Turn Limited Learning Insights Into Actionable Skill Data With Claude
HR and L&D teams are under pressure to prove that training actually builds skills, not just generate completion stats. This page shows how to use Claude to turn scattered learning data into clear insights about what works, what doesn’t, and where to invest your L&D budget. You’ll get both strategic guidance and concrete prompts you can use immediately.
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The Challenge: Limited Learning Insights
Most HR and L&D teams are swimming in learning data but starving for real insight. You have LMS exports, feedback forms, survey responses, webinar recordings and assessment scores – yet you still struggle to answer a basic question: which learning modules actually improve skills and performance? As a result, decisions about what to keep, fix or retire in your catalog are often based on gut feeling or satisfaction scores, not on hard evidence.
Traditional approaches to training analytics were built for a different era. Standard LMS dashboards focus on attendance, completion rates and quiz scores, not on behavioural change or business impact. Manual analysis of open text feedback and training logs is slow and inconsistent, so it rarely happens at scale. Even when HR exports data into spreadsheets or BI tools, you need scarce data skills to turn that into meaningful insight, and you still miss the nuance in comments, discussions and coaching notes.
The business impact is significant. Without clear insight into learning effectiveness, budgets get spread thin across generic programs that may not move the needle. High-potential employees waste time on irrelevant modules while critical skill gaps remain uncovered. It becomes harder to defend L&D investments against other priorities because you cannot clearly link programs to measurable improvements in performance, retention or internal mobility. Over time, your organisation falls behind competitors that can more precisely develop and deploy skills.
This challenge is real, but it is solvable. Modern AI for HR and L&D – especially large language models like Claude – can process long-form training data, surface patterns hidden in comments and transcripts, and connect learning activities to observable skill signals. At Reruption, we’ve helped organisations build AI-first learning tools and analytics layers that turn unstructured training information into clear, actionable narratives. In the rest of this guide, you’ll see practical ways to use Claude to close your learning insight gap and make sharper, evidence-based L&D decisions.
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From Reruption’s work building AI-powered learning and training solutions, we see the same pattern across HR teams: the data to understand learning effectiveness already exists, but it is fragmented across systems and too unstructured to analyse manually. Tools like Claude change the economics of analysis by digesting long logs, survey responses and transcripts and turning them into structured, decision-ready insights. Our perspective: the opportunity isn’t just to add another report, but to redesign how your organisation measures skills, learning impact and L&D ROI with an AI-first lens.
Think in Terms of Skills, Not Courses
The biggest strategic shift when using Claude for learning analytics is to move from a course-centric view to a skill-centric one. Instead of asking, “Is this training popular?” the core question becomes, “Which specific skills does this module build, and how effectively?”. Claude can help you map learning content, assignments and assessments to a defined skills taxonomy, then track where learners show improvement or persistent gaps.
At a strategic level, this means aligning HR, L&D and business leaders on a common language for skills and proficiency levels. Before you scale any AI-based analysis, invest time in defining 20–50 priority skills for your critical roles and how they show up in behaviour, artifacts (e.g., project deliverables) and feedback. Claude can then be instructed to evaluate comments, reflections and assignments through that skills lens, giving you insight that connects directly to workforce planning and talent decisions.
Start With One High-Value Learning Journey
It’s tempting to point Claude at your entire L&D catalog, but strategically it’s more effective to begin with a single, high-impact learning journey – for example, leadership development, sales enablement or onboarding for critical roles. This concentrates your effort where insight will have immediate business impact and makes it easier to validate whether Claude’s outputs are useful.
Pick a journey where you have sufficient data: sign-ups, completions, quizzes, feedback forms, and ideally manager evaluations or performance indicators. Use Claude to analyse this end-to-end experience and generate a “learning effectiveness narrative”: what works, what confuses learners, where engagement drops, and which elements correlate with better outcomes. Once stakeholders see concrete, trusted improvements for one journey, you’ll find it much easier to secure support to expand AI analytics to other programs.
