Transformative Discovery: Integrating Coaching Principles for Project Success

The Human-Centered Approach to Discovery

At the core of effective discovery work lies the importance of coaching when gathering requirements. Over time, I’ve realized that meaningful insights rarely emerge from rigid templates or formal interviews; instead, they arise through genuine conversations where people feel supported enough to pause, think deeply, and express what they need.

Often, an initial request such as “We need a dashboard,” or “Can you shorten this workflow?” uncovers more fundamental issues like decision-making, team alignment, confidence, or communication barriers. By approaching discovery with a coaching mindset, we can reveal these underlying concerns rather than just addressing superficial symptoms. If you’ve ever experienced a discovery session that seemed more like coaching than interviewing, you’ll recognize the value of intentionally cultivating this dynamic.

Reflecting on my recent years of interviews, I’ve noticed a shift, they increasingly resemble coaching sessions. Initially, I thought I was merely “collecting requirements,” but over time, it became clear I was guiding people in clarifying their actual needs. Rather than just recording their requests, I was facilitating their thinking.

In early design meetings, users typically begin with basic asks: “We want a dashboard,” “Can you make this workflow shorter,” “Can we have a button that does X?” These are useful starting points, but they seldom tell the whole story. When I consciously adopt a coaching approach, slowing down, listening attentively, and posing thoughtful questions, the dialogue changes dramatically. At that moment, our focus shifts beyond the user interface into deeper topics: friction, decision-making processes, confidence, accountability, ambiguity, and the human elements hidden beneath feature requests.

Many professionals who have spent decades in their roles rarely get the chance to reflect on the patterns shaping their daily work. So, when I ask something as straightforward as, “What’s the hardest part about planning next season?” the answer often reveals gaps and bottlenecks behind the scenes, rather than issues with the software itself. These stories simply don’t surface during standard meetings.

Uncovering Deeper Insights through Curiosity and Coaching

Curiosity allows us to explore areas untouched by process charts and requirement documents. Prioritizing the individual over the process exposes context that’s invisible on paper, like emotional burden, workplace politics, quiet worries, workarounds, and shared tribal knowledge. Coaching fosters an environment where all these factors come to light, transforming them into valuable material for design decisions.

I used to think the better I got at systems, the less I’d need to do this. But it turned out the opposite is true. The better the system, the more human the conversations become. Coaching is almost like a bridge, helping people cross from “I think I need this feature” to “Here’s what I’m actually trying to solve.”

Active Listening and Guided Curiosity

Active listening forms the core of my approach, ensuring I deeply understand not just participants’ words but the meaning behind them. I reflect statements back — such as, “So it sounds like the challenge isn’t entering the data, it’s aligning on which data to trust, right?” — to confirm genuine understanding. This often transforms technical discussions into conversations about alignment, ownership, or governance.

A key tool is the “Five Whys” technique, which I use as a guide for curiosity rather than a rigid checklist. If someone requests better notifications, I’ll probe: “Why is that important?” and follow with questions like, “Why is it hard to notice things right now?” or, “What happens when you miss something?” By the fourth or fifth ‘why,’ the conversation surfaces underlying factors such as workload, confidence, or fear of missing out, revealing emotional and operational triggers beneath the initial request.

In workplaces, these deeper issues often connect to organizational culture. For example, a request for faster workflows sometimes indicates a real need for predictability or reduced chaos, rooted in communication or authority structures rather than the system itself. Recognizing these patterns enables more effective design decisions by addressing root causes instead of just symptoms.

Intentional silence is another valuable technique. After asking a question, I resist filling the pause, giving participants space to think and speak freely. This silence often prompts unfiltered insights, especially when someone is on the verge of articulating something new. Allowing this space helps participants trust and own their insights, leading to more meaningful outcomes.

Future-Focused Exploration and Empowering Language

I also employ future-anchoring questions like, “Imagine it’s six months after launch — what does success look like for you?” or, “If the system made your job easier in one specific way, what would that be?” These help participants shift from immediate concerns to aspirational thinking, revealing priorities such as autonomy or coordination that guide design principles.

Tone and language are critical for psychological safety. I aim to make discovery feel inviting, often assuring participants, “There’s no wrong answer here,” or encouraging them to think out loud. When people use absolutes — “We always have to redo this,” “No one ever gives us the right information” — it signals where they feel stuck. I gently challenge these constraints by asking, “What might need to change for that to be different?” This opens possibilities and helps distinguish between real and internalized limitations. Coaching-based discovery is key to uncovering and addressing these constraints for lasting change.

Reflections and Takeaways

Coaching Tools as Foundational Practice

Initially, I viewed coaching tools as separate from implementation work, and more of an optional soft skill than a crucial element. Over time, my outlook changed: I saw these tools as fundamental to successful outcomes. I noticed that the best results happened when participants truly took ownership of the insights we discovered together. That sense of ownership was strongest when the understanding came from them, even with my guidance. Insights gained this way tend to last longer and have a greater impact.

