Artificial intelligence (AI) is increasingly woven into the fabric of our daily lives, from recommendation engines to large language models (LLMs) that assist with our professional tasks. However, there is a growing concern that our reliance on AI systems promotes cognitive offloading, diminishes critical thinking, and disrupts the development of human mastery. As users delegate reasoning to AI systems, bypassing traditional methods of developing expertise, they reduce their critical engagement with their tasks. All of these factors warrant a deeper exploration to understand the implications of AI on human cognition, creativity, and innovation.
Current research into the influence of AI across different age groups and contexts is beginning to expose the full impact of cognitive offloading. Gerlich’s 2025 study, “AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking,” [1] provides valuable insights into this phenomenon. Through a mixed-methods approach to research, involving 666 participants, Gerlich found that heavy AI use significantly reduced users’ critical-thinking skills, mainly because users offloaded cognitive tasks to AI tools rather than engaging deeply with problems themselves. Younger participants, in particular, exhibited a higher dependence on AI tools and lower critical-thinking scores, emphasizing the need for strategies to mitigate these cognitive costs.
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Gerlich’s research builds on previous research into other information-on-demand technologies such as Internet searches, [2] mobile-phone directories, and global-positioning systems (GPS), each of which reduces the need for memorization and the internalization of knowledge. Collectively, these tools have reshaped cognitive practices by externalizing memory and diminishing the need to internalize spatial, procedural, domain-specific knowledge and even cognitive skills such as memorizing phone numbers and other behavior patterns. AI now extends this trend into areas requiring higher-order thinking and expertise, further challenging traditional pathways to skills development. As AI integrates with more business processes, we risk eroding the expertise that users develop through repetition, intuitive automation, and unconscious competence. Like AI, humans rely on training data—that they gain through real-world experiences, critical engagement, and internalization—to develop intuition, pattern recognition, and instinctive decision-making. [3, 4, 5] When people bypass these processes, their depth of expertise and the ability to take leaps of creativity and innovate suffer.
Why This Matters: Innovation, National Security, and Future Generations
AI's integration into workflows poses a significant challenge: the erosion of critical-thinking skills. Without these skills, the human ability to innovate through intuitive leaps—those Eureka! moments when insights converge to spark groundbreaking ideas—diminishes. This loss affects individual’s expertise, as well as the collective capacity for creativity and problem-solving.
Innovation drives business differentiation, giving companies a competitive edge. When work relies solely on variations of pre-existing AI-generated outputs, organizations risk stagnation because of their inability to forge unique paths or disrupt industries.
Moreover, the implications extend beyond economics to national security. A workforce that depends on AI at the expense of critical thinking impairs future generations’ ability to lead in thought, technology, and innovation. Within an increasingly competitive global landscape, countries that foster the development of deep cognitive skills retain a strategic advantage—economically and militarily—ensuring their dominance in AI-powered business, manufacturing, healthcare, warfare, cybersecurity, and defense technologies. Although initiatives such as President Trump’s push for global AI dominance underscore the urgency of leading in AI technology, we must also prioritize the cultivation of human expertise for long-term strategic advantage. Striking a balance between AI advancements and the preservation of human ingenuity is critical to maintaining a competitive edge globally.
AI Design Challenges and Opportunities
The potential cognitive impacts of broad AI use present UX designers and business-process analysts with a new challenge: balancing the immense potential of AI with the foundational principles of human mastery. We must leverage AI to streamline decision-making and avoid redundant focus on skills we’ve already mastered, but not at the expense of informed decision-making and opportunities for developing and retaining new skills or deepening our expertise. The challenge lies in designing tools and processes that maintain AI’s velocity and efficiency while preserving critical thinking and mastery—a balance that fosters innovation rather than dependency, reinforces critical thinking, engages human creativity, and encourages deep cognitive interaction.
The rise of AI tools in UX and customer experience (CX) design highlights how these technologies can inadvertently foster the illusion of expertise. For example, UX designers might use AI-generated wireframes, personas, journey maps, or design recommendations without fully understanding the data or assumptions driving those outputs. This could result in user experiences that reinforce biases or address surface-level issues while missing deeper usability challenges.
Consider a healthcare app as an example. Without domain expertise, a UX designer might overlook critical nuances such as the emotional and cognitive needs of patients and caregivers. If the designer has never spoken to real patients, he might fail to understand the cognitive impairments patients experience from physical illness, pain, fatigue, and emotional distress. Similarly, without engaging with caregivers, he might not grasp the pressures they face in balancing their lives while caring for family members. Each of these oversights presents an opportunity to innovate—to meet an unmet need that could provide a market differentiator or inspire a new product line.
