I came across an interesting technology called Mill, much like Lomi, which is an in-home composting device. Many people avoid composting because they either do not have access to a compost pile (1), they simply are not willing to put in the effort (2), or because compost can have an undesirable odor (3). I thought this device was interesting because not only does it alleviate these issues, but it also leverages a Gibsonian approach to habit forming that many products neglect. Mill works because it is situated where the act of composting is directly perceivable and actionable.

One of the major issues we face in habit forming, from a purely Gibsonian perspective, is that behavior is grounded in perception. Seeing an affordance, like a compost bin, provides the opportunity to act. Mill supports habit formation by making composting convenient, functional, and consistently available in perceptual space. In ecological UX terms, if we want recurrent behaviors, we also need recurrent structures embedded in the environment. Mill demonstrates how technology can shape habits not by persuading us through cognitive sorcery, but by embedding affordances where they are most likely to be used, normalizing automatic and pro-environmental practices.

This morning in my Business Course, we were discussing group and team dynamics from a management perspective. Interestingly, in our class discussion I realized that there is no formal strategy or standardized metrics/process for delegating responsibilities (to my knowledge). In my experience, the process goes something like, “can you handle x task” or a simpler “do x task.” I found it surprising because if we are aiming to maximize performance, with minimal negative trade-off, certainly the delegate’s ability-task fit should be optimized.

If I were to generate an initial hypothetical process for delegation, it would flow in this way: identify task characteristics, ask each delegate to describe the task, use raters to determine the degree of alignment between task characteristics and delegate descriptions, computer interrater reliability, then assign delegate(s) based on alignment. While we did not discuss this in my course, I would love to hear from readers how you might address this topic. This was a great example of how new minds, like students, entering a domain can reveal possibilities that are overshadowed by training, conventions, or tunnel-vision.

Recently I was asked by my institution to develop an article connecting my faith to my work. It is often diffucult to publicize these views, as religion can be a contentious topic. However, in reading the following, consider the role of faith claims that underly our professional practice, whether through a formal religion, or simply through personal morals and expeirences. For this assignment, I wrote the following...

Preston teaches humanity-centered courses discussing human behavior in the workplace. Professor Menke’s perspective on human behavior, especially in workplaces, can be described in three ways: learning-centered, ecologically-valid, and data-driven.

All humans work in a world designed to be worked (Genesis 1–2). They are bound (cf. Theories of Bounded Rationality) within environments that reveal His attributes clearly (Romans 1:20; cf. The Existence and Attributes of God). However human normative models, and thereby employee performance, are often reduced to a single metric or mental representation; this is a consequence of Enlightenment reductionism. In Preston’s approach, human work is guided by one’s local environment, providing opportunities to learn from the multidimensional coupling between oneself and the world – a relationship otherwise known as experience.

Therefore, there is an active relationship between a person’s actions and their perceivable space (Gibson, 1979). Preston is guided by these principles from personal experience, science (cf. A Meaning Approach to Cognition), and faith (John 13:17; James 1:22–24; Matthew 7:16–20; Hebrews 5:14). Furthermore, the active relationship is reflected by God: God’s words are not merely communication but impart knowledge, thus always leading to action. An application of this is found in Psalms, where neither song nor prayer is simply communication but is an active expression of reverence to Christ through praise.

Thus, Preston’s approach to faith and workplace behaviors emphasizes fitting human abilities to the environment — in other words, pursuing ecological validity. He seeks to recognize how personal abilities can be aligned with Christ’s calling, not merely through abstract theory, but through embodied processes (cf. Varela et al., 1991), that are ecologically grounded, experienced, and consistent with the lawful structure of God’s creation.

I had lots of education emphasizing the role of embodied actions for systems, but I was unsure where to apply those skills; I invested in IxDF to learn how real-world interaction design is driven by theory. IxDF is a learning platform targeting the broad scope of product design: user experience, user interfaces, accessibility, psychological theory, ergonomics, and especially human–computer interaction.

This platform engages learners across disciplines and across devices, educating learners in generalizable principles, device categories for UX (i.e., mobile, VR, etc.), and specific requirements for design (i.e., legal, accessibility, etc.).

I started on the platform last year (2024) and worked through all courses (39 at this time) and participated in 10 masterclass webinars. When I finished, I received an email that I ranked in the top 10% of learners. For me, this platform was about learning new perspectives and terminology used in practice. IxDF certainly boosted my ability to connect theoretical principles in design, across disciplines, to specific projects and domains. It also made me aware of new theory that I had not been exposed to, enhancing my educational background as well as preparing me for employment in design.

In hindsight, I was particularly impacted by the relevance of the content. The applied principles were connected to theory. Furthermore, the theory discussed on the platform often was coined by people in my domain of study, my colleagues, or colleagues of my colleagues. These connections made IxDF feel relatable and a natural fit for my design journey. I suspect that anyone who has gotten their feet wet in design, whether in theory or in practice, will find IxDF takes what you have learned and can expand on it.

Prior to the courses, I was worried there may be some challenge in taking my academic training and applying it to real-world jobs. Now that I have gone through the courses and interacted with instructors working on real-world problems, I am assured that my training in UX-adjacent fields will transfer well to a more formal UX setting.

For anyone interested, you can view my courses and webinars completed on LinkedIn.

As I have entered a professor role, I am often asked by faculty and students alike about what can permeate, if anything, the boundary between AI and the classroom. Through these questions, I have been exposed to the study of AI Governance. In these conversations, and sometimes presentations, I try to simplify the principles that differentiate AI from human intelligence; since human intelligence is what we are leveraging when leanring, and developing our ability to learn in the classroom. As a disclaimer, I have found several ways to mitigate learning barriers often arising when using AI, but I will detail below a general approach to why AI can hinder classroom learning.

AI is at its most over simplified form, a complex set of algorithms and data structures designed to synthesize and "recognize" patterns. It is regression statistics. Human intelligence is embodied: it is experienced through our senses, emotions, and interactions with the world around us. In other words, humans live in context, agents do not. This has major implications for how meaning is interpretted. For an AI, meaning is assumed based on what is conventional for that situation, based on statistical consistency in training data. This means that meaning is not interpretted in context, and therfore, is not replicating how humans learn. Because many of my students are student-athletes, I emphasize to them that learning is about identifying relavent and useful information in their environment. For those that appreciate sports analogies, the athletes learn by understanding the context: where they are on a field, where the ball is, and where hte opposing players are. They must make decisions about they next play, or next class assignment, and then reflect on the degree to which they were successful.

The reflection peice is not unique to humans. AI agents are trained on pre-existing data, just as humans identify errors in their past experience. However, humans are much better are addressing truly novel situations, and adapting to them. AI agents are limited by their training data, and the algorithms that define how they learn. In other words, if there is no data reflective of a problem During an AIs training, then it is unable to respond effectively. Humans on the other hand employ their perception to synthesize many context-sensitive constrains or characteristics. In summary, humans learn and solve through perciving the charactertistics of their environment, generating novel choices; AI simply rewrite or synthesize old informaiton that is often irrelevent to our dynamic, highly adaptive, always-generating-new-problems, world. While I do not prohibit all AI in my classroom, I encourage students to focus on to remember that they are paying to learn, which is to identify what is meaningful; they are cheating themselves by simply grasping at grade points through AI.

I found design by accident—time after time. In 2nd grade, my grandparents gave me an old Windows 98 computer. I loved exploring the settings and features it had, but the best part was a game called Contraptions. In this game, the player had to design systems out of basic tools and simple machines to achieve various tasks. I fell back on the concepts of this game for many years and in 9th grade I, seemingly randomly, was able to join a four-year pre-engineering program mostly out of curiosity (and because friends were signing up). It gave me a solid foundation in design and engineering, which fit naturally with my early fascination with computers since my grandparents gave me theirs in second grade. That momentum pushed me toward Information Technology and computer programming.

Serendipity struck again when my dad sent me a Coursera IT certificate. At the time I was exploring game design, in theory and practice, in Unreal Engine 4. It did not seem like a realistic career path, but I continued to develop some skills in that area. Towards the end of summer after high school, I got a surprise phone call that there was an opportunity for me to receive a basketball scholarship. My college didn’t offer IT, but I thought I could pursue a health-related field in systems design by leveraging the skills I was still developing at the time.

Just like many college students, you take classes you don’t think are particularly useful. However, an Introduction to Psychology course changed everything. I realized that principles of human cognition and behavior help explain how people use technology. While most of the department focused on clinical work, I gravitated to cognitive and applied topics, finished the psychology requirements quickly, and expanded into biology to complement my health science training. I had actually never realized that most of psychology is clinical until much later. Alas, I discovered Erin Reynolds’ Nevermind—a biofeedback-driven game whose world responds to the player’s physiological state—which pulled me toward Human-Computer Interaction (HCI). I completed an undergraduate thesis in HCI and then pursued Human Factors Psychology with an HCI focus. It was completely by luck that I was even allowed to pursue biology, due to a new university policy limiting how many credits a student could take. I was very luckily grandfathered in. In the same way, despite not having the GPA requirement for honors, I was allowed to pursue an honors thesis in human-computer interaction. And once again by happenstance, I was given an opportunity to leverage my IT and psychology background in an externally sourced coding class, which I later grew to be the lead instructor of data science for that company.

All of these coincidental moments provided me an interdisciplinary basis to see design in many fields and contexts. On the health science and biology side, it became biofeedback, brain-computer interfaces, affective computing, ergonomic product design, etc. On the psychology and IT side, it became systems design, cognitive systems engineering (this came later), interface design, and the theory that supports such designs (cognition, perception, etc).

In Human Factors, I drew on my pre-engineering background to design educational and experimental software and to prototype games, applying psychological theory and engineering methods. That work led me into User Experience (UX), where I discovered that ideas like affordances connected directly to my educational curriculum and to my colleagues. Today I approach design holistically through interdisciplinary functions: align technological function with human needs, constraints, and real contexts. I’m focused on applying this combined perspective—engineering, psychology, HCI/UX, and data—to meaningful, data-driven projects.