My approach to discovery has evolved significantly over time. Initially, I viewed discovery as a process focused on extracting insights from users. More recently, it has transitioned into facilitating users’ own self-discovery, enabling them to articulate intuitions and knowledge that may have previously been unexpressed. This progression from a transactional checklist to a collaborative and transformative meaning-making practice has had a substantial impact on my design methodology.

Efficiency through Early Alignment and Clarity

Contrary to prevailing assumptions, coaching-based discovery does not impede project timelines. Although it demands greater initial investment of time, the resulting enhanced alignment and mutual understanding often expedite progress. Early engagement in substantive discussions enables teams to minimize rework, clarify decision-making processes, and avoid misinterpretations, which can ultimately result in projects being completed ahead of schedule due to unified objectives.

Efficiency is driven by clarity. When users feel acknowledged and their perspectives are incorporated, their level of engagement and willingness to collaborate increases. The trust established during these interactions persists throughout testing, feedback, and rollout stages, mitigating many subsequent problems that typically occur when user requirements are not considered from the outset.

Strong Implementation Questions Are Strong Coaching Questions

At their core, effective implementation questions are essentially strong coaching questions. These are fuelled by curiosity, maintain a non-judgmental tone, and aim to empower others. Instead of guiding someone toward a set answer, such questions encourage individuals to uncover their own insights about the work.

Regardless of the type of discovery — be it design, implementation, or workflow — insight comes from those directly involved. Coaching goes beyond mere technique; it represents a mindset based on the belief that people already hold valuable wisdom. The coach’s job is to help draw out this knowledge, using thoughtful questions.

A key moment in coaching-based discovery happens when someone has a sudden realization, saying things like, “I’ve never thought about it that way,” or “Now I understand why this keeps happening.” These moments are where improvements in design and implementation begin.

Such realizations act as anchors throughout a project. When team members shift their understanding, these breakthroughs can be revisited during times of complexity or tough decisions, providing direction as a “north star” to keep teams aligned.

Coaching is not just a resource, it should be demonstrated in everyday interactions. As teams experience its benefits, they often adopt coaching practices with each other, leading to genuine transformation that extends past individual projects and influences wider workplace culture.

Ultimately, the real value of this work lies not just in the solutions themselves, but in the conversations that reshape how people engage with their work.

Applying the EPIS framework to Dashboard Design and Implementation 

Introduction 

In a previous post, I discussed the various types of dashboards and the design principles and best practices for dashboard design and development [1]. I highlighted how the Exploration, Preparation, Implementation and Sustainment (EPIS) framework has proven to be an effective methodology to ensure the proper application of user-centered design methodology and implementation science to dashboard design.   

Through a structured four-phase approach, EPIS ensures that dashboards are designed with a deep understanding of the user problems and needs. It also ensures that dashboards remain usable and provide the most value and impact through proper initiation and monitoring post implementation.  

Through its application of the multi-phased approach to new interventions and technologies, EPIS further strengthens the opinions I expressed in my previous post, that a well-designed dashboard can become a co-pilot and an effective tool in enabling users to gain insights and make key decisions. When properly applied to dashboard design, EPIS can help establish the foundation and architecture for the entirety of the application the dashboard serves. 

The EPIS framework 

EPIS comprises of four phases that align well with the design and implementation of a dashboard through clear identification of outer systems and inner organizational contexts [2]. Even though the EPIS framework starts with exploration, it is important to consider the sustainment phase as work progresses throughout the different phases. This ensures that the dashboard is designed and developed with sustainability in mind. The EPIS framework consists of four phases: 

Exploration – this phase is informed both by user-centered design methodology and implementation science to identify the data metrics and functionality the dashboard must offer. The process begins by identifying the actors and conducting interviews to further understand their needs, and what data metrics would be most useful to them. Interviews may be guided by the Consolidated Framework for Implementation Research (CFIR), which provides a taxonomy and structure for evaluating implementation factors across multiple contexts [3][4]. CFIR consists of five domains that include intervention characteristics, outer and inner settings, characteristics of individuals and process. These domains inform the collection and assessment of user feedback and provide a framework for documenting decisions and evaluating outcomes.  

Preparation – the preparation phase considers the data metrics identified and their value, including any interoperability and dependencies among the identified metrics. Barriers and facilitators to implementation are also identified to ensure that data metrics can be implemented with the proper frameworks for coaching, audit and feedback, and that they promote effective decision-making. It is also important to consider data sources and their ease of access. Any lag or performance issues in accessing the data are also identified and addressed. 

Implementation – during implementation, the value, accuracy, interoperability and clarity of the data metrics identified are continuously monitored. Ongoing needs assessment and monitoring of barriers to implementation are also conducted. It is important to continuously seek end user feedback throughout the implementation phase on users’ understanding and interpretation of the data metrics and functionality added to the dashboard. This can be achieved through user interviews, surveys and usability testing sessions.  