Design a Human-in-the-Loop Review Process
Claude can dramatically accelerate learning insights, but HR should not outsource judgement entirely to an AI model. Strategically, you need a human-in-the-loop process that defines what is automated (aggregation, clustering, drafting insights) and what is owned by experts (interpretation, prioritisation, intervention design). This protects against misread nuance and builds trust with works councils and leadership.
Set up a recurring cadence where L&D specialists and HR business partners review Claude’s summaries and recommendations together. Encourage them to challenge the output, cross-check with raw data and enrich the findings with contextual knowledge about organisational changes, cultural factors or local specifics. Position Claude as a “learning insights analyst” whose work must be validated and refined, not as a black-box decision maker.
Prepare Your Data and Governance Upfront
Using AI in HR analytics touches sensitive data – performance feedback, open text comments, even health- or diversity-related issues in some contexts. Strategically, you must prepare your data pipelines and governance before scaling Claude. Decide which sources are in scope (LMS logs, survey results, 360 feedback, coaching notes, call transcripts), what needs to be anonymised or pseudonymised, and which regions or groups require additional safeguards.
Work with Legal, IT and Data Protection Officers to define clear policies: what data is processed by Claude, how long it’s stored, and who can access the outputs. A well-governed approach not only reduces compliance risk but also increases employee trust when you later communicate that AI is being used to improve learning, not to micro-monitor individuals.
Frame the Initiative Around Business Outcomes, Not Technology
For executives and line managers, the value of AI-powered learning analytics is not about Claude itself, but about solving concrete business problems: ramping new hires faster, closing critical skill gaps, or improving quota attainment. Strategically, you should frame your Claude initiative in these terms from day one.
Define a small set of target outcomes (e.g., reducing time-to-productivity for new sales reps by 20%, or cutting ineffective training hours by 15%). Then use Claude’s analysis to regularly report progress on these outcomes, not just on engagement metrics. This positions HR as a strategic partner who brings data-backed recommendations instead of more “training requests”, and it makes it much easier to argue for a higher L&D budget when you can show demonstrable impact.
Using Claude to fix limited learning insights is less about generating pretty dashboards and more about fundamentally changing how HR understands skills, learning journeys and business impact. With the right scope, governance and human-in-the-loop process, Claude becomes a force multiplier for your L&D team, turning messy training data into clear, actionable direction. At Reruption, we combine this AI capability with hands-on implementation and our Co-Preneur mindset to build learning insight systems that actually get used. If you’re exploring how to move beyond completion rates and into real skill analytics, we’re happy to help you test the approach on a concrete use case and scale from there.
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Unify Your Learning Data Into Analyse-Ready Packs for Claude
Claude is powerful with long, complex inputs – but you still need to give it well-structured “packs” of data. Start by exporting relevant information from your LMS and survey tools: course IDs and titles, module outlines, attendance and completion logs, quiz results, open text feedback, and (if available) manager evaluations linked to the same cohort.
Combine these into logical bundles. For example, create one file per program per cohort that contains: program description, learning objectives, module list, participant list (anonymised if needed), and all associated comments and survey responses. Then, feed these bundles into Claude with clear instructions about what to extract: themes, bottlenecks, skill improvements and correlations with performance indicators.
Example prompt to analyse one learning program:
You are an HR learning analytics expert.
You receive all data for a single learning program and its latest cohort.
1. Read the full input carefully.
2. Identify:
- The 3–5 main strengths of the program
- The 3–5 main weaknesses or confusion points
- Recurring themes in open text feedback
3. Map issues to specific modules or activities.
4. Suggest 5 concrete improvements ranked by impact/feasibility.
Return your answer in this structure:
- Program summary (5–7 sentences)
- Strengths
- Weaknesses
- Module-level issues
- Recommended improvementsExpected outcome: HR gains a clear, narrative overview of each program without manually reading hundreds of comments, making review cycles faster and more consistent.
