AI at Work: Stress, Help, or Both?

August 25, 2025November 19, 2025 ~ Manuel Castañón-Puga

You land your first job and your manager asks you to try a new AI tool. Part of you is excited; part of you worries about mistakes, pressure, or privacy. Ahumada-Tello and colleagues looked at these everyday doubts using worker data from OECD countries to see how AI actually relates to happiness at work and to the choices people make on the job. They used survey data and factor analysis to study the links between AI use, decision-making, and well-being, finding patterns across hundreds of workers.

According to the authors, the big picture is encouraging: AI, on average, does not lower happiness. It tends to support better and faster decisions, which can feel good when your to-do list is long. In the survey, most people agreed AI improves decisions and speeds them up, showing how tools can cut busywork so you focus on real problems. Picture an intern using AI to group customer messages so the team answers faster and with fewer errors—that is the kind of help many respondents reported. The study also organized AI at work into five practical areas: worries about AI, help with decisions, general use, managing AI teams, and working directly with AI systems.

The authors dig into a surprising point: worries about AI—like job security, data, or losing control—did not automatically make people less happy. In fact, for some, noticing the risks pushed them to adapt, learn new skills, and feel more in control again. At the same time, the study highlights limits and conditions. Trust and transparency matter when algorithms are involved, and some jobs still need human empathy and flexibility that tools cannot replace. Imagine a chatbot helping with routine questions while a person handles sensitive cases; that mix fits what the researchers describe.

So what should a young worker take from this? In this study, using AI, getting help from it in decisions, and integrating it into daily tasks were all linked with higher happiness at work. The drivers behind that lift are very down-to-earth: adaptability, efficiency, a sense of mastery, leadership opportunities, and clarity about what to do next. In simple terms, learn the tools, keep your human skills sharp, and ask for clear explanations when AI is used. That way you get the speed boost without losing yourself in the process.

Referencia:
Ahumada-Tello, E., Evans, R. D., Romero-Gómez, D., López-García, J., & Castañón-Puga, M. (2023). The impact of AI on the workplace: OECD AI surveys of employers and workers. 2023 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT), 121–126. https://doi.org/doi:10.1109/GCAIoT61060.2023.10385121.

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Smarter Choices, Better Ideas: How to Make Decisions and Innovate Like a Pro

May 26, 2025November 19, 2025 ~ Manuel Castañón-Puga

Big choices don’t just happen; they follow a simple path. First, notice that a decision needs to be made. Then gather solid info from inside and outside your world. List your options, weigh the evidence, pick one, act, and later assess the outcome. That’s the whole loop, and doing it on purpose makes success more likely. Ahumada-Tello et al. explain this step-by-step process and stress the importance of using reliable data at each stage to avoid guesswork. Today, there’s a flood of data to help you: from basic spreadsheets to tools like data mining and business intelligence. Used well, these don’t replace your judgment—they sharpen it.

Innovation isn’t only about inventing the next smartphone. It can be a new product you can touch, a new service you experience, or a better way to do the work behind the scenes. Consider a supermarket that introduces home delivery (service), a store transitioning from paper forms to digital dashboards (process), or phones that continually add features year after year (product). Changes can be small or huge. Rebranding a snack is incremental. Jumping from older mobile networks to 3G or 4G was revolutionary because it reshaped what phones could do and how the entire industry operated. Sometimes the change is to a single part (modular), like swapping a camera lens. At other times, the entire setup is re-wired (architecturally), such as the transition from film to digital cameras, which altered how every piece fits together.

So what matters most for a team’s results? In a study of tech firms in Tijuana, researchers found that clear, professional decision-making had the strongest link to better organizational performance, even more than new-product work or innovation programs. In their model: Performance = −4.876 + 0.152×New Product Development + 0.102×Innovation Management + 0.403×Decision-Making Process. That big 0.403 shows decision-making packs the biggest punch. The takeaway is simple: learn to structure choices, manage information well, and you’ll boost your outcomes—even before you launch the next big feature.

How do you put this into practice day-to-day? Start small. When you’re picking a class, a side hustle, or a project idea, run the mini-cycle: define the problem, collect relevant info, list options, rate the pros and cons, decide, act, and review what you learned. Use easy data sources—such as surveys, quick tests, or simple dashboards—to keep emotions from steering the ship. Then look for innovation sweeps you can actually do: a smoother process for your study group, a fresh service twist for your freelance gig, or a tiny product upgrade that delights people. Small, steady improvements build momentum, and when a radical opportunity appears, you’ll be ready to make a confident call backed by a clear process.