Sustainment – the sustainment phase ensures the continued delivery of the dashboard, its associated data metrics, and functionality through an iterative design and evaluation process. A set of important outcomes is established based on the purpose of the dashboard and the user feedback gathered during the preparation phase. The sustainment phase provides ongoing support to ensure that the dashboard delivers the outcomes it was designed to do based on its purpose.  

Figure 1. The EPIS framework.

Applying EPIS to dashboard design 

Applying the EPIS framework to dashboard design emphasizes the application of user-centered design methodology especially during the exploration and preparation phases, while implementation science supports the implementation and sustainment phases.  

In [4] the authors highlight eight recommendations for enhancing dashboard use in the context of EPIS: 

  1. Determine data metrics and value of data accuracy – this applies to all phases and requires identifying data metrics of value to all actors. The actors identified can include end users who may interact directly with the dashboard, and other stakeholders who may not interact directly with the dashboard but may be impacted by it. It is also important to consider the sources of data used and their degree of data accuracy. 
  1. Data interpretability and clarity – applied during the preparation and implementation phases, this recommendation involves assessing potential unintended consequences and monitoring user interpretation of data to ensure accuracy.  
  1. Early and ongoing multi-level needs assessment/identification of implementation barriers and facilitators – applied during the exploration, preparation and implementation phases, this recommendation ensures that designers conduct an ongoing assessment of the needs of all relevant actors who may be exposed to the dashboard or may be impacted by it.  
  1. Design for equity – applied during the preparation and implementation phases, this recommendation addresses any potential data biases and considers the impact of the dashboard on diverse populations. Data presented on the dashboard must reflect the needs of the diverse populations that may be exposed to it.  
  1. Usable and intuitive dashboard components – during the preparation and implementation phases, it is important to ensure that dashboard components are intuitive and easy to understand when visualizing data. The dashboard must be user-friendly, intuitive, easy to understand and visually appealing without requiring the users to seeks assistance to interpret the data.  
  1. Iterative design and evaluation – the preparation, implementation and sustainment phases must incorporate an iterative dashboard design and evaluation process. This is accomplished through frequent usability testing to gather feedback from users and updating the design to address any usability gaps identified.  
  1. Consider appropriate outcomes – during the preparation, implementation and sustainment phases, it is critical that an appropriate set of outcomes is identified depending on the purpose of the dashboard. The dashboard may serve as an enabler for intervention or as the provider of the implementation strategy for intervention. An intervention dashboard is critical for delivering outcomes such that it would significantly impact the effectiveness of those outcomes if it is removed from practice. Implementation strategy dashboards provide insights that support the implementation of an intervention. They assist the user by delivering performance data to inform decisions or behaviours. 
  1. Plan for sustainment – even after the dashboard in implemented and is in use, it is important to develop a plan to ensure the sustained use of the dashboard over the long run. This involves frequent engagement of users to learn about any gaps or barriers to use and proposing solutions to those barriers. The sustainment plan should also consider the short- and long-term needs of users to ensure the dashboard continues to offer value over time.   

Conclusion 

The EPIS framework was developed to address challenges in implementing data-driven solutions in public service environments including child welfare, social services and health care. It has proven to be an effective framework for developing dashboards for any type of application, and a leading guide for the adoption of user-centred methodology and implementation science in the design and development of context-sensitive dashboards. As the framework continues to be refined and adapted to diverse research, policy and practice settings it will continue to establish itself as the foundation for the design and development of usable and sustainable dashboards.  


Designing solutions that work for users is what fuels my work. I’d love to connect and talk through your design ideas or challenges, connect with me today on LinkedIn or contact me at Mimico Design House.


References 

[1] Dashboards Drive Great User Experience 

[2] What is EPS?  

[3] Smith LR, Damschroder L, Lewis CC, Weiner B. The Consolidated Framework for Implementation Research: advancing implementation science through real-world applications, adaptations, and measurement. Implement Sci. 2015;10(Suppl 1):A11. Published 2015 Aug 20. doi:10.1186/1748-5908-10-S1-A11 

[4] Rossi FS, Adams MCB, Aarons G, McGovern MP. From glitter to gold: recommendations for effective dashboards from design through sustainment. Implement Sci. 2025;20(1):16. Published 2025 Apr 22. doi:10.1186/s13012-025-01430-x 

The Power of Optimism in Design: Lessons from Sales and Personal Experience

When we are optimistic, we strive to bting out the best in us and create our best work. An optimistic attitude allows us to be bold and even adventurous with our ideas, and we start to create out of inspiration and confidence in the true potential of our work.

