Did the Training Actually Work? What New Research Says About Measurement, AI, and Transfer

by Chris Packert | Oct 17, 2026 | Featured, Learning Strategy

Ep. 01 header for L&D Insights at Altitude: "Did the Training Actually Work?" over a night sky and Rocky Mountain ridgeline.

Emerge ID: Award-winning learning design. AI-powered development. Better training at a lower cost.

In this episode, our hosts Nora and Ben walk through seven current sources, one professional body report and six peer-reviewed studies, and ask the same thing of each: does it actually change how people work? Here is what our team took from them.

Watch or listen to Episode 1 on YouTube

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Employers are building talent, so "people attended" is no longer enough

The Chartered Institute of Personnel and Development released its Resourcing and Talent Planning Report 2026 (Hogarth and McCartney) in September, surveying 1,014 UK HR professionals in resourcing and talent planning. Forty-eight percent of employers increased their efforts to develop talent in-house over the past year, and 91 percent are already upskilling existing employees or would consider it to fill hard-to-recruit roles. Among organizations that acted on retention, increasing learning and development opportunities was the most common step, at 45 percent.

Building rather than buying raises the bar: when learning fills skill gaps, completing a course only matters if people can now do the job.

Proving it depends on the measure, and a review in Human Resource Development Quarterly shows how easily measures go wrong. Josef Guggemos and Michael Goller reexamined 325 instruments from 92 workplace learning studies. In a reflective measure, each item is a sign of one underlying quality, so the items should rise and fall together. In a formative measure, each item is a distinct ingredient, and together they make up what is measured, so dropping one changes the score's meaning. Depending on how strictly the authors coded them, about 35 to 50 percent of instruments were formative by design but analyzed as reflective. Analyzing one kind as the other can bias the results. The lesson for practice: before you put a survey score in front of your CFO, ask what the instrument measures and how it was built.

Who is doing the thinking? How AI use shapes ownership and skill

AI raises a related question: when a tool does part of the work, who builds the skill? In the British Journal of Educational Technology, Yanyi Wu and Xinyu Lu studied GenAI reliance and agency among 342 university students in 85 groups in an AI-supported collaborative writing course, measured three times. When a student leaned on generative AI more than usual, their sense of agency, their ownership of the work, tended to drop afterward. It is observational and involved students, not employees, so we translate with care. The authors advise keeping the learner's responsibility for interpretation and judgment visible.

A second study, on a multi-agent AI system, shows setup matters too. Xiaoyan Chu, Hongzhu Dai, and Xuesong Zhai worked with 87 students in an instructional design course using the system. Groups working with the agents together were linked to higher design project scores, while individuals working alone showed bigger gains in critical thinking awareness. One course and a quasi-experiment make it an early signal. Together, the two studies point the same way: how much people lean on AI, and how it is configured, shapes what they get better at.

Our view: AI should serve the learning goal, not set it. As the episode puts it, "So the question isn't, should we use AI? It's, what do we want people to get better at?" If an AI assistant writes the action plan, whose plan is it?

Overloaded sessions: what you cut matters

In the International Journal of Training and Development, Nattapon Aunhabundit, I-Chun Chen, and Allan Lu studied graphic recording with 100 managers at one company. Half had training with graphic recording, an artist drawing key ideas live. The more overloaded people felt, the worse their reactions and test scores, but that drop was flatter in the graphic recording group. Think of it as a cushion, not a cure. The bigger fix starts with what you cut. The study measured reactions and a knowledge test right after training, not on-the-job behavior. Whether people use it depends on what comes next.

What shapes whether new skills stick after training

Transfer, using what you learned once you are back on the job, is where most training succeeds or fails. Two studies in the European Journal of Training and Development show how much depends on what follows.

Joerg Hruby and Carlos Canfield surveyed 138 alumni of global leadership programs six to twelve months after training. Global mindset and transfer climate, meaning the support around people back at work, both predicted transfer, and where the climate was strong, individual capability mattered less. The authors suggest tailoring post-training support to each learner. The design is cross-sectional and self-reported, so it shows association, not cause.

Charlotte Diekmann and Alexander Pundt tracked 62 leaders after leadership training, checking in every week for five weeks. The link between average transfer satisfaction and burnout tendency depended on how much that satisfaction swung from week to week. Their title calls transfer an emotional rollercoaster, and many programs step away right when the hard part begins. Support on the job belongs inside the design.

Three takeaways

  1. Measure what people can do, not who attended. As employers lean on in-house development, completion rates are not evidence of capability. Check that your measures are built to measure what you claim.
  2. Design AI use so human judgment stays in the lead. Heavier reliance on AI was linked to lower ownership, and how AI was configured shaped what learners gained. Decide what people must be able to do first, then where AI fits.
  3. Plan for the stretch after the course ends. Transfer climate predicted whether skills were used, and transfer satisfaction moved week to week. Support on the job is part of the design.

The practical challenge

Pick one course your team developed this year and ask: how do we know it changed what people do? If the answer is completion rates, that is your starting point.

Let's design for what people must be able to do

AI can build training in an afternoon now. The hard part is making sure it works on the job. That is what our Complete Learning Architecture approach is for: defining what people need to do differently, the support around them at work, where AI helps and where human judgment must lead, and how you will know it worked.

Emerge ID is led by the 2020 USOPC Coach Educator of the Year recipient with more than 30 years in learning and development. If your next initiative needs to change what people do, we invite you to consider Emerge ID for your next project: https://emergeid.com/

Find more episodes of L&D Insights at Altitude every two weeks.

Sources cited in Episode 1

This image shows the author of the blog posts.

Written by Chris Packert

Chris Packert is the founder and CEO of Emerge ID, a learning architecture and instructional design company that helps organizations design, build, launch, and improve meaningful learning experiences. With more than 30 years of experience across K–12 education, higher education, workforce development, military distance education, and sport education, Chris brings a practical, systems-based approach to learning strategy. His work focuses on helping teams clarify performance needs, create accessible and engaging learning solutions, implement effective learning technology, and measure impact beyond completion data. Through Emerge ID, Chris partners with organizations to turn complex learning challenges into clear, usable, and measurable learning experiences.

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