Mobile learning: the solution for revolutionizing the learning landscape

Context is everything. When you’re swimming in the ocean and see a fin sticking out of the water, your brain concludes: "It's a shark, get out of the water!" But if you're in a pool, you think: "It's a kid with a swim toy that looks like a shark fin." In both situations, the context leads you to reach two very different conclusions and behavioral responses.

How people behave in any given situation is a function of both who they are as individuals (e.g., their personality, skills, past experiences) and the context in which the behavior takes place (e.g., the situation itself). In other words, context matters, and it is difficult to interpret an individual’s behavior without an understanding of the context they faced.

When it comes to using assessments during the hiring process, organizations have a vested interest in making certain that these assessments reflect the organization and job – the context. Doing so helps jumpstart onboarding by ensuring that candidates' assumptions about the organization, the job, and their suitability for both – that they invariably make during the recruiting process – are rooted in reality.

But assessments modeled after the organization and job are superior for another reason: They are generally stronger than generic assessments that cut across job type, level, organization, industry, etc.

1. More predictive. First and foremost, the closer the alignment between the assessment and the specific context in which the individual will ultimately perform (i.e., the job at the organization), the better the assessment will do in predicting future job performance. In fact, research demonstrates that highly contextualized assessments have incremental predictive validity beyond situational judgment and job knowledge assessments. This means that even after measuring candidates' job-relevant knowledge and how they would handle particular situations, highly contextualized assessments still reveal candidates' ability to perform the job that we don’t otherwise know from these other tools.

Why is this true? Because the best predictor of future behavior is past performance. For many years, this adage has been dubbed "the Golden Rule of selection." Think about it: What's the best way to predict whether an individual will be a good salesperson at your organization in the future? Answer: Observe them in the job of salesperson at your organization. The only problem in the pre-employment context, however, is that you cannot observe a candidate perform a job they do not have… Or can you?

Assessments designed to reflect the realities of an organization and job often take the form of a simulation – sometimes completely automated; other times involving role plays conducted by trained assessors. In essence, these assessments let candidates "try the job on for size" – explore the situations and challenges faced, engage in dealing with the situations, etc. Such work samples provide the opportunity to, in essence, perform a job that candidates do not yet have, thus enabling conclusions about how they would perform the job if hired.

2. Less adverse impact. Not only are highly contextualized assessments, such as simulations, highly predictive of future job success, but they also have lower risk of adverse impact. In fact, a seminal meta-analytic research study – looking across many years of other research studies – found that simulations comprising role-plays or presentations have about 50 percent less risk of adverse impact (i.e., sub-group differences) compared to other assessment tools. This decreased risk of adverse impact translates into a more diverse group of candidates deemed qualified for the job, ultimately leading to a more diverse workforce.
3. Higher face validity. Finally, because highly contextualized assessments look like the job, candidates see the relevance of these assessments for the job to which they've applied. Candidates understand why you are asking them to perform some task or answer particular questions because the assessments make sense in their minds given what they know about the job. This is known as face validity, which highly benefits the organization. This underlying concept can decrease the risk of candidates challenging the results of an assessment, improve perceptions and impressions of the employing organization, and increase job offers acceptance rates.

All three areas of highly contextualized assessments are paramount on their own, and together highlight the importance of tailoring pre-employment assessments to the organization and job. They serve the dual purpose of teaching candidates about the job, while also assessing their capabilities and alignment with the organization's needs.

The employment decision is important for both the candidate and the employer, and it benefits both parties to ensure that candidates are assessed in an accurate and authentic manner to make the best, most informed decisions possible.

How do you more effectively drive learner engagement and concept retention in your digital learning courses? Sound is key. Why? Sound elicits emotional responses which make moments more memorable, and also shapes your unique sense of space, time, and reality. Adding a layer of sound would amplify presentations of any kind.There are three elements to consider when incorporating sound into digital learning courses: sound effects, voice talent, and transitions.