Introduction

In his book Learned Optimism Martin Seligman describes a study in 1985 that focused on fifteen thousand insurance agent applicants to Met Life [1]. The study involved one thousand of the fifteen thousand applicants who failed the standard industry test, but who took an additional test called the ASQ that determined whether they were optimists or pessimists. The higher the ASQ score the more optimistic an agent was determined to be. The goal of the study was to hire these agents as part of Met Life’s workforce and measure the performance of optimistic agents compared to the pessimistic ones.

The study showed that agents in the top half of the ASQ score sold 20% more insurance than the less optimistic ones from the bottom half. Agents from the top quarter of the ASQ sold 50% more insurance than those from the bottom quarter. The study predicted that optimism determined which of the agents survived and sold the most insurance, and it did so about as well as the industry test. Seligman’s study clearly shows the impact of optimism on the success of insurance agents selling insurance, and it enabled Met Life to change their hiring practices to not focus only on whether agents passed the industry test, but also on how optimistic they were.

How does Seligman’s study apply to design? Designers, like insurance agents, are also sales people. They sell ideas, stories and concepts that shape products to a different group of people. While insurance is a discretionary product that consumers can choose not to buy, designers sell key ideas that shape the way business organizations operate, the way they serve their customers and that ultimately shape the business bottom line. In a way, the challenges designers face in their task of selling their design ideas can be just as challenging, if not more challenging, than that of insurance agents. That’s because designers must sell ideas for products that must work and achieve the business and organizational success they set out to achieve. In this case, “not buying” the product is not an option for the organization, and the product and the ideas behind it must succeed.

Why optimism is essential for a designer

A designer’s role is not limited to only being able to tell compelling stories about the design solutions they propose. They need to effectively sell the ideas built into those stories to business stakeholders, who can often be at different levels of seniority and receptiveness. If we were to rely on the results of the study by Seligman as a guide, then optimism and a positive mindset play a key role in enabling a designer to achieve their objectives.

An optimistic mindset is essential to the designer’s success and the success of their team. Every organization has a different culture and level of receptiveness to change, especially when it comes to digital products that reimagine existing business processes. The designer may be working with a business team that is used to following a decades old business processs and they may be resistant to changing it. A successful designer can place themselves in any type of business environment, adapt to the existing culture and learn to work within its confines.

The key is to maintain a positive and optimistic attitude towards the outcomes of the project, the stakeholders and the team the designer works with. This attitude communicates the designer’s confidence in their ability to succeed in their work by demonstrating to everyone around them, that they believe in the value their work and design will bring.

How optimism helped me in my career

On a past project where I led the design for an options trading application at a financial institution, I witnessed the importance of an optimistic mindset firsthand during a design review I was leading. The application I was working on was a highly specialized investing application, and I was working with a team of stakeholders who were extremely specialized in their line of business and had strong beliefs about how the application should behave. The stakeholders believed, and rightly so, that they had a strong understanding of their customers and how they tended to use the application. When I started work on the application, I relied on user research, personas and journeys as well as extensive competitive analysis and frequent peer reviews to propose my designs. During one of the design reviews, as I was reviewing a flow in the design I was proposing, one of the stakeholders in a corner of the room interjected and said:

“I am not sure why we should take his (referring to me) advice on how this should work. We can do a far better and more efficient job at it if we design and implemented it ourselves.”

This comment could have demotivated me and discouraged me from continuing with my review. I could have viewed it as a failure rather than feedback. However, I remember not being phased by it because I was confident in the value of my design and I needed to show that to the stakeholders. Instead of taking that comment and allowing it to impact me negatively, I acknowledged the feedback and I repeated my explanation of the design. I backed up my explanation by invoking the research, the competitive analysis, best practices and peer reviews that led me to the proposed design. Reframing this experience and seeing the positive in it fueled my confidence that I would be able to get buy-in from all stakeholders including the one who offered the feedback. I was able to show them the value in the design I was proposing by acknowledging their feedback and showing my confidence in the design I was proposing through evidence and research.

There was another occasion in my career where I realized the value of having an optimistic mindset. On that occasion, I found that my design was well received by the stakeholders after several design reviews and iterations. However, one day as I was reviewing my design with my manager, I realized he did not have a good opinion of it. This was not uncommon, because a good designer should always operate from a perspective that no design is prefect and that there is always room to improve. However, what was uncommon was the way this feedback was delivered:   

“I understand this design has been approved by the stakeholders, but I don’t like it. It may too late to change it now, but I will make sure to change it in the next product iteration.”

In other words, instead of his feedback being constructive and offering ideas on how to improve the design, it was dismissive and only offered the option of him taking over the design and changing it himself. This could have been another instance where it was easy to feel a sense of failure after all the work and effort I invested in obtaining the stakeholders’ buy-in. Instead, I worked with my manager to understand where he thought the gaps were, took his feedback and incorporated it in the design. I took leadership and ownership of this situation and worked with the project manager to allow me to regroup with the stakeholders, and I was able to successfully obtain their buy-in on the updates.