Use Claude to Build and Refine a Skills Taxonomy From Real Learning Data
If you don’t yet have a formal skills framework, Claude can help you bootstrap one from your existing learning content and assessments. Start by feeding Claude a representative set of course descriptions, learning objectives and assessment questions from your highest-value programs.
Ask Claude to infer the underlying skills and cluster them into a draft taxonomy, then iterate with HR and business stakeholders. You can then reuse this taxonomy to tag both content and learner outputs.
Example prompt to draft a skills taxonomy:
You are helping an HR team build a skills taxonomy.
Below you find course descriptions, learning objectives and exam questions.
Tasks:
1. Infer 20–40 distinct skills covered by this material.
2. Group them into logical categories.
3. For each skill, provide:
- Name (max 4 words)
- Short definition
- 3 example behaviours at basic proficiency
- 3 example behaviours at advanced proficiency
Return the result as a structured list.Expected outcome: a practical, business-grounded skills framework that can later be refined, rather than a theoretical model that never connects to actual learning content.
Analyse Open Text Feedback and Transcripts for Root Causes
One of Claude’s strengths is handling long-form, messy text, such as survey comments, workshop notes or webinar transcripts. Use this to go beyond “average satisfaction” and identify root causes of learning issues. For example, you can upload all comments for a leadership program plus transcripts from key sessions and ask Claude to cluster issues, quote representative examples and relate them to specific modules.
Combine qualitative insight with simple quantitative counts (e.g., how often each theme appears) to prioritise action.
Example prompt to extract root causes:
You are analysing qualitative feedback for an HR learning program.
The input contains: survey comments, chat logs, and selected session transcripts.
1. Cluster feedback into themes (e.g., pace too fast, examples not relevant).
2. For each theme, provide:
- A short description
- 2–3 direct quotes from participants
- The likely root cause
- Suggested design changes to address it
3. Indicate which themes appear to have the highest impact on learning effectiveness.Expected outcome: instead of guessing why a module underperforms, HR sees concrete patterns and can make targeted design changes.
Link Learning Data to Performance Signals With Claude-Assisted Analysis
To move from engagement metrics to impact, you need to connect learning data with performance signals like sales numbers, productivity metrics or internal mobility. Claude can’t directly do statistical analysis, but it can help you reason about patterns and generate hypotheses once you supply aggregated summaries from your BI tools.
For example, you can export a simple table summarising performance by cohort (e.g., average sales before vs. after training) and provide Claude with narrative descriptions of the program and any changes made over time.
Example prompt to reason about impact:
You are an HR analytics advisor.
Below you find:
- A description of the learning program and its objectives.
- A table (in text form) with average performance metrics by cohort.
- Notes about major changes to the program across cohorts.
Tasks:
1. Identify plausible relationships between program changes and performance trends.
2. Highlight where the data is inconclusive or might be influenced by external factors.
3. Suggest 3–5 follow-up analyses HR should run with BI to validate impact.
4. Propose 3 concrete decisions HR could test in the next cohort.Expected outcome: HR gains a structured, narrative interpretation of numbers that can be discussed with Finance and business leaders, supporting a stronger L&D business case.
Generate Personalised Learning Insights and Micro-Recommendations for Learners
Beyond program-level analytics, Claude can produce individualised learning insights for employees without creating a huge manual workload for HR. Feed Claude anonymised or employee-consented data for a learner: completed modules, quiz results, reflection assignments and selected work samples (e.g., sales emails, project reports).
Ask Claude to identify strengths, gaps and recommended next steps, phrased in a supportive, coaching style. These outputs can be embedded into your LMS or shared with managers to guide development conversations.
Example prompt for individual learning feedback:
You are a supportive learning coach.
You receive:
- A list of modules the learner has completed
- Their quiz scores
- Short reflection answers and sample work outputs
1. Identify the learner's top 3 strengths.
2. Identify the 3 most relevant skill gaps based on the input.
3. Suggest a personalised 4-week learning focus:
- 2–3 modules to revisit or deepen
- 2 on-the-job practice ideas per skill gap
4. Write the feedback in a constructive tone, addressing the learner directly.Expected outcome: employees receive targeted guidance that makes learning feel relevant and actionable, while HR gets scalable personalised support without adding headcount.