1. Engaging sound effectsPractitioners of mindfulness meditation focus on the present moment in order to reach a particular mental state. The present moment is always changing, of course, depending on the surrounding constellation of smells, sounds, sights, and feelings. Try to isolate the ambient sounds in the air, and you will come to appreciate both the limits of your focus and the omnipresence of sound. Note, too, how memories involving both sight and sound are more vivid than those derived from just one sensory channel.Sound effects can perform numerous functions: emphasize a particular point, underscore a key concept, balance serious content with humor, and more. Try, for instance, to imagine films such as Star Wars, Titanic, or The Godfather without their orchestral scores and soundtracks. Just as sound effects are critical to each story on an emotional level, they can make the digital learning experience more meaningful and memorable.Consider using ambient sounds that are colloquial to your learners, such as the "ding" signaling the arrival of a new text message, or the "whoosh" of sending an email. Using these sounds tactfully throughout your presentations will increase learner engagement by initiating states of excitement, focus, or reflection. However, be sparing in your use of such effects, as space is essential for the appreciation of sonic subtleties.
2. Appropriate voice talentYour choice of voice talent is critical – any recognizably-human voices invoked for digital-learning purposes must align with your organization’s cultural DNA and corporate identity.For the digital learning course in question, be sure to consider its subject matter, audience, and tone. After identifying each, decide whether a stodgy accent, homey lilt, or something else entirely would facilitate the most engaging learning experience. Once you have a few options, be sure to send samples to your client for feedback and approval. Reactions to voices, after all, are highly personal.
3. TransitionsTry now to pinpoint all the recurring noises around you, whether infrequent or ever-present. Depending on where you are, you may hear the low hum of a fridge, the persistent honk of a horn, or even the steady beating of your heart. These periodic sounds, absorbed subconsciously, signify the passage of time and transformation of your surroundings.Transition sounds, like the famous musical motifs in each aforementioned film, aid in memory retention by stimulating an emotion, foreshadowing an event, or accentuating a concept. In digital learning courses, transitions work best at the beginning and end of a module. That said, if placed tastefully throughout a course, transition sounds will add polish, build a brand, and encode content in a memorable way.

These are just a few of the ways that sound can be used in your digital learning courses as a design pillar, making training more engaging and enjoyable. Whether you meditate or passively absorb your surroundings, sound adds texture, depth, and meaning to the fabric of our lives. How will you use sound to build your next creation?

E-learning designers are still catching up to what brand differentiation experts have known for a long time. Experience matters.

Consider Bubly, a maker of sparkling water, recently purchased by PepsiCo. Bubly doesn’t try to differentiate at the product level: in a blind taste test between Bubly and LaCroix, participants were unable to tell one from the other. Instead, Bubly focuses on the consumer’s experience of the product.

To begin, there’s the enthusiastic welcome: each can features a pull-tab greeting that mimics text messages – “hey u,” “hiii,” or “yo,” – simulating the kind of playful rapport you might have with friends and family. Next, the product’s peach, pineapple, and grapefruit-toned cans and smiling logo work together to convey positivity, creating a look and feel that aligns seamlessly with its slogan: “no calories. no sweeteners. all smiles.” Finally, Bubly gamifies buying. As writer Elizabeth Demolat points out, no store stocks all twelve flavors at any one time, leading to online and in-person buzz about where to find specific flavors. This strategy, along with the release of a variety of limited-edition flavors, has essentially turned “the act of purchasing a product into a treasure hunt.”

Bubly’s brand differentiation leverages enthusiasm, emotion, and excitement—experiential elements that echo the design pillars of best-in-class e-learning. Here’s how to incorporate each.

1. Enthusiasm

Find new ways to breath energy into the experience. Take, for example, a short, animated video that uses action film motifs to explore emotional awareness in the workplace.

The sequence begins with an establishing shot of a manager providing constructive feedback to an employee. The action moves quickly into the employee’s brain, which is set up as a command center. A group of intelligence agents, straight out of Mission Impossible, look on with alarm. One more word from the manager on “areas for improvement,” and the emotion-regulating amygdala will be triggered, hijacking the employee’s normal reasoning processes. The intelligence agents strategize, introducing different tools and techniques that can be used to regain perspective, and the learning journey begins to take shape.

Greeted with a fresh, playful take on a critical workplace competency, learners are primed to go deeper.

2. Emotion

How do you get beyond the rational regions of your brain – the ones that “control language, but not decision-making” – to tap into feelings and emotions? One particularly creative course on human anatomy leverages powerful visuals to reach learners on that deeper level.

Participants begin by learning that there are more nerve cells in the brain than stars in the Milky Way, observing a close-up of the brain’s circuitry dissolving into tiny specks lighting up the night sky. Because the underlying anatomy remains hidden, medical-aesthetics practitioners learn that they will essentially be working in the dark. The stars fade out slowly, one by one, until there’s nothing left on screen but total darkness—a strange, slightly unnerving experience that drives home the importance of understanding anatomical structures on a visceral level.

3. Excitement

Give people something they’ve never experienced before by challenging the norms of typical training.

Data-protection policies, for instance, are critical safeguards wherever they’re in place, but existing e-learning on the subject is almost always designed as a passive, one-way transmission of information. One exceptional data-protection course takes a different approach, using live-action video and a dramatic soundtrack to depict a privacy breach occurring in real time.