These experiences, along with many other experiences I had over my career, taught me the power of optimism and a positive mindset. On both of the occasions I mentioned above, I was successful in moving the design toward production with great feedback from end users. I did not give up and maintained confidence in my abilities and my design ideas, and I was able to approach my work from a perspective that was geared towards and focused on success. Everyone I worked with also wanted to invest in my success because it also meant they were also investing in their own success. When a designer adopts an attitude of optimism they quickly notice that everyone they work with also adopts the same attitude. There is no doubt that an optimistic attitude paves the way to overcoming the  challenges and obstacles faced in the design process, even if that took time and effort to achieve.

How a designer can adopt an optimistic mindset

The practice of cultivating optimism is grounded in Psychology. When practiced by the designer, these principles can bring tremendous benefits to their work. Below are the most important principles that can be practiced to cultivate optimism:

Cognitive reframing – Mistakes can happen. Important requirements may be missed or misunderstood. Other times, design decisions are made but no close attention is paid to them until much later in the design stage, and they must be changed. Instead of the designer blaming themselves for mistakes, it is more useful to reframe the mistake, turn it into a learning experience and find a way to pivot.

Gratitude practice – For a designer this means observing and focusing on the occasions when their work makes a positive impact. For example, not everyone may agree with their design direction, and they may often face criticism on what designs should look like. Focusing on the positive aspects and how their design is making a difference to the stakeholders and end users, and appreciating the impact it is making, can inspire the designer to take the feedback received and further improve on their work.  

Visualizing positive outcomes – Visualizing best case scenarios helps create a sense of possibility and cultivates an attitude that anything can be achieved. A designer should focus on the possibilities a design solution can create and operate from a mindset that their design solution will bring the value that is expected.

Surrounding oneself with optimism – Our environment is important in reinforcing how we behave and react. When we surround ourselves with positivity, positive outcomes will come to us. This practice goes beyond the workplace and applies to everything we do in life. If a designer is constantly seeking a positive environment, then that will ultimately reflect in their attitude and how they come across to others they work with. The designer will notice that everyone they work with becomes more receptive to their ideas, and more willing to work with them to develop those ideas and share in their success.

Setting small, achievable goals – Progress fuels optimism, and small wins help us feel capable. When design problems seem difficult or consensus seems out of reach, it is important to set small and achievable milestones, and celebrate when those milestones are achieved.

Practicing self-compassion – We are always our own harshest critics. Speaking to ourselves with compassion and encouragement, not blame, helps us find the positive in our work and move forward with solutions that will allow us to achieve our goals.

Adopting an ‘Experimental’ mindset – This practice is the most important one in cultivating an optimistic mindset. If something does not work, it should be considered as feedback to improve and not a failure. It is all too often that designers experience setbacks where design may not meet expectations. Sometimes business stakeholders or managers may disagree with the designer’s approach. A designer might react to this thinking “I did not do a good job on this design” or “I failed to understand the requirements”. Reframing this as “I can propose different design alternatives” or “I can show how my design improves on the requirements” makes the setback feel less threatening.

Conclusion

Seligman’s study showed that optimism could predict success, above and beyond traditional criteria for hiring insurance agents. The results of the study were so effective that Met Life and other industry players changed their hiring practices to hire agents who scored high on the optimism test yet narrowly failed the industry test. Like this study in insurance sales, the success of a designer in his work also hinges on optimism and a positive attitude. It is possible for a designer to follow and implement the best practices and theory of design, but if they cannot maintain an optimistic and positive attitude they will struggle to achieve a successful outcome. When we are optimistic, we strive to bting out the best in us and create our best work. An optimistic attitude allows us to be bold and even adventurous with our ideas, and we start to create out of inspiration and confidence in the true potential of our work. Optimism empowers a designer to focus on the success of their design ideas rather than the failures, turn setbacks into opportunities and adopt a mindset where everything is achievable even when faced with complex problems.   

References

[1] Seligman, M. E. P. (2006). Learned optimism: How to change your mind and your life (2nd ed.). Vintage Books.

How My Human-Computer Interaction (HCI) Research Shaped My Design Career

“Research in HCI continues to be the primary contributor of the methodologies, technologies and tools we use to support modern application design, and it continues to remind us that the origins of design as a discipline have always been deeply rooted in how humans interact with computers.”

Introduction

The methodology and best practices behind design are constantly evolving, yet they have always been deeply rooted in Human-Computer Interaction (HCI). I think about how my career progressed in context with the rapidly changing nature and landscape of design and usability, especially when it comes to the lightning speed with which AI technologies have evolved and the ubiquity of user interfaces and technologies supporting them.

I have always viewed my time at Queen’s University and my research as a graduate student as the foundation of my career. I did not set out to pursue a career in design or usability when I started my graduate studies. In fact, I thought that my career would evolve around software development or solutions architecture. I had a good theoretical foundation during my undergraduate studies, yet the idea of choosing a research topic that is yet to be explored seemed daunting to me at first. Among all the specialized fields of study in Computer Science such as Data Mining, Machine Learning, and Parallel Computing, I knew that Software Engineering was a topic that I was interested in exploring further.