As UX designers and process analysts, we are uniquely positioned to influence the design and implementation of AI to enhance its role as a decision-support tool that complements human judgment. AI should develop and build upon human cognition rather than replacing it. To reach this goal we must overcome or mitigate the following design challenges:
the allure of cognitive offloading
the perception of AI infallibility
reinforcement of cognitive bias
encouragement of passive thinking
loss of domain-knowledge retention and development
overestimation of skills and promotion of shallow understanding
impaired ability to discern true human expertise
the echo-chamber effect
psychological effect of AI output speed
isolation of AI-augmented workforces
The Allure of Cognitive Offloading
According to cognitive-load theory, [6] humans naturally conserve cognitive resources by simplifying tasks. This evolutionary tendency becomes problematic when AI-generated information creates an illusion of expertise, bypassing deeper cognitive engagement and encouraging reliance on external systems. This occurs particularly with LLMs, which often present information with authoritative certainty despite their limitations. Gerlich’s research confirms this tendency, showing that participants frequently trusted AI systems because of their speed and apparent expertise, causing cognitive offloading and diminishing critical engagement. Consider mitigating this challenge using the following user-interface (UI) disclosures:
Provide transparency into confidence levels. AI systems should disclose their confidence levels, the limitations of their training data, and potential sources of error. Transparency could help users understand when to question or verify AI outputs.
Define the boundaries of AI capabilities. AI systems should regularly remind users of the boundaries of their capabilities. Tools should prominently display information about what the AI can and cannot do.
The Perception of AI Infallibility
AI’s ability to process vast amounts of data quickly reinforces the perception of expertise, leading users to assume that AI-generated outputs are infallible. UX studies demonstrate that people equate technical complexity with competence, further deepening their trust in AI. However, such trust in AI is dangerous in contexts requiring nuanced understanding or iterative problem-solving because it risks eroding human expertise and intuition. Despite their sophistication, AI systems are not sentient experts. They are pattern recognizers and their knowledge is limited to the data on which they were trained. While an AI can detect correlations and suggest outcomes, it cannot comprehend meaning or context as human experts do. Consider mitigating this challenge through training and UI notifications, as follows:
Institute AI literacy. Prioritize the teaching of digital-literacy and critical-thinking skills, empowering users to assess the reliability of AI-generated information.
Provide prompt-creation support. AI user interfaces could provide users with more instruction and feedback on developing effective prompts and asking exploratory questions to bridge their knowledge gaps. Tools that offer guided prompts or highlight related knowledge paths can help users refine their inputs and expand their understanding.
Reinforcement of Cognitive Bias
By presenting results in a deterministic manner, many AI-driven tools and systems risk reinforcing cognitive biases that lead users to over-trust simple solutions. [7, 8] User prompts reflect existing domain knowledge, opinions, and biases, resulting in responses that are constrained by the user’s limited perspective. Confirmation bias can lead users to selectively trust AI outputs that align with their expectations, while ignoring the AI’s limitations or alternative interpretations. Consider mitigating this challenge by using UX design patterns and conducting systems audits.
Challenge cognitive biases. Embed features that highlight alternative interpretations or edge cases. For example, tools could surface contrasting data points or scenarios to encourage critical thinking.
Conduct continuous auditing. Periodically conduct manual reviews and testing to identify bias, improve accuracy, and ensure that outputs remain aligned with ethical guidelines.
Solicit real-time user feedback. Involve subject-matter experts (SMEs) more consistently in day-to-day oversight, enabling real-time fine-tuning. Embedded feedback functions, performance monitors, and dashboards that provide oversight and track anomalies can alert teams to potential drift in performance and ensure timely intervention.
Encouragement of Passive Thinking
Research shows that frequent interactions with LLMs fosters passivity rather than active knowledge-building and critical thinking. [9] In UX design and business process re-engineering (BPR) this could lead to a phenomenon in which users repeatedly defer to an AI, without forming their own opinions or a deeper understanding of workflows. This results in an ever-increasing skills gap over time. Consider mitigating this challenge through UX design, training, or cultural changes, as follows:
Reinforce human-AI collaboration. UX designers must internalize users’ real-world problems; leveraging distributed cognition through collaboration with experts, engineers, and users to challenge biases; think critically, and identify opportunities for innovation. An AI can assist by compiling feedback from user-research data and identifying themes and patterns, thereby reinforcing, and prioritizing key insights.
Promote active engagement. Design AI systems that require users to input their reasoning or decisions before showing them AI suggestions. For example, in business-analytics tools, you could ask users to predict outcomes or flag potential patterns before presenting AI-driven insights.
Develop critical-thinking exercises. Regularly include exercises that require employees to challenge AI outputs or provide counterarguments, fostering a culture of questioning and innovation.
Reward human contributions. Acknowledge and celebrate moments in which human creativity or expertise have made a difference, reinforcing the value of critical thinking and hands-on experience.