Reference:
Ahumada-Tello, E., Castañón-Puga, M., Gaxiola-Pacheco, C., & Evans, R. D. (2019). Applied decision making in design innovation management. In Studies in Systems, Decision and Control (Vol. 209). https://doi.org/10.1007/978-3-030-17985-4_5

Why Your Group Chat Acts Like an Ecosystem (and What That Says About Real-World Organizations)

May 19, 2025November 19, 2025 ~ Manuel Castañón-Puga

Think of a complex adaptive system as many small players, all doing their thing at the same time, reacting to each other, and shaping the big picture without a single boss running the show. Ahumada-Tello and Castañón-Puga describe it as a decentralized network of “agents” (people, teams, apps, even companies) whose combined choices create the overall result. When these agents interact, local moves—such as a few friends changing plans—can ripple into larger patterns, like the overall vibe of a whole club or community. That’s why studying interaction (who talks to whom, and how often) often explains more than just listing everyone’s individual traits.

Four ideas make this easier to picture in daily life. First, there are many layers: your team sits inside a class, a school, a city, and so on; each level influences the others. Second, it’s nonlinear: small inputs can snowball—one rumor or meme can flip a whole plan. Third, connectivity matters: the links between people can be more important than any one person. And fourth, agents adapt: we sense what’s going on, choose, act, and adjust again, which lets the group self-organize without a central controller.

From these interactions, patterns “emerge.” No one plans every step, but simple local rules still add up to smooth outcomes—such as a supply chain functioning or a club event coming together. Good systems keep a balance: not frozen, not chaotic, but near the “edge of chaos,” where creativity and options are highest. They also benefit from variety (different viewpoints), simple rules that everyone understands, and numerous quick feedback loops where small tweaks can lead to significant improvements. Your world is full of nested systems, too—your part-time job is inside a local market that sits inside a national economy—so expecting change and adjusting fast is a winning habit.

What does this mean for organizations you’re part of—student groups, startups, workplaces? They work best when they avoid two traps: a rigid “perfect balance” that kills innovation, and total rule overload that tips into chaos. Aim for limited instability where people can experiment safely. Over-centralizing with too many procedures can suffocate initiative, so use a few clear rules, strong communication, and trust teams to adapt. As Ahumada-Tello and co-authors note, organizations thrive through emergence, self-organization, and evolution—so design your projects to learn quickly, share information widely, and let good ideas spread.

Reference:
Ahumada-Tello, E., & Castañón-Puga, M. (2019). Sistemas adaptativos complejos: un método de análisis organizacional. In I. Plascencia López & J. Ramos (Eds.), Teorías de la complejidad en las ciencias económico-administrativas: Una aproximación (1st ed.). Universidad Autónoma de Baja California.

Learning to Code, One Helpful Nudge at a Time

May 5, 2025November 19, 2025 ~ Manuel Castañón-Puga

If you’ve ever opened a coding tutorial and felt lost by line two, you’re not alone. Hurtado et al. describe a simple idea that helps: teaching beginners with a platform that guides you step by step, provides clear feedback, and recommends the next thing to learn based on your progress. Their tool, Protoboard, suggests learning materials by combining teacher input with intelligent rules about difficulty, and it adapts to each student rather than presenting the same content to everyone. Think of it like a friendly playlist for studying Java: it starts with easier “tracks,” then levels you up as you demonstrate your readiness. The system uses fuzzy-rule recommendations tied to beginner, medium, and advanced learning objects, along with basic metadata such as audience and format, to determine what you should see next.

When you open a unit, Protoboard prompts you to read the short lesson first and then try two types of practice: one where you fill in missing code and another where you start from a blank page. This order matters because it builds confidence before throwing you into the deep end. The app also checks for good habits—clear variable names, proper use of brackets, clean structure—and points out exactly what went wrong when you slip. That means your mistakes turn into quick lessons instead of long detours on Stack Overflow. In plain terms: you see what to fix, why it matters, and what “good” looks like.