The research I embarked on with the help of my advisor, Prof. Nick Graham, involved researching and programming user interface libraries that developers would use to write applications [1]. It would take me years after completing this work to realize the significance of its contribution to HCI. That’s because I was initially focused on the execution of the ideas in my research, and designing and writing code to implement user interface libraries. However, the two years I spent doing this work would prove to be transformative in my understanding of application design, and in how my research would shape my thinking and work as a user experience designer, a product designer and an interaction designer.

The Origins of User Experience in HCI

To understand the origins of user experience design and how it evolved, it is important to shed light on how deeply rooted it is in HCI.

The term “User Experience” (UX) was originally coined by Don Norman while working at Apple in the early 1990s. On why he coined the term Norman writes [2]:

“I invented the term because I thought Human Interface and Usability were too narrow. I wanted to cover all aspects of the person’s experience with the system.”

Long before Norman proposed the term “User Experience”, Human-Computer Interaction emerged as a formal research field in Computer Science in the early 1970s and 1980s.

In 1982, the Special Interest Group on Computer-Human Interaction (SIGCHI) was established under the Association of Computing Machinery (ACM). SIGCHI was established as a global body to focus on the emergence of Human-Computer Interaction in the 1970s and 1980s as a major field of Computer Science research, and the rapid shift in computing from command-line interfaces to graphical user interfaces (GUIs). This shift highlighted the importance of human factors, cognitive psychology and ergonomics as key elements in the design of interactive systems.

Since its establishment, SIGCHI has become the most prominent international conference where top HCI researchers and design practitioners present new theories, models and technologies that have helped shape the field of usability and user experience design. SIGCHI cemented the role of HCI as a discipline of Computer Science and established the core theories, principles and methodologies behind the user experience design practice as we know it today, including usability testing, interaction design and service design.

In the Psychology of Human-Computer Interaction, Card, Moran and Newell [3] highlighted the user as the key information processor. Therefore, good systems design must focus on understanding human perception, memory and problem solving rather than hardware and programming. Furthermore, since human attention, memory and perception are limited and predictable, the system must be designed with these considerations in mind.

Card, Moran and Newell established key models that helped establish the foundations of UX research and usability today, with the most notable one being the Model Human Processor (MHP) model. The MHP model identifies the human mind as comprising of three main subsystems:

  • Perceptual – responsible for sensory, visual and auditory input and output.
  • Cognitive – responsible for thinking, reasoning and short-term memory.
  • Motor – responsible for all motor skills required for a user to interact with a system such typing, mouse movement and pointing, and eye tracking.

Jakob Nielsen helped further shift the focus in application development on the user when he formalized the role of usability in software engineering in his book Usability Engineering [4]. Nielsen argued that usability must be an integral part of the software design and development cycle through rapid, iterative, and low-cost methods. Nielsen also defined the five key components of usability as learnability, efficiency, memorability, errors and satisfaction. These components remain the cornerstones of user experience design and its role in the software development lifecycle today.

My Research in HCI Shaped My Mindset As a Designer

My research focused on the topic of User Interface Plasticity, which turned into a published article in an HCI journal [1]. I explored how simple user interface widgets such as a menu and a scrollbar could behave on a desktop computer and a digital whiteboard. I wrote libraries that allowed developers to write an application that automatically rendered scrollbar and menu widgets and adapted them to the device they were running on. In other words, the menu and scrollbar widgets retained plastic properties, which meant they could be ‘molded’ to match the the device they are deployed on. In designing the menu and scrollbar widget libraries, I needed to shift my focus from implementing libraries for the menu and scrollbar widgets to thinking about how these widgets would be used by the users along with the context and device. This relates back to the need to consider the perceptual, cognitive and motor subsystems discussed by Card, Moran and Newell in the MHP model.

The launch of the iPhone (2007) a few years after my research was published, and the iPad a few years after that, sparked a rapid pace of development in UI frameworks for mobile devices and tablets. This pace of development was propelled by the growing adoption of the web coupled with a user base that became increasingly sophisticated, with clear expectations on how applications should behave depending on the devices they were using. The launch of the iPhone and iPad allowed me to understand the importance of my research in defining how user interfaces behaved on different devices.

More importantly, my research shaped my understanding of how applications should behave on difference devices, and how application design overall is governed by the principles of HCI. Until that point, I was trained to write command-line programs on Linux using C, and as long as the program behaved as expected on the command line by providing the right prompts to the user, receiving the required inputs and producing the correct output, the program was considered successful.