Create diverse input mechanisms. Design processes in which users interact with an AI an through various modalities such as text, visuals, and interactive decision trees. This approach engages different cognitive pathways and reduces the risk of passive acceptance.
Loss of Domain-Knowledge Development, Retention, and Recall
Past research shows that, as people rely more on external tools such GPS and search engines, they might become less likely to retain information or develop problem-solving instincts. The Google effect provides an example, [10] where users remember where to find information rather than the content itself. This loss of underlying knowledge retention could erode the iterative learning processes that are essential for building intuition and expertise. A loss of foundational knowledge and real-world context makes it harder for experts to adapt or innovate. Over time, this reduction in underlying domain knowledge inhibits an expert's ability to discern nuance, recognize debunked theories and outdated information, or recall mitigating strategies and edge cases. Consider mitigating this challenge through quizzing, reflection, and cultural changes—for example:
Create memory aids and testing. Periodically quiz or prompt users to help them recall previous decisions or key knowledge. This strengthens their memory and keeps them engaged with the material over time.
Embed reflection steps. Incorporate mandatory reflection or review stages in workflows when users summarize what they’ve learned or explain their rationale for decisions.
Establish a human-centered culture. Maintain messaging that AI is a support, not a replacement for human expertise. Embed messaging within processes that emphasize AI’s role as a collaborator or assistant rather than as a decision-maker.
Overestimation of Skills and Promotion of Shallow Understanding
The lower understanding of a knowledge domain often correlates with an over-estimation of skill and ability, a psychological condition known as the Krueger Dunning Effect. [11] Thus, users with only a surface-level understanding could misapply AI-generated insights in complex decision-making scenarios. For example, a junior project manager responsible for supply-chain optimization might use an AI-generated report to cut costs by eliminating a vendor without understanding the critical role that the vendor plays in maintaining redundancy during peak seasons.
Similarly, a UX designer who is unfamiliar with accessibility needs might rely on AI-recommended layouts without realizing that they must be paired with the underlying data for screen readers or alternative navigation methods. Such oversights highlight the risks of relying on AI without adequate domain knowledge. AI-generated insights that users misinterpret or blindly trust can exacerbate inequities. For example, an AI-generated design feature that is based on generic user data could unintentionally disadvantage underrepresented groups of users. Consider mitigating this challenge using UX design, business processes, training, and cultural changes, as follows:
Implement automation gradually. Provide controls that let managers introduce automation gradually, allowing more junior team members to develop expertise as they use the tool. For instance, enable manual overrides and require periodic user input to prevent over-reliance on the tool by novice users. Create reports that evaluate the sophistication of user engagement to identify skills gaps and inform career-development plans.
Establish feedback loops. Provide users with detailed feedback, explaining how the AI made certain decisions or why the AI system made specific suggestions. Conversely, encourage expert users to provide feedback to refine and inform AI responses. This both reinforces users’ understanding of the underlying data and improves decision-making processes.
Create simulated learning environments. Use AI to create immersive simulations or scenario-based training, encouraging users to engage with problem-solving in realistic contexts without offloading cognitive effort entirely.
Foster a knowledge-sharing culture. Encourage employees to share their expertise by documenting insights or mentoring others, ensuring the distribution of knowledge rather than confining knowledge to individuals or tools.
Impaired Ability to Discern True Human Expertise
Hiring managers, project managers, and technical leads must evaluate whether job candidates demonstrate genuine expertise or present polished, AI-assisted answers and portfolios during interviews. The latter decreases their ability to discern real insights from surface-level knowledge, thereby impairing their ability to develop teams with the right balance of novice, mid-career, and expert team members. Consider mitigating this challenge through the following cultural changes and collaboration activities:
Encourage mentorship and pairing. Establish a mentoring program and implement the pairing of novices with mid-career or expert team members to bridge knowledge gaps and ensure that you’re cultivating genuine expertise within teams. Through pairing, senior staff can identify areas where junior team members lack understanding and provide targeted suggestions for their skills development. This collaborative process enhances the skills of less experienced individuals, reinforces expertise in experienced staff, and ensures the sharing and refinement of critical knowledge and decision-making processes across the team.
Remove the stigma of ignorance. A cultural shift is necessary to normalize the admission of knowledge gaps and encourage curiosity, vulnerability, and humility. Business processes can incorporate moments that encourage stakeholders to state uncertainties and seek external input. Design workshops and UX feedback loops can include reflection points for identifying blind spots and collectively addressing gaps. Organizations can implement anonymous feedback boards to allow team members to share questions or gaps without fear of judgment. You could also employ rotating subject-matter expert panels to create question-and-answer (Q&A) safe spaces.
Engage in design critiques and showcases. These events can provide a forum for presenting early ideas for feedback, questions, and challenges. Empathy-driven peer-pairing sessions during short-term sprