Training Evaluation Models

Kirkpatrick is often criticized. But rarely fully understood. Let's change this 👇 The model is simple. It describes four levels of evaluating learning impact: Level 1 — Reaction How participants experience the learning. Level 2 — Learning What knowledge and skills they acquire. Level 3 — Behavior How their on-the-job behavior changes. Level 4 — Results What organizational outcomes improve. That’s it. Four levels. And yet, it is frequently dismissed as outdated or simplistic. Why? Because we often treat it as a measurement checklist, instead of a design framework. Kirkpatrick is not just about evaluating training. It’s about thinking in cause-and-effect logic. Instead of asking, “Was the training good?” we should be asking a sequence of strategic questions. When designing: – What business outcome must change? – What behavior must shift to deliver that outcome? – What knowledge and skills are required? – What learning experience will enable mastery? And when evaluating: – How did participants evaluate the experience? – How well did they acquire the knowledge and skills? – How did behavior change at work? – What changed in the targeted business indicators? Planning must start from the top (Results). Measurement must begin from the bottom (Reaction). Think forward. Measure backward. Of course, the model has nuances - leading and lagging indicators, performance environment, manager accountability, isolation factors. But beneath the complexity lies a simple and powerful logic. The pyramid is not a hierarchy of surveys. It’s a chain of impact. That’s why I created this visual, to show the model not as theory, but as a practical thinking framework. How do you approach Kirkpatrick in your projects? #designforclarity #LearningAndDevelopment #InstructionalDesign #LearningStrategy #Kirkpatrick #LearningImpact #LXD #CorporateLearning

*** 🚨 Discussion Piece 🚨 *** Is it Time to Move Beyond Kirkpatrick & Phillips for Measuring L&D Effectiveness? Did you know organisations spend billions on Learning & Development (L&D), yet only 10%-40% of that investment actually translates into lasting behavioral change? (Kirwan, 2024) As Brinkerhoff vividly puts it, "training today yields about an ounce of value for every pound of resources invested." 1️⃣ Limitations of Popular Models: Kirkpatrick's four-level evaluation and Phillips' ROI approach are widely used, but both neglect critical factors like learner motivation, workplace support, and learning transfer conditions. 2️⃣ Importance of Formative Evaluation: Evaluating the learning environment, individual motivations, and training design helps to significantly improve L&D outcomes, rather than simply measuring after-the-fact results. 3️⃣ A Comprehensive Evaluation Model: Kirwan proposes a holistic "learning effectiveness audit," which integrates inputs, workplace factors, and measurable outcomes, including Return on Expectations (ROE), for more practical insights. Why This Matters: Relying exclusively on traditional, outcome-focused evaluation methods may give a false sense of achievement, missing out on opportunities for meaningful improvement. Adopting a balanced, formative-summative approach could ensure that billions invested in L&D truly drive organisational success. Is your organisation still relying solely on Kirkpatrick or Phillips—or are you ready to evolve your L&D evaluation strategy?

When I launched my first GenAI feature, I had to completely relearn how to define "Is it good enough to launch"? I was comfortable with Traditional ML metrics. If you asked me about Precision, Recall, or F1 Scores, I could have a great discussion with Data Scientists about whether or not we are ready to launch. But when my engineering lead asked me for the Go/No-Go criteria for our new LLM feature, those metrics didn't help me much. He asked a simple question: "How do we know this is good enough to ship?" To be honest? At first I didn't know what to answer... I realized I could speak "Traditional ML Metrics", but I didn't know how to speak "LLM Quality". My initial strategy was what most teams do: Vibe Check it and launch because there's pressure from an executive. We ran a few files through the model, read the outputs, and nodded. "Yeah, looks good. Ship it!" 🚀 That works for a prototype. (Don't do that in production...) We tend to test for the 'Happy Paths' that we know our LLM can do, and tend to ignore thoroughly testing all the other things that might break your feature. Through that launch (and a lot of research since), I learned that you need a mix of three specific evaluation layers to actually trust your launch: ⚡ 1. Code-Based Evals (Sanity Checks) Start here - Use standard code (Python/Regex) to catch the "dumb" errors instantly like... - Did the model return valid JSON? - Is the answer under 50 words? It’s instant and free. It doesn't tell you if the answer is smart, but it tells you if it's broken. 🧠 2. Human Evals (Your Ground Truth) Most teams try to skip this layer because it is slow, expensive, and manual, and they want to jump straight to automation. It's a trap! You need real humans to grade the outputs to define what "Good" looks like. This creates your Golden Dataset—the source of truth that you measure everything else against. If you don't do this, your automated metrics are just measuring noise. The toughest part about Human Evals is convincing your stakeholders (esp. leadership) that you need to spend ALL THAT TIME doing Human Evaluations - they are critical, don't skip them. 🤖 3. LLM-as-a-Judge (Fast and Scalable) Ideally, you'll use a stronger model to grade your own model’s output. Something like... - Input: "Compare the Model's answer to the Golden Answer." - Criteria: "Rate accuracy on a scale of 1-5." This lets you run thousands of tests in minutes. It allows you to scale your "human" logic without the human bottleneck and keep evaluating as you make changes. The biggest mistake you can make is thinking you can automate trust. You can't. You have to put in the hard, manual work (Human Evals) too. --- 👋 How are you currently measuring quality for your AI features? Are you using "vibes" or do you have a formal eval pipeline? --- 💎 Need help with Evals? George Zoto, Marily Nika, Ph.D and I put together a hands-on AI Evals course for you - Check my comment below for a code with a discount!

What actually happens when LLMs evaluate LLM-generated research? 🐍 Scientific quality quietly collapses. New research analyzing 125,000+ paper-review pairs from ICLR, NeurIPS, and [ICML] Int'l Conference on Machine Learning just dropped on arXiv, and the findings are a wake-up call for scientific integrity. When LLMs review research papers, the core problem isn’t hallucination. It’s Rating Compression. LLM reviewers are trained to be helpful and polite. That makes them very bad at giving strong rejections and strong endorsements. Everything gets squeezed into a beige middle - grammatically perfect, low-variance, low-conviction reviews. This creates three dangerous illusions: → It looks like LLM reviewers prefer LLM-written papers. In reality, weaker papers tend to use more AI writing, and LLM reviewers are simply too “nice” to flag mediocrity. → The signal that separates breakthrough research from plausible-sounding work disappears. → Worst of all, LLM-assisted meta-reviews are significantly more likely to flip a decision to “Accept” given the same underlying scores than a human meta-reviewer would. If we use LLMs to write papers and to grade papers, we don’t just lose the human touch - we lose the ability to distinguish insight from polish. Some takeaways that I found useful... • Authors: If you use an LLM to pre-review your paper, ignore the score. Focus only on critiques of logic, novelty, and assumptions. • Reviewers: Watch for beige reviews, polished language with no strong stance on novelty or impact. • Chairs: High confidence + low variance is classic bot behavior. Evaluation systems need variance-aware checks. This isn’t about banning LLMs from peer review. It’s about understanding their systematic biases and designing processes that compensate for them. As an engineer, I love automation, especially when there are 20k+ submissions. But judgment? That still has to stay human. IMO LLMs can assist, but should never be the final arbiter. Curious where others draw the line 💭 #AIEthics #PeerReview #ICML #ICLR #NeurIPS

𝐓𝐡𝐞 𝐒𝐞𝐜𝐫𝐞𝐭 𝐭𝐨 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐓𝐡𝐚𝐭 𝐀𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐖𝐨𝐫𝐤𝐬? 𝐒𝐭𝐚𝐫𝐭 𝐚𝐭 𝐭𝐡𝐞 𝐄𝐧𝐝. 🏁 I used to think my job as an L&D professional started with a syllabus. I was wrong. Recently, I was tasked with building a learning solution for our Talent Acquisition (TA) team. The goal wasn’t just to "train recruiters"—it was to solve a business problem. Instead of looking at what they needed to know (Level 2), I started with what the business needed to achieve (Kirkpatrick Level 4). The "Reverse" Approach I didn’t start with slides. I started by analyzing Voice of the Customer (VOC) survey results, focusing on various metrics from both Hiring Managers and Candidates. Working Backwards: ✅ Level 4 (Results): I defined the business KPI. ✅ Level 3 (Behavior): Based on the VOC metrics, I identified the specific actions recruiters needed to change—specifically around "Precision Intake" and "Candidate Experience Management." ✅ Level 2 & 1 (Learning & Reaction): Only then did I design the actual training content that addressed those specific behavior gaps. The Result? The training didn't feel like a chore; it felt like a solution. Because I built it based on the actual metrics revealed in the VOC surveys, the TA team saw immediate value, and the business saw a measurable shift in hiring efficiency. The Lesson: If you want your learning solutions to be more than just "check-the-box" exercises, stop asking "What should we teach?" and start asking "What does the data say I need to solve?" How do you use VOC data to shape your enablement programs? 👇 #LearningAndDevelopment #InstructionalDesign #TalentAcquisition #KirkpatrickModel #Enablement #DataDrivenLD #BusinessImpact

Exciting New Research on LLM Evaluation Validity! I just read a fascinating paper titled "LLM-Evaluation Tropes: Perspectives on the Validity of LLM-Evaluations" that addresses a critical issue in our field: as Large Language Models (LLMs) increasingly replace human judges in evaluating information retrieval systems, how can we ensure these evaluations remain valid? The paper, authored by researchers from universities and companies across multiple countries (including University of New Hampshire, RMIT, Canva, University of Waterloo, The University of Edinburgh, Radboud University, and Microsoft), identifies 14 "tropes" or recurring patterns that can undermine LLM-based evaluations. The most concerning trope is "Circularity" - when the same LLM is used both to evaluate systems and within the systems themselves. The authors demonstrate this problem using TREC RAG 2024 data, showing that when systems are reranked using the Umbrela LLM evaluator and then evaluated with the same tool, it creates artificially inflated scores (some systems scored >0.95 on LLM metrics but only 0.68-0.72 on human evaluations). Other key tropes include: - LLM Narcissism: LLMs prefer outputs from their own model family - Loss of Variety of Opinion: LLMs homogenize judgment - Self-Training Collapse: Training LLMs on LLM outputs leads to concept drift - Predictable Secrets: When LLMs can guess evaluation criteria For each trope, the authors propose practical guardrails and quantification methods. They also suggest a "Coopetition" framework - a collaborative competition where researchers submit systems, evaluators, and content modification strategies to build robust test collections. If you work with LLM evaluations, this paper is essential reading. It offers a balanced perspective on when and how to use LLMs as judges while maintaining scientific rigor.

💡 "What if the key to your success was hidden in a simple evaluation model?” In the competitive world of corporate training, ensuring the effectiveness of programs is crucial. 📈 But how do you measure success? This is where the Kirkpatrick Evaluation Model comes into play, and it became my lifeline during a challenging time. ✨ The Turning Point ✨ Our company invested heavily in a new leadership development program a few years ago. I was tasked with overseeing its success. Despite our best efforts, the initial feedback was mixed, and I felt the pressure mounting. 😟 Then, I discovered the Kirkpatrick Evaluation Model. This four-level framework was about to change everything: 🔹Level 1: Reaction - I began by gathering immediate participant feedback. Were they engaged? Did they find the training valuable? This was my first step in understanding the initial impact. 👍 🔹 Level 2: Learning - Next, I measured what participants learned. We used pre-and post-training assessments to gauge their acquired knowledge and skills. 🧠📚 🔹 Level 3: Behavior - The real test came when we looked at behavior changes. Did participants apply their new skills on the job? I conducted follow-up surveys and observed their performance over time. 👀💪 🔹 Level 4: Results - Finally, we analyzed the overall impact on the organization. Were we seeing improved performance and tangible business outcomes? This holistic view provided the evidence we needed. 📊🚀 🌈 The Transformation 🌈 Using the Kirkpatrick Model, we were able to pinpoint strengths and areas for improvement. By iterating on our program based on these insights, we turned things around. Participants were not only learning but applying their new skills effectively, leading to remarkable business results. This journey taught me the power of structured evaluation and the importance of continuous improvement. The Kirkpatrick Model didn't just help us survive; it helped us thrive. 🌟 Ready to transform your training initiatives? Let’s connect with a complimentary 15-minute call with me and discuss how you can leverage the Kirkpatrick Model to drive results. 🚀 https://lnkd.in/grUbB-Kw Share your experiences with training evaluations in the comments below! Let's learn and grow together. 🌱 #CorporateTraining #KirkpatrickModel #ProfessionalDevelopment #TrainingEffectiveness #ContinuousImprovement

“Train-the-trainers” (TTT) is one of the most common methods used to scale up improvement & change capability across organisations, yet we often fail to set it up for success. A recent article, drawing on teacher professional development & transfer-of-training research, argues TTT should always be based on an “offer-and-use” model: OFFER: what the programme provides—facilitator expertise, session design, practice opportunities, feedback, follow-up support & evaluation. USE: what participants do with those opportunities—what they notice, how they make sense of it, how much they engage, what they learn, & whether they apply it in real work. How to design TTT that works & sticks: 1. Design for real-world use: Clarify the practical outcome - what trainers should do differently in their next sessions & what that should improve for the organisation. Plan beyond the classroom with post-course support so people can apply learning. Space learning over time rather than delivering it in one intensive block, because spacing & follow-ups support sustained use. 2. Use strong facilitators: Select facilitators who know the topic & how adults learn, how groups work & how to give useful feedback. Ensure they teach “how to make this stick at work” (apply & sustain practices), not only “how to deliver a session.” 3. Make practice central: Build the programme around realistic rehearsal: deliver, get feedback, & practise again until skills become automatic. Use participants’ real scenarios (especially change situations) to strengthen transfer. Include safe practice for difficult moments (challenge, unexpected questions) & treat mistakes as learning. Build peer learning so participants learn with & from each other, not just the facilitator. 4. Prepare participants to succeed: Assess what participants already know & can do, then tailor the learning. Build confidence to use skills at work (confidence predicts application). Help each person create a simple, specific plan for when & how they will use the approaches in their next training sessions. 5. Ensure workplace transfer support: Enable quick application (opportunities to deliver training soon after the course), plus time & resources to do it well. Provide ongoing support (feedback, coaching, & encouragement) from leaders, peers &/or the wider organisation. 6. Evaluate what matters: Go beyond satisfaction scores - assess whether trainers changed their practice & whether this improved outcomes for learners & the organisation. Use findings to improve the next iteration as a continuous improvement cycle, not a one-off event. https://lnkd.in/eJ-Xrxwm. By Prof. Dr. Susanne Wisshak & colleagues, sourced via John Whitfield MBA