While this can get gimmicky, immersing learners in a volatile environment with uncertain outcomes builds tension, a key lever for creating buy-in.

‍So, how can we help clients build better learning experiences?

‍Many clients see digital learning as a product, one that looks a lot like what’s already out there: didactic, uninspired, dull. By nudging clients toward digital learning courses that mirror what they already know about branding, we might just be able to help them build experiences that stand out in a crowd.

There’s a specific kind of strategy meeting getting scheduled right now, in nice hotels with bad coffee: the AI reset off-site.

And for good reason. In a 2026 WRITER survey, 48% of leaders described their AI rollout as, in their own words, a "massive disappointment." That's nearly half the room.

What that number really measures is the distance between what these tools can do and what people are doing with them. In our experience, that distance is almost entirely human.

Which is why the off-site is the right instinct. Making the most of that time is the harder part.

What separates an AI reset that actually changes the game from an expensive two-day conversation? In our experience, it comes down to avoiding four common design mistakes.

Mistake 1

Blaming the bots

The gap between AI investment and real adoption is almost always about people, not technology. And when adoption stalls, we usually find it's one of four things.

1. They don't think it will help them
2. Nobody around them is using it
3. They don't feel capable
4. Or they don't have real access to the tools they were promised

Four different problems, and four completely different fixes.

That's why diagnosis comes first. If you don't know which barrier you're dealing with, every intervention becomes an educated guess. And you cannot tell which one you have by staring at a dashboard. A belief gap and a skill gap look identical in a status report and need opposite interventions. Show up guessing, and you'll spend real money teaching people to use a tool they simply don't trust yet. Congratulations - you've just catered the wrong conversation.

Mistake 2

Letting leaders off the hook

One of the biggest predictors of whether change sticks is also one of the most overlooked: leadership.

If your executives show up as observers, nodding along and quietly answering email under the table, your people clock it in about four minutes.

That doesn't mean your CEO has to emcee the thing. It means they use the tools in front of everyone, participate in the conversation, and make it clear this isn't someone else's initiative.

Recently we’ve been working with a Fortune 200 global professional services firm who’s top 120 leaders were at very different points with AI. Some were redesigning entire processes. Others were using it to summarize emails, or not at all. Rather than focus on the technology, the four-hour session focused on what leaders could do with AI, applying it to a live strategic challenge and ending with a personal commitment to lead differently. The response was strong enough that the organization is now cascading the experience globally.

The lesson is simple: when leaders experience AI as a strategic capability, they're better equipped to model the behavior that makes adoption stick. Nothing you build during those two days survives without that entire chain of leadership doing its part.

Mistake 3

Chasing the wrong outcome

Without a behavioral baseline, you have no way to prove anything actually moved. No baseline, no ROI. You're just hoping the energy in the room was good, which is a wonderful feeling and a terrible metric to bring to your CFO.

But the baseline isn't just about proving the off-site worked. It's about understanding where you're starting in the first place. And you'll want that clarity, because the quiet resistance is real. In that same 2026 research, nearly a third of employees admitted to actively working around their company's AI strategy. If you don't win their belief in the room, some of them will keep politely ignoring the whole thing from their desks. You can't measure your way out of that. You have to earn your way out of it.

Which brings us to the biggest reframe of all.

Mistake 4

Leaving follow-through to chance

We've been working with a Fortune 100 medical device company on their AI strategy for three years. It started with their leadership team, a three-hour session built around what those leaders would do differently, and it landed. What became clear afterward was that the same experience needed to happen everywhere else. So, it expanded: 90-minute activations for 15,000 people, and this year intact teams redesigning their own workflows.

Three years in, that first session is the smallest part of the story.

Your event is where momentum gets created. What happens at 30, 60, and 90 days is where results get made.

If you're planning one of these and want to change what happens on Monday, not just how everyone feels on Friday, that the work we do.

‍We'd be glad to help you design it.

Candidates now arrive at interviews pre-coached by AI, with their resumes optimized to pass every checkpoint. Polish has stopped being a signal. The traditional hiring process was built to read exactly the cues that AI is now best at producing, and the signals hiring managers once relied on have weakened as a result. And for roles where the wrong hire carries real business consequences, losing the ability to tell who will actually perform is not a minor inconvenience. It is a material risk, and it exposes the business to unnecessary turnover, reduced performance, and heavier investment for talent growth and development.

So how do you observe the behaviors that matter most, before someone is in the role?

Not by asking better questions, but rather by putting candidates in situations designed to elicit that behavior.

