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Justin Mass

December 2014 - TD Magazine

Going Digital: Re-Imagining New-Hire Orientation

We all walk around the world with certain expectations. We have a sense of how the food will taste at a fast food restaurant. We know what our favorite slippers feel like on a cold morning. We brace ourselves when we get a letter from our accountant. When our expectations aren’t met, we struggle. We complain that the line at our go-to coffee shop is longer than normal. We can’t believe that a particular clothing store no longer sells red T-shirts.We also struggle when something or someone exceeds our expectations because we don’t know how to react. The entertainment industry plays on this mismatch of expectations and reality. But often, we celebrate when faced with such unexpected scenarios. Julia Roberts can afford to shop in the pricy boutique in Pretty Woman. Frodo is much more than a just a hobbit in Lord of the Rings and defeats the greatest evil in the land_._We carry around the same expectations at work. Learning and knowledge teams often begin projects with deeply held beliefs about the other. Prior interactions between the two teams (like the mass of ERP projects in the 1990s and 2000s) have built and reinforced such preconceptions. Yet in reality, both teams exceed these expectations.Learning teams see knowledge managers as organizers of information, building categories and subcategories to put knowledge into well-identified bins. Knowledge managers historically controlled the content necessary for the learning team to develop training, managed access to that knowledge, and ensured that all of the knowledge was formatted properly. The learning team saw the knowledge managers as keepers of policies, processes, and procedures for knowledge. The demands of these procedures frustrated learning teams, creating an impression that knowledge managers were more worried about the format and categorization of knowledge than whether it fit a particular need. Knowledge managers were perceived as gatekeepers, not enablers. Learning teams felt compelled to create supplementary content to fill in what they perceived to be gaps in the “fit” of the knowledge.Knowledge teams, on the other hand, saw learning managers as trainers only, adapting content that they had created. Learning teams might change the order of the presentation of knowledge to make it easier to consume, which would affect the context of the knowledge presented. Knowledge teams also felt an intense pressure to deliver “complete” knowledge to the learning team. This was particularly difficult in complex projects where changes to the deliverables would alter the knowledge required to build training, creating numerous back-and-forth interactions between knowledge and learning teams, and frustrating both. Knowledge teams also were frustrated by the amount of time that the learning team required to build training.Obviously, neither of these sets of expectations is true. Today, both learning and knowledge teams are driving toward the same goal—delivering the knowledge and skills that team-members need as close to when they need them as possible. Just-in-time knowledge and learning delivery systems mirror one another in intent.Knowledge and learning teams need to recognize their limiting expectations and learn to exceed them. Can you imagine the applause you can get if the teams move past their expectations?Would you like to know how to build this partnership and achieve exceptional results? Check out our March issue of TD at Work: “Enabling Success through Learning and Knowledge Sharing.”

Great Expectations: Move Past Preconceptions and Forge a Healthy Partnership

Imagine a world where your next job interview is conducted by artificial intelligence (AI) that analyzes your every blink, twitch, and vocal inflection. Now picture that same AI deciding whether you’re fit for a promotion based on your learning style. Does this sound like science fiction? Not quite. Welcome to the brave new world of AI in the training industry, where the line between innovation and ethical quagmire is as thin as a microchip.As AI seeps into every corner of our lives, from weather apps to our social media feeds, one industry stands at a critical crossroads: learning and development. Here, the promise of personalized learning experiences collides head-on with concerns about privacy, bias, and the very nature of human learning. Are we on the brink of an educational revolution, or are we unknowingly coding the biases of today into the learners of tomorrow?AI systems are only as unbiased as the data they’re trained on and the humans who design them. In 2019, HSBC teamed up with Talespin to create a VR program for soft skills training, but it hit some bumps when it rolled out globally. The AI, primarily trained on Western expression datasets, consistently misinterpreted common nonverbal cues:In Hong Kong, the AI got confused by subtle Chinese communication styles. When a Chinese employee said, “We might want to consider another approach,” the AI read it as uncertainty when it was a polite way of disagreeing.In the Middle East, the AI didn’t recognize the importance of the right hand in greetings and gestures. Using the left hand, which is considered impolite in many Middle Eastern cultures, wasn’t flagged as a faux pas.In the UK, “That’s not bad,” means it is quite good. However, the AI interpreted it as lukewarm approval rather than positive feedback. The VR scores didn’t match up with real-world performance, and HSBC didn’t just shrug it off. They brought in cultural experts and threw in some extra training on cross-cultural communication. They also made sure humans were keeping an eye on things, just in case the AI missed some cultural nuances.HSBC’s story shows how tricky it can be to use AI for soft skills training across different cultures. But it also proves that with some tweaks and a willingness to learn, you can turn those challenges into some valuable insights for global business.Privacy is another major concern.In the training industry, AI systems often collect vast amounts of data on learners’ behavior, preferences, and performance. While this data can be used to improve learning outcomes, it also poses significant privacy risks if not handled properly.To address concerns:Strict data protection measures must be put in place to safeguard learners’ privacy. Organizations should give learners control over their data, including the right to access, correct, and delete their information.Regular audits to ensure compliance.Next, is the issue of transparency. Many AI algorithms, particularly those using deep learning, make it difficult to understand how they arrive at their decisions. If an AI system recommends a certain learning path or makes an assessment of a student’s abilities, both educators and learners should be able to understand the reasoning behind these decisions.To improve transparency:AI developers should prioritize creating interpretable models that can provide clear explanations for their decisions.Organizations using AI in training should provide clear documentation on how their AI systems work and make decisions.To further improve AI in training, greater diversity must be ensured in AI development teams for a wider range of perspectives, potentially reducing bias in AI systems. Rigorous testing for bias should be conducted before they’re deployed in educational settings, and continuously monitored. By addressing these concerns, we can harness AI’s power to improve training outcomes while protecting learners’ rights and ensuring fairness. As educators, technologists, and lifelong learners, we have a collective responsibility to shape an AI-driven future where technology augments human intelligence.Ultimately, this journey isn’t just about smarter machines, but about nurturing smarter, more capable humans. Let’s embrace this challenge with open minds and an unwavering commitment to ethical progress.

Justin Mass

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