If you run a simulation program, the issue usually isn’t a lack of data, it’s that the data isn’t tied to decisions. How to make sim data move readiness: ↪️ Align to outcomes. Pick a short scorecard (3–5 items): escalation accuracy, time-to-intervention, near-misses, time-to-independent practice, and voluntary practice per week. ↪️ Instrument the cases. Capture decision points, timing, path taken, and remediation notes, then compare across cohorts and units. ↪️ Make it visible. Share one-page trends in huddles and 1:1s, not just the LMS. ↪️ Coach, then close the loop. Use the signals to target feedback, redeploy short scenarios, and re-measure after protocol changes or near-miss reviews. You’re already doing the hard work. Make the insights match the impact. VRpatients supports this flow, assign once, learners practice asynchronously, educators see per-learner analytics and exportable trends, without adding admin load. #HealthcareLeadership #SimulationEducation #ClinicalReadiness #VRinHealthcare #DataInHealthcare

Evaluating LLMs is hard. Evaluating agents is even harder. This is one of the most common challenges I see when teams move from using LLMs in isolation to deploying agents that act over time, use tools, interact with APIs, and coordinate across roles. These systems make a series of decisions, not just a single prediction. As a result, success or failure depends on more than whether the final answer is correct. Despite this, many teams still rely on basic task success metrics or manual reviews. Some build internal evaluation dashboards, but most of these efforts are narrowly scoped and miss the bigger picture. Observability tools exist, but they are not enough on their own. Google’s ADK telemetry provides traces of tool use and reasoning chains. LangSmith gives structured logging for LangChain-based workflows. Frameworks like CrewAI, AutoGen, and OpenAgents expose role-specific actions and memory updates. These are helpful for debugging, but they do not tell you how well the agent performed across dimensions like coordination, learning, or adaptability. Two recent research directions offer much-needed structure. One proposes breaking down agent evaluation into behavioral components like plan quality, adaptability, and inter-agent coordination. Another argues for longitudinal tracking, focusing on how agents evolve over time, whether they drift or stabilize, and whether they generalize or forget. If you are evaluating agents today, here are the most important criteria to measure: • 𝗧𝗮𝘀𝗸 𝘀𝘂𝗰𝗰𝗲𝘀𝘀: Did the agent complete the task, and was the outcome verifiable? • 𝗣𝗹𝗮𝗻 𝗾𝘂𝗮𝗹𝗶𝘁𝘆: Was the initial strategy reasonable and efficient? • 𝗔𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻: Did the agent handle tool failures, retry intelligently, or escalate when needed? • 𝗠𝗲𝗺𝗼𝗿𝘆 𝘂𝘀𝗮𝗴𝗲: Was memory referenced meaningfully, or ignored? • 𝗖𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻 (𝗳𝗼𝗿 𝗺𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀): Did agents delegate, share information, and avoid redundancy? • 𝗦𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗼𝘃𝗲𝗿 𝘁𝗶𝗺𝗲: Did behavior remain consistent across runs or drift unpredictably? For adaptive agents or those in production, this becomes even more critical. Evaluation systems should be time-aware, tracking changes in behavior, error rates, and success patterns over time. Static accuracy alone will not explain why an agent performs well one day and fails the next. Structured evaluation is not just about dashboards. It is the foundation for improving agent design. Without clear signals, you cannot diagnose whether failure came from the LLM, the plan, the tool, or the orchestration logic. If your agents are planning, adapting, or coordinating across steps or roles, now is the time to move past simple correctness checks and build a robust, multi-dimensional evaluation framework. It is the only way to scale intelligent behavior with confidence.