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David L. Ward

ReadyMade Answers To New Trainer Queries

Creative Thinking October 1960

David Hosmer, EdM, CPTD, CCM, is an independent practitioner doing business as Cascading Coaching. He is frequently called upon for his expertise in strategic organizational redesign, leadership and management development, career services, as well as design and facilitation of micro- and macro-level interactive learning initiatives.Hosmer’s experience spans more than 25 years in varied industries, repeatedly exceeding expectations in the areas of L&D, coaching, talent management, and organizational effectiveness. For 15 years, he was a director in several renowned domestic and global organizations. He has built and transformed four OD and talent development functions aligned with company strategic imperatives. Several of his teams have won or have been nominated for internal and external awards.He has fulfilled full-time talent development and OD leadership roles at MIT, Charles River Laboratories, Thermo Fisher Scientific, Wyeth (Pfizer), Boston Edison (Eversource), Private Healthcare Systems, Fleet Boston (Bank of America), Beth Israel Deaconess Medical Center, and Brigham and Women’s Hospital.His clients have included MIT, Boston University, Clark University, California Community Colleges, Hult International Business School, Harvard Graduate School of Education, Harvard Medical School Primary Care Institute, Tufts University, Robert Paul Properties, Beth Israel Deaconess Medical Center, Charles River Analytics, Havas Media, Axis Communication, and the Association for Talent Development.Hosmer earned his master’s degree in education from Harvard Graduate School of Education, bachelor of arts in psychology from Boston University, and personal and professional coaching-specialized graduate studies from Cambridge College. He is author of numerous articles and book contributions. Hosmer has also served as keynote speaker and guest panelist for several professional and career conferences and webinars.

David Hosmer

Get the A to Z Resource on Training Evaluation Fully revised and updated, ATD’s Handbook for Measuring and Evaluating Training is the comprehensive go-to reference for talent development professionals in need of immediate measurement and evaluation (M&E) guidance. Edited by M&E powerhouse Patti Phillips, with contributions from 30 expert practitioners, this handbook provides an essential roadmap to developing effective processes to prove the value and impact of your learning and development programs.Training measurement and evaluation is one of the top frustrations of a talent development professional's job. At first glance, it appears to be a drain on precious time and resources, yet it is a critical function for understanding the results and proving the value of L&D programs. If not conducted properly, it can yield questionable results, lack of accountability, and stakeholder skepticism. All to say, TD professionals must have a solid foundation in measurement and evaluation to do their jobs effectively.With this handbook, you’ll feel confident each time you face an M&E challenge, from planning the evaluation and collecting and analyzing data to optimizing results and making evaluation efforts work in your organization. You will use the data to improve your training programs and meet the standards your organization strives for.The updated edition includes new chapters on how to tell the evaluation story to business leaders, visualize data in reports, and leverage artificial intelligence smartly as well as refreshed chapters on all the M&E fundamentals. Rich in real-life application, it offers practitioner tips, knowledge checks, and support resources and references. Your advisors along the way are an impressive array of experts from the field, each chosen for their knowledge, experience, and actual results in specific areas.Whether you’re a trainer, manager, professor, or student of training evaluation, this handbook has been designed to meet your needs. 