Establish a Recurring Claude-Assisted L&D Review Cadence
To make AI-powered learning insights stick, embed Claude into your regular L&D operating rhythm. For example, create a quarterly review ritual where HR exports updated data for priority programs, runs the standardised Claude prompts, and then discusses the outputs with stakeholders.
Use consistent templates for Claude’s analysis and store results centrally, so you can track how insights and actions evolve over time. Over 2–3 cycles, you’ll see which recommendations lead to measurable improvements and where your prompts or data need refinement.
Expected outcomes (realistic ranges over 6–12 months, depending on scale and maturity): 30–50% reduction in manual time spent on feedback analysis, 10–20% reduction in clearly ineffective training hours, faster iteration cycles on key programs, and a much stronger evidence base to defend or expand your L&D budget.
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Frequently Asked Questions
Claude can process the unstructured learning data that traditional LMS reports ignore: open text feedback, reflection assignments, coaching notes and workshop transcripts. By analysing this content alongside attendance and quiz data, Claude can highlight which modules actually build specific skills, where learners are confused, and which parts of a program drive the most behaviour change.
Instead of just knowing that 92% completed a course, HR gets narrative insights such as “Module 3 is where most learners drop off and report confusion about applying the concept in their role.” This allows you to redesign or drop underperforming elements and invest more in what works.
You don’t need a full data science team to start. At minimum, you need:
- An HR or L&D lead who understands your learning programs and business context.
- Basic data extraction support from IT or your LMS admin to export logs, survey results and transcripts.
- Someone comfortable experimenting with Claude prompts and iterating based on the outputs.
Reruption typically helps clients set up the first end-to-end workflow: defining the data bundle, crafting robust prompts, and designing the review process. Once this is in place, your HR team can operate and refine the process without heavy technical dependencies.
For a focused use case, you can see meaningful insights in a matter of weeks, not months. A typical timeline looks like this:
- Week 1–2: Select one priority learning journey, export data, and define the first analysis prompts.
- Week 3–4: Run Claude analyses, review outputs with L&D, and implement quick wins (e.g., clarifying confusing modules, adjusting examples).
- Month 2–3: Refine prompts, add additional data sources (e.g., manager feedback), and formalise a recurring review rhythm.
Measurable impact on skills and performance (e.g., better assessment scores, shorter ramp-up time) typically appears over one or two program cycles as improvements take effect. The key is to start narrow, learn quickly and scale only what demonstrably adds value.
The ROI comes from three main levers:
- Time savings: Automating analysis of feedback and transcripts can cut manual review time by 30–50%, freeing HR and L&D to focus on design and stakeholder alignment.
- Better allocation of L&D budget: With clear evidence of what works, you can reduce or retire low-impact content, often cutting 10–20% of ineffective training hours and reinvesting in high-value areas.
- Improved performance: By identifying and addressing specific skill gaps, organisations see faster onboarding, higher sales effectiveness, or fewer quality issues – all of which have direct financial impact.
Claude itself is relatively low-cost compared to traditional analytics projects; the main investment is in setting up the right workflows. That’s why we recommend starting with a contained pilot that can demonstrate concrete savings or gains within one budget cycle.
Reruption supports organisations from idea to working solution with an AI-first, Co-Preneur mindset. For this specific challenge of limited learning insights, we typically start with our AI PoC offering (9,900€), where we co-define a concrete use case (e.g., analysing one key learning journey), build a Claude-based prototype, and test whether it delivers actionable insights with your real data.
Beyond the PoC, we help you embed Claude into your HR and L&D workflows: designing data pipelines, refining prompts, ensuring security & compliance, and training your team to operate the solution themselves. We don’t just hand over slides; we work inside your organisation’s reality until a functioning learning insights process is live and delivering value.
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