Does this approach actually help? Hurtado et al. tested it with 112 students across two universities, focusing on classic control structures like if/else, switch, while, do-while, and for. After studying a topic, each student completed a pair of exercises (one “complete the code,” one “from scratch”). On average, students needed roughly one to three tries to get programs right—evidence that the feedback and structure were doing their job. The trickiest bits were usually the if/else cases, which makes sense for beginners; still, most learners landed the solution in just a few attempts.

Why should you care if you’re just starting out? This study suggests a smoother and less frustrating way to learn. A tool that nudges you to read first, practice right after, and adopt clean habits can save you time and make your code easier to grow later. Teachers benefit too—they can see how many attempts a task takes and adjust lessons or add new examples where people stumble. For you, that means clearer instructions, more tailored practice, and faster progress. If you’re curious about coding, look for resources that copy these ideas: short lessons, immediate practice, precise feedback, and gradual difficulty. Small wins stack up, and with the right nudges, you’ll go from “What is this bracket doing?” to “I’ve got this” much faster than you think.

Reference:
Hurtado, C., Licea, G., García-Valdez, M., Quezada, A., & Castañón-Puga, M. (2020). Teaching computer programming as well-defined domain for beginners with protoboard. Advances in Intelligent Systems and Computing, 1160 AISC, 262–271. https://doi.org/10.1007/978-3-030-45691-7_25

How Tech Can Read the Room—and Make Your Experience Better

April 21, 2025November 19, 2025 ~ Manuel Castañón-Puga

Ever felt like a game, app, or museum exhibit “just gets you” and reacts at the right moment? Rosales et al. explain a simple idea behind that feeling: measure how you’re interacting, then use that to adapt what you see. They lean on a classic set of eight clues about your behavior—presence, interactivity, control, feedback, creativity, productivity, communication, and adaptation—to describe your “level of interaction.” Think of them as vibes the system watches for: Are you engaged? Are you trying new things? Are you getting responses when you press buttons? These signals help the system learn what to show next, so you don’t get bored or lost.

To test this in real life, the team visited an interactive science museum in Tijuana, where people—especially children and teenagers—play to learn. They tracked everyday details, such as how long someone stayed, where they moved, whether they read information labels, and if they returned to the same spot. That may sound small, but together, those bits tell a story about attention and curiosity, helping designers make labels clearer, stations easier to use, and activities more enjoyable. Imagine a driving or flight station that notices you’re stuck and gives a quick tip, or speeds things up when you’re clearly nailing it—that’s the goal.

Under the hood, Rosales et al. use a fuzzy logic system—don’t worry, it’s just math that handles “in-between” values instead of only yes/no. Each of the eight clues gets a score between 0 and 1, and the system groups those scores into levels from “very bad” up to “excellent.” Then it determines your overall interaction level, ranging from 0 to 5, much like a skill tier in a game. If your level is near the next tier, it nudges you upward and updates its knowledge of you for the next step. In plain terms, the exhibit watches what you do, estimates your current mood, and adapts so you can keep learning without zoning out.

Does it work? They tried it with data from 500 visitors. The team split the group in half—one half to set up the tool and the other half to test it—and compared the system’s calls with human judgments. The results were close most of the time, with about 76% accuracy, which is decent for a first pass. For everyday life, that means smarter exhibits, apps, and games that can sense when to give you hints, when to challenge you, and when to switch things up. It’s the same idea you can use yourself: notice your own signals—am I engaged, getting feedback, learning something new?—and tweak your setup, whether that’s changing a study app’s difficulty, turning on captions, or picking a different mode in a game. Small cues add up to a better experience.

Suggested by Gayesky and Williams’ level idea and brought to life by Rosales et al., this approach is about meeting you where you are and moving with you. The more systems pay attention to those eight everyday clues—and the more they adjust in the moment—the more tech feels like a helpful guide, rather than a hurdle. Next time a tool feels smooth and responsive, there’s a good chance it’s quietly reading the room and adapting to keep you in the zone.

Reference:
Rosales, R., Ramírez-Ramírez, M., Osuna-Millán, N., Castañón-Puga, M., Flores-Parra, J. M., & Quezada, M. (2019). A fuzzy inference system as a tool to measure levels of interaction. In Advances in Intelligent Systems and Computing (Vol. 931). https://doi.org/10.1007/978-3-030-16184-2_52