, not a replacement for learning professionals or subject-matter experts. AI can rapidly propose role-based learning paths, microlearning modules, and quiz questions, but the organisation must own the final decisions and validations.
A practical approach is to establish design loops: HR and L&D leads define guardrails (skills, compliance, tone), ChatGPT generates structured proposals, and SMEs review and adapt. This mindset reduces resistance from stakeholders because AI is clearly framed as an amplifier of expertise, and it mitigates the strategic risk of “outsourcing” learning design quality to a model you don’t fully control.
Start with High-Impact Populations, Not the Whole Catalogue
It’s tempting to ask ChatGPT to analyse every course and every role at once. Strategically, that’s the wrong move. Start with a pilot focused on a critical population where irrelevant training is clearly hurting performance: for example, new sales hires, frontline supervisors, or support agents.
By narrowing scope, you can rapidly test how ChatGPT performs in surfacing outdated modules, mapping content to skills, and proposing tailored journeys. You’ll learn where additional data is needed and where human review is essential. This pilot evidence then helps you build the internal case to scale the approach to other functions and regions.
Prepare Data, Policies and People for AI-Enhanced Learning
Using ChatGPT for HR learning personalisation is not just a tooling decision; it’s an organisational readiness question. You need to consider what data can be used (role descriptions, performance data, LMS logs), how it can be shared with AI under your security and compliance requirements, and who will own the AI-enabled workflows.
Strategically, set up clear policies about which data sources are allowed, how sensitive information is handled, and what must remain on-premise or in a private model. In parallel, upskill HR and L&D teams on prompt design, critical evaluation of AI output, and basic model limitations. Without this, you risk either over-reliance on AI or deep organisational scepticism that blocks adoption.
Define Clear Success Metrics for Learning Relevance
Finally, treat this as a business initiative, not a technology experiment. Before deploying ChatGPT, define what improved learning relevance and effectiveness should look like. Examples include reduced time to proficiency for new hires, higher completion and satisfaction scores for targeted courses, increased usage of recommended modules, or correlation between specific learning paths and performance indicators.
With these metrics in place, you can strategically decide where to iterate: Is AI suggesting the wrong content? Are managers not endorsing the new paths? Are quizzes not predictive of real performance? This moves the conversation from “Is ChatGPT good?” to “Where does AI add measurable value to our L&D strategy, and how do we double down on that?”
Used with the right strategy, ChatGPT can turn an unfocused training catalogue into a skills-based learning system that actually fits your roles and business goals. It won’t replace HR or L&D expertise, but it will dramatically accelerate how you analyse content, design relevant journeys, and iterate based on data. At Reruption, we combine this strategic lens with hands-on engineering so you’re not left with theory but with working prototypes and clear impact metrics; if you want to explore how this could work in your environment, our team is ready to co-design and test a solution with you.
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Real-World Case Studies
From Pharmaceuticals to Payments: Learn how companies successfully use ChatGPT.
Best Practices
Successful implementations follow proven patterns. Have a look at our tactical advice to get started.
Map Your Existing Courses to Roles and Skills with ChatGPT
Start by exporting your current course catalogue from your LMS (titles, descriptions, tags, learning objectives). Combine this with role profiles and competency models. Use ChatGPT to build a first-pass mapping of which courses support which roles and skills. This is the fastest way to see where content is generic, duplicated, or not aligned with your current organisation.
Prompt ChatGPT with a structured format and ask for a table-style mapping. For sensitive data, use a private deployment or anonymise details. A typical workflow: HR prepares a CSV extract, a learning specialist chunks the content, and ChatGPT processes each block to propose role and skill matches.
Example prompt:
You are an HR learning architect.
I will give you:
1) A role description
2) A skills framework for this role
3) A list of courses with titles and descriptions
Task:
- Map each course to 0-3 skills from the framework
- Rate the course relevance for this role (High/Medium/Low)
- Flag obviously outdated or generic courses
Return a markdown table with columns:
Course Title | Mapped Skills | Relevance | Notes (e.g. outdated law, too generic)Expected outcome: a preliminary, AI-generated curriculum map that highlights irrelevant or low-value courses in hours instead of weeks of manual review.
Generate Role-Based Learning Paths Instead of One-Size-Fits-All
Once your content is mapped, use ChatGPT to transform it into role-specific learning paths. Feed it the skills required for a given role and the filtered list of relevant courses. Ask the model to sequence content from foundational to advanced and to propose estimated time investments.
This is where you can immediately tackle the “generic course” problem. Instead of assigning all employees the same leadership or compliance course, you ask ChatGPT to build different tracks for first-time managers, experienced leaders, and specialists, using the same underlying content but different entry points and depth.
Example prompt:
You are designing a learning path for a new Inside Sales Representative.
Inputs:
- Skills for this role: prospecting, product knowledge, negotiation, CRM usage.
- List of relevant courses with tags and levels.
Task:
- Create a 6-week learning path with weekly modules.
- For each week, specify goals, 2-3 courses or learning assets, and one practice activity.
- Emphasise relevance to on-the-job tasks and avoid generic theory.Expected outcome: clear, tailored learning paths that managers can assign and discuss with employees, replacing broad, unfocused training mandates.
Use ChatGPT to Rewrite and Localise Irrelevant or Outdated Modules
Some courses will be structurally sound but feel irrelevant because they are outdated, too theoretical, or not adapted to your context. Here, ChatGPT can act as a content refactoring engine. Provide it with the original lesson text, your updated policies or frameworks, and examples of your company’s language and scenarios.
Ask ChatGPT to rewrite modules with your context, role-specific examples, and current compliance wording. You can also generate different variants for different audiences (e.g. managers vs. individual contributors) while keeping the same learning objectives.
Example prompt:
You are an instructional designer for our company.
Input:
- Original course section on "Data Privacy Basics" (see below)
- Our updated data privacy policy (see below)
- Target audience: frontline retail employees with no legal background
Task:
- Rewrite the lesson section in simple language
- Use practical examples from retail scenarios
- Remove outdated references and align with the new policy
- Keep it under 500 words and end with 3 reflection questions.Expected outcome: refreshed, relevant course content that fits your current reality without having to start from scratch for every module.
Create Microlearning and Quizzes Tailored to Skill Gaps
Generic, hour-long e-learning is one of the main reasons employees disengage. Use ChatGPT to generate microlearning units and quizzes that target specific skills gaps. Combine assessment data (e.g. quiz results, manager ratings) with your skills framework to identify weak spots and then ask ChatGPT to create focused practice pieces.
This can include short scenario-based questions, flashcards, or 5-minute reads that directly relate to a task. For example, if service agents struggle with de-escalating calls, generate situational prompts and responses they can practice with.
Example prompt:
You are creating microlearning for customer support agents.
Skill gap: de-escalating frustrated customers.
Task:
- Create 5 short scenarios of angry customer interactions (chat format)
- For each, provide 3 possible agent responses (A/B/C)
- Indicate the best response and explain why in 2-3 sentences
- Keep language realistic and aligned with our tone: calm, solution-focused.Expected outcome: a bank of highly targeted microlearning elements you can plug into your LMS, learning campaigns, or chat-based practice tools.
Analyse Feedback and Engagement Data to Continuously Prune Content
To prevent your catalogue from drifting back into irrelevance, build a periodic review process where ChatGPT analyses learner feedback, completion rates, and quiz results. Export comments, survey results, and basic usage metrics from your LMS and feed them into the model in anonymised form.
Ask ChatGPT to detect patterns: which courses are consistently rated as irrelevant, which modules are abandoned halfway, or where learners complain about outdated examples. Combine these insights with your skills mapping to decide what to retire, merge, or update.
Example prompt:
You are an L&D analyst.
Input:
- Anonymised learner comments about 20 courses
- Completion rates, average time spent, test scores
Task:
- Identify courses with low perceived relevance
- Summarise the main reasons learners cite
- Suggest 3 concrete actions per course: retire, update (with focus), or keep
- Prioritise actions that will have the highest impact on relevance.Expected outcome: a living learning catalogue that is regularly pruned and sharpened, instead of a static list that becomes more irrelevant each year.
Integrate ChatGPT into HR and Manager Workflows
Finally, make AI-powered learning recommendations part of everyday HR workflows instead of a separate “AI project”. For example, equip HRBPs and managers with a ChatGPT-based assistant (via chat interface or intranet widget) that can, on demand, suggest 2-3 relevant learning options for a given performance review outcome or career move.
Provide the tool with access to your skills framework and curated course list; restrict it from suggesting anything outside approved content. Train managers on a few standard prompts they can use during check-ins with employees.
Example prompt for managers:
You are an HR learning assistant.
Employee context:
- Role: Senior Accountant
- Development goal: improve data storytelling and influence non-finance stakeholders
- Time available: 2 hours per week for 8 weeks
Task:
- Suggest a focused learning plan using our approved internal courses (see list)
- For each week, recommend 1-2 assets and 1 practical on-the-job activity
- Provide talking points I can use in our 1:1s to reinforce relevance.Expected outcome: higher perceived relevance of training because recommendations are woven into real conversations, not pushed from a central campaign, and AI is used to support—not replace—manager judgement.
Across organisations that apply these practices seriously, realistic outcomes include a 20–40% reduction in unused or low-impact courses, noticeable increases in engagement with targeted learning paths, and faster time-to-proficiency for key roles. The exact numbers will vary, but the pattern is clear: when HR uses ChatGPT to align content with roles, skills, and real work, learning investments become easier to justify and far more effective.
Build an AI system with us now!
We build a proof of concept for your problem for 5,000–8,000€. You get a tangible demo instead of slides with promises.
Frequently Asked Questions
ChatGPT can analyse your existing course catalogue, role profiles, and skills frameworks to identify where content is too generic, outdated, or misaligned with current roles. By mapping each course to specific skills and roles, it quickly highlights low-relevance modules and duplication. HR and L&D teams can then decide what to retire, update, or repurpose, and use ChatGPT to draft role-based learning paths that address actual skill gaps instead of pushing one-size-fits-all training.
You don’t need a large data science team to start. The core requirements are: an L&D lead who understands your skills framework and business priorities, someone who can extract and prepare course and role data (often HRIS/LMS admins), and a few HR or learning professionals willing to test and refine prompts. Basic prompt-writing skills and a clear understanding of your compliance constraints are more important than deep AI expertise. For more advanced setups (e.g. private models, LMS integration), technical support from IT or an external partner like Reruption is recommended.
For a focused pilot on a single target group, most organisations can see tangible results within 4–8 weeks. In the first 2–3 weeks you typically prepare data, define the skills and roles in scope, and run initial mapping and learning path generation with ChatGPT. The following weeks are used for SME review, deploying updated or tailored content, and collecting early feedback from learners and managers. Measurable improvements in relevance (engagement, satisfaction, manager feedback) usually appear within one or two learning cycles; deeper business impact (e.g. time-to-proficiency) takes longer but can be tracked.
Yes, when implemented with clear goals, ChatGPT can significantly lower content production and maintenance costs while increasing the impact of your existing licences and materials. Instead of commissioning fully new courses, you can refactor and personalise what you already own. To prove ROI, track metrics such as reduced number of low-usage courses, higher completion and relevance ratings, reduced time for content creation, and performance indicators tied to targeted learning paths. When HR can show that fewer, more relevant courses lead to better outcomes, it becomes much easier to defend or even expand the L&D budget.
Reruption combines AI strategy, engineering and enablement to move you from idea to working solution. Through our AI PoC offering (9.900€), we define a concrete use case—such as mapping your catalogue to skills and generating role-based learning paths—then build a functioning prototype that proves technical and business feasibility. We handle use-case scoping, model selection, rapid prototyping, and performance evaluation, so you see real outputs with your data instead of slideware.
Beyond the PoC, our Co-Preneur approach means we embed with your team, challenge assumptions in your current L&D setup, and help you integrate ChatGPT into existing HR and learning workflows. We focus on security and compliance, design the right prompts and guardrails, and equip HR and managers to use the solution confidently. The result is not just a tool, but an AI-first way of designing relevant, effective learning experiences inside your organisation.
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