Conclusion

The rise of Human-Computer Interaction in the 1970s and 1980s came out of a growing need to enable software applications to better serve users. It was no longer sufficient to expect that command line interfaces would be able to satisfy the needs of all users. As devices and the web evolved, so did users’ expectations of how applications should behave on the variety of devices available.

HCI was still growing as field of research in Computer Science when I embarked on my research at Queen’s University with Prof. Graham, yet it profoundly shaped my mindset as a designer, and how I approached design problems in various industries throughout my career. My research helped lay the conceptual foundations for device-independent UI frameworks that fed into ubiquitous computing, multi-platform design frameworks, adaptive UIs for smart devices and early thinking about device independence and context awareness. Through this work I was able to practice novel concepts at the time such as the MHP model, and other concepts introduced by Nielsen on usability in software engineering.

All of this work helped focus me on solving design problems and designing applications with a clear focus on user interaction. This is why I believe that as designers we must ensure that we always maintain a thorough understanding of the theory and research the HCI field offers. The core foundations of design have always been deeply rooted in Human-Computer Interaction, in Computer Science and in Psychology. Research in HCI continues to be the primary contributor of the methodologies, technologies and tools we use to support modern application design, and it continues to remind us that the origins of design as a discipline have always been deeply rooted in how humans interact with computers.

If this story resonates with you — or if you’re tackling challenges at the intersection of UX design, usability, and emerging technologies like AI — I’d love to connect.

Whether you’re working on adaptive interfaces, modernizing legacy systems, or simply want to apply HCI principles more deeply in your product design, I help teams bridge research, strategy, and practical execution.

Feel free to reach out through LinkedIn. Let’s explore how thoughtful, human-centered design can transform your next project.

References

[1] Jabarin, B., & Graham, T. C. N. (2003). Architectures for widget-level plasticity. In Proceedings of the 10th International Workshop on Design, Specification, and Verification of Interactive Systems.

[2] Norman, D. A. (n.d.). The Definition of User Experience (UX). Nielsen Norman Group. Retrieved July 9, 2025, from https://www.nngroup.com/articles/definition-user-experience/

[3] Card, S. K., Moran, T. P., & Newell, A. (1983). The Psychology of Human-Computer Interaction. Hillsdale, NJ: Lawrence Erlbaum Associates.

[4] Nielsen, J. (1993). Usability Engineering. Boston, MA: Academic Press.

Why AI Won’t Replace Designers: The Human-Centered Core of Design

Introduction

Artificial intelligence (AI) is introducing new capabilities across various professions, including design. As AI continues to evolve, it will increasingly be able to execute tasks that professionals have spent years developing, mastering and specializing in. In design, AI is transforming established design methodology through its ability to generate design drafts significantly faster than what human designers can traditionally achieve.

There is concern that AI could eventually replace designers due to its ability to produce designs of quality that matches or even surpasses those created by experienced professionals. Nonetheless, envisioning a world where AI is able to perfect what is fundamentally a human-centered discipline remains challenging. Design, as a profession that is fundamentally centered on human interaction, is particularly well-positioned to challenge AI’s influence on our daily lives and on our professional practices, and this concept can be extended to other professions and not just design.

In this article, I discuss why design serves as an excellent example of a profession that defines how AI can assist designers by allowing them to produce superior designs with greater efficiency, rather than supplanting the essential skills that proficient designers contribute to their field. I show how AI can make the future of design more exciting and promising as the technology continues to evolve and enable designers to do more. In the process, AI will enable designers to focus on developing the design skills that matter, namely those anchored deeply in design thinking, empathy and user research.  

Design is rooted in Human Factors

Design, as a discipline, is rooted in the ability to comprehend, empathize with and relate to user behaviour and mental models. This is achieved through the designer’s ability to identify and resolve problems by connecting with users, building trust and cultivating empathy. This is how a meaningful co-creative environment is established, where design is genuinely focused on addressing user needs and providing solutions that positively influence human agency and fosters social, organizational and demographic constructs.

Effective design empathises with users by first diving deep into their requirement while taking into account the thoughts, feelings and emotions they would experience through their interaction with an application. This human-centered approach to design is at the core of why artificial intelligence may not be able to fully supplant designers.

User behaviour is unpredictable

User behaviour does not always follow predictable patterns that are documented and defined through data. Each design problem has unique requirements based on the users, their context and environment, and how they navigate their surroundings. Real world user behaviour is fluid and not always predictable. Physical and social contexts profoundly influence how users think, understand, and act. The concept of situated action is essential to understanding why AI, which relies on predefined and existing models, fails to capture the complexities of human-centered design [1].

In describing embodied interaction, Paul Dourish [2] emphasizes the importance of considering the connection between mind and body when addressing design problems, rather than solely focusing on immediate issues. This approach necessitates observing and engaging with users, acknowledging that the intricacies of their daily lives can influence their actions, thus requiring design solutions that go beyond the linear and well-defined models characteristic of AI.