ATD’s Handbook for Measuring and Evaluating Training, 2nd Edition

In January, OpenAI’s conversational platform, ChatGPT-3, became the fastest-growing app in internet history. This is just one indication of growing curiosity about GPT (generative pre-trained transformer) and other chatbots in the past few weeks. As one of the first learning professionals to see the potential of conversational AI and one of the few designing the applications for years, I’m excited to see the recent attention paid to this engaging technology.In my book, AI in Talent Development, I outline several ways chatbots can be used as part of a robust learning experience, and ChatGPT-3, the third version in OpenAI’s ChatGPT series, demonstrates most of them. When ATD asked me to write a post on this subject, I struggled to find the value I could add to the exploding conversation. I decided to do what people say I do best: Give you the bottom line and point the way towards practical applications. Let’s start with the basics.How ChatGPT-3 WorksChatGPT operates on an autoregressive model. It’s not as complicated as it may sound. A transformer is a program, model, or algorithm that converts code into something else—in this case, human language. The autoregressive part means the software uses prior events to predict the most likely future events, which is what your brain does all the time. It allows ChatGPT and similar applications to simulate a conversation between two humans. Learn the two keys to making an application like this work:1. Natural Language ProgrammingNatural Language Processing (NLP) allows the bot to recognize input through human speech or writing, eliminating the need for menus or code to engage with it. This same ability enables the bot to reply through text. Before the development of NLP, most chatbots were menu-driven, and the bot responded by selecting the best fit from a database of pre-written options. Conversational AI represents a new way for humans to use computers; it’s a user interface requiring less hardware and prior knowledge than any previous UIs.!CAUTION! Don’t confuse Natural Language Processing with the pseudo-science term using the same acronym. Neural Linguistic Programming is widely disproved and an invalid practice.2. Massive Training DatasetsLike any neural network (including your own), AI models must be effectively trained using a technique called deep learning. Quite frankly, the stunning capabilities of ChatGPT-3 are nothing new. As David Chalmers points out, there is no new technology here. There are many bots with similar abilities, and I described them in detail in 2020. How this bot is trained and how well this approach works is innovative. First, Open AI used the largest training dataset, combining a database containing much of the internet, a huge library of books, and Wikipedia.Then, after refining the bot in-house, Open AI released it free to the world. As we all started to play with it, we were training it further for them. Think of it as a crowd-sourced training effort. The sheer size of the training database resulted in what everyone always expected: Impressive results that can mimic human expression so well it is hard to tell a machine generated it. However, there continues to be an underlying problem: Chatbots are only as good as their training (like us humans). If you consider the source of GPT’s training, you can see why it sometimes makes mistakes.What Can We Do With ChatGPT-3?Because this product is still being explored, there may be new use cases by the time this post publishes. Below are a few applications that can save time and make your work as a learning professional more efficient and productive.Write ContentIf you ask the bot to write a paragraph introducing a subject, it comes back almost instantly with plausible, grammatically correct text. Because its training included unvalidated sources like the internet and Wikipedia, some of the content it produces must be corrected. Still, you can get excellent first drafts and refine your content from there.Summarize DocumentsLooking for a “Reader’s Digest” version of a book or article? Paste the text into a prompt to ChatGPT, and it returns a nice summary that should hit most of the highlights.Write and Troubleshoot Computer CodeBecause ChatGPT was built to analyze and produce output in human language, it is also great at computer languages. Programmers are using it to troubleshoot code. It can even generate new code if you ask it to develop a new capability.Translate DocumentsFar more effective than other translators out there, ChatGPT can take a website or article and translate it into another language.Write and Grade QuizzesYou can feed your content into GPT and ask it to generate a quiz with answers. Or, give it a general subject, like world history, and it will do the same. Feed your student’s quiz responses into GPT, and it will grade them (even essays!).Create GamesI asked GPT to create a game teaching sixth-grade students about the solar system, and the bot came back with a fun adventure through space.Improve Your WritingI didn’t do this for this article, but I could have asked GPT to review my draft and suggest improvements.Conduct ResearchI asked the bot to provide a list of current peer-reviewed studies on chatbot use in education using APA style and received a properly formatted list.Why Does ChatGPT Have Some People Worried?While most of the comments about GPT are positive, you will find a few educators deeply concerned about this new tool’s capabilities.Mimicking Human WritingTeachers are worried about GPT’s ability to write plausible text reading like a human created it and write in a known author’s style. A student could ask the bot to write a paper in the same style as one of their previous works, and it would be hard to tell the GPT paper wasn’t an original student effort. But, again, this mimicking ability is nothing new. AI has generated art in the style of great artists for years. ChatGPT applies the same predictive approach to language.ChatGPT Makes MistakesAnother concern is the bot’s ability to present false information so convincingly that some users forget to validate the response with research. For example, I asked the bot to calculate the mean of five numbers, and the result was interesting. I received a detailed explanation of the statistical definition of “mean” and how to calculate it, which I did NOT request. However, the bot added the five numbers incorrectly.To be fair, ChatGPT wasn’t built to perform math problems. But it didn’t tell me that or report the result might be incorrect. Like any willing assistant, it did its best to provide an answer. If I had been a student relying on GPT’s help, I would have been wrong. The reason for most of these mistakes is the same reason that it is such a stunning improvement in the chatbot experience: It’s primary sources of information are the internet and Wikipedia.I asked Vince Han, one of the pioneers in building chatbots for learning, to sum up the challenges presented by GPT’s new capabilities, and here’s his response:“One of the effects of ChatGPT & increased quality in Artificial Intelligence in general is shifting the skills users need. For example, most users have developed a skill for how to search which is knowing how to craft keywords and how to sift through search results. Now, people will need the skill of discernment, or learning how to decipher truth and views from a chatbot that provides what reads like a definitive answer. So, from a performance support perspective, yes, one can now get an immediate response to a question but how accurate and relevant that response is will be up to them to determine. “As educators, we must now confront a problem that has been boiling for decades—the decline in critical thinking across most domains, demographics, and workgroups. In my opinion, ChatGPT makes this gap more visible and increases the urgency for closing it quickly.Closing ThoughtsBecause Microsoft owns the license for the exclusive use of the model, we’ve all been working for them, revealing exciting use cases that may find their way into AI-enabled Microsoft products. For now, the platform consists of both free and professional versions. It’s an excellent way to begin learning about the promise of AI and our place alongside it. As machine learning expert Santiago Pino says, “AI will not replace you. A person using AI will.”Where Can You Learn More?There are so many articles about this subject right now that I’ve limited myself to a few favorites:Under the Hood of Chat GPT With Myra RoldanHow ChatGPT3 Impacts the Future of L&D in an AI WorldChatGPT Has Educators Scrambling to Keep UpWhat Is ChatGPT and How to Teach With ItWhat Is ChatGPT and Why Does It Matter?What Is ChatGPT? Is It the Beginning of the End?Ger’s Learning Notes #51 - ChatGPTFor more information and resources, Check out ATD's AI Resource page.

ChatGPT-3 Has Everyone Talking

For many people in our profession, it is a struggle to create a measurement and reporting strategy. It doesn’t have to be hard. Join this webinar to learn a simple approach to get you started. Dave Vance and Peggy Parskey will employ concepts from their 2021 book Measurement Demystified: Creating Your L&D Measurement, Analytics, and Reporting Strategy and from their just-released 2022 book Measurement Demystified Field Guide. They will share a step-by-step process which employs an easy-to-use framework for classifying, selecting, and reporting the most appropriate measures to meet your needs.
During this webinar, you will learn:

A step-by-step approach to creating a measurement strategy
The four most important reasons to measure
The importance of a balanced set of measures
How to report the measures


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