The limits of predicting from paper

Credentials tell you what someone has done. Structured interviews tell you what someone says they would do. Neither lets you observe what they actually do in the moments that count.

This distinction matters most in client-facing, relationship-driven roles, where the performance gap between a strong hire and a weak one plays out in real business outcomes (revenue, retention, client growth). Organizations that hire at scale in these roles carry that gap across hundreds of decisions at a time.

The better approach is to watch candidates do the work before you hire them. Put them in simulated, role-relevant scenarios, and pair the simulation with a second, different kind of measure so no single method carries the whole decision. That combination is what lets you evaluate real performance before anyone is in the role. Organization-specific simulations provide a clear read on who is ready and capable of performing on day one. In a world of AI-supported candidate signals, the use of simulations makes the process harder to prep for. It is harder to fake. And, when designed well, it is substantially more predictive than other hiring methods.  

What counts as evidence

Claims about predictive power are easy to make. Evidence for them is rarer than you would expect.

A predictive validity study, the kind that links pre-hire assessment scores to how someone actually performs once hired, is some of the hardest evidence to produce and the rarest to see. Many assessments are validated against proxies: another test, or a theoretical model of the role, rather than real results on the job. Connecting scores to concrete business outcomes and doing the statistical work to show the link holds, takes years of shared data and a level of commitment from both the assessment provider and the client that most partnerships never reach. That is precisely why it is worth asking for. A provider who can show how assessment scores track to training completion, retention, and first-year output is offering something categorically different from one who can only show a correlation with another test.

Why simulation holds up where other methods do not

When a candidate sits across from a trained assessor (someone playing the client or prospect on the other side of the conversation) and has to work through a real situation, they cannot rely on a rehearsed answer. The scenario is specific. The stakes feel real. What you see is close to what you would get on the job.

That is the value of simulation-based assessment: it does not test what candidates know about the role.

It shows how they use what they know when a real person is on the other side of the conversation, before the stakes are real.

For roles that carry significant business responsibility, this distinction is the whole game. The cost of the wrong hire in a high-stakes client-facing role is not just a missed quota for a quarter - It plays out in relationships that do not develop, clients who leave, and productivity losses that compound over time. Getting those hiring decisions right, at scale, with consistency, requires methods that are built for predictive accuracy, not just candidate experience or hiring speed.

What this means for how organizations think about hiring

Most organizations are still optimizing the wrong things in their hiring process. They invest heavily in employer branding, application flow, and interview structure, all of which matter, but less in the core question: does our hiring process actually predict who will succeed in this role?

AI has sharpened the stakes here. If every candidate can present as polished and prepared, screening based on presentation becomes less useful. What holds up is direct observation of the behaviors that the job requires.

A few principles worth building from:

• Measure what the job requires, not what is easy to measure. Cognitive tests and personality questionnaires have their place, but they do not look much like the job. The closer the assessment is to the actual work, the better it predicts performance in it.

• Ask what your assessment predicts. Training completion? Retention? First-year output? Most organizations cannot answer that question today, largely because providers have rarely been asked to prove it. It is a fair thing to ask for.

• Take the human element seriously. In a simulation, a candidate is having a real conversation, responding in real time, navigating a situation that requires judgment. Even with the help of AI, that is hard to game. And it remains one of the strongest predictors of on-the-job performance available.

The data exists to make hiring decisions more accurate, fairer, and more directly tied to business outcomes. For organizations operating in high-stakes roles at scale, there is too much on the line to rely on methods that cannot hold up to that standard.

You may be interested in BTS’ thought leadership in the five talent shifts AI is forcing now.  

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La IA ya forma parte del día a día de las ventas. Hoy cualquier asesor puede llegar a una reunión con datos, tendencias e insights generados en segundos. Sin embargo, disponer de más información no garantiza conversaciones de mayor valor.

A través de una experiencia real con un consultor comercial, este artículo explica por qué la inteligencia artificial funciona como un GPS: ayuda a interpretar el entorno, pero no conduce la conversación ni entiende las prioridades del cliente.

En este artículo descubrirás:

• Por qué el acceso a la información ya no supone una ventaja competitiva.
• La importancia del business acumen para interpretar los datos con criterio.
• Cómo hablar el lenguaje del cliente genera credibilidad y diferenciación.
• Por qué las relaciones B2B evolucionan hacia relaciones P2P basadas en la confianza.
• Qué capacidades consultivas seguirán siendo exclusivamente humanas incluso en la era de la IA.

La tecnología seguirá evolucionando, pero la ventaja competitiva estará en quienes sean capaces de combinar inteligencia artificial con conversaciones centradas en el cliente, pensamiento estratégico y relaciones de largo plazo.