Designers anticipate complexity

A designer can pose intuitive questions to foresee potential challenges users might encounter, especially in unique or ambiguous situations. Don Norman’s example of “Norman Doors” [3] effectively demonstrates the importance of human-centered design in conveying functionality through affordance and feedback. It is therefore challenging for AI to anticipate and predict complexities in design and effectively address user problems.

Artificial intelligence can only identify issues when provided with comprehensive sets of data that encompasses as many patterns and probabilities of human behavior, and how all these patters and probabilities can be applied to solutions for various design challenges. This task is further complicated by the uniqueness of individual users’ thinking and behavior. Designers, on the other hand, endeavor to discern overarching patterns in user behavior and common themes, and pinpoint opportunities for design enhancement through usability testing and user research.

A small percentage of users will always manifest unique needs, perspectives, and methods of interacting with the user interface, requiring the designers to make strategic and deliberate design decisions on how best to accommodate these users while balancing the overall goals of the application. The key takeaway is that user behavior patterns are constantly evolving and can be unique to different users and user groups, making it impractical to accurately encapsulate these behavior patterns through data to be used by AI models.

AI cannot co-create meaningfully with users

For design to be usable and effective for users, it must be executed with them rather than for them. This concept is inspired by the political and social context of Scandinavian trade unions in the 1970s and 1980s, which advocated for greater participation in the design of IT systems utilized in their workplaces [4]. It underscores the notion that design is inherently collaborative, focusing not only on creating tools to provide solutions but also on developing tools that navigate human agency and organizational structures. Designers create for eve evolving user groups with diverse ages, socio-economic backgrounds, geographical locations, and professional contexts, and the key to successful design lies in co-creating solutions that serves the needs of these diverse user groups.

Designers often lead this co-creation process by building trust and fostering principles of shared goals and collaboration. This approach helps deliver meaningful products that genuinely assist users in achieving their objectives and addressing their needs. AI cannot replace the invaluable ability of designers to navigate power dynamics, facilitate feedback, and ensure inclusive design.

What AI Can and Cannot Do

AI presents valuable opportunities for designers by serving as a collaborative partner. It can greatly enhance the designer’s output in several ways, such as:

  • Rapidly generating visual mockups tailored to the designer’s specifications.
  • Searching through extensive datasets of design patterns.
  • Automating tasks like adding content and creating simple flows.
  • Generating functional prototypes and interactive user interfaces from designs or prompts.

However, as previously discussed, AI has limitations when it comes to essential design tasks. Specifically, it is unable to:

  • Navigate power dynamics and feedback loops during stakeholder presentations and design reviews.
  • Perceive users’ feelings and emotions with sensitivity during user research sessions.
  • Establish deeply meaningful trust and authentic co-creation relationships with stakeholders and end users.
  • Comprehend users’ needs thoroughly and understand how complex contextual factors can influence their behavior.

Real-World Design Challenges Require Human Judgment

Design must not only ensure that user needs are addressed in an application but also meet the requirements of stakeholders and the business behind the application. Otherwise, poorly designed applications can lead to financial losses in financial applications, exclusion of user populations such as those with accessibility needs in government applications, and potential harm in healthcare applications. In addition to human factors, design demands deep empathy, accountability, and the ability to anticipate future risks. These characteristics are intrinsic to the human-led design process and cannot be easily automated or replaced by AI.

Therefore, AI should be considered as a designer’s creative partner rather than their replacement, providing powerful tools to produce designs more efficiently and create highly interactive and code-ready prototypes. Designers who learn to leverage AI in their work will shape its role in design and can spearhead the movement towards more informed and user-centered design using this innovative technology. AI will not independently shape the future of design, instead designers will drive AI’s integration into the design process while maintaining the core principles of design such as empathy, design thinking, and user research, principles that AI cannot easily and reliably adopt.

Conclusion

Our apprehensions regarding AI may be justified if it were capable of independent thinking and applying human-centered design principles to individual design problems, addressing user pain points, needs, and goals. Such concerns might also be warranted if AI could effectively communicate with end users and stakeholders, understand their requirements, and lead an ongoing process of refinement and interaction to achieve outstanding design outcomes. Despite continuous advancements in AI, the inherently human-centered nature of design ensures that it remains focused on understanding people rather than merely producing data-driven results. AI will continue to serve as a tool that enhances the designer’s mindset and skill set, which are profoundly rooted in humanity.

References

[1] Suchman, L. A. (1987). Plans and Situated Actions: The Problem of Human-Machine Communication. Cambridge University Press.

[2] Dourish, P. (2001). Where the Action Is: The Foundations of Embodied Interaction. MIT Press.

[3] Norman, D. A. (1988). The Design of Everyday Things. Basic Books.

[4] Bødker, S., Ehn, P., Sjögren, D., & Sundblad, Y. (2000), Co-operative Design – Perspectives on 20 Years with the Scandinavian IT Design Model, Proceedings of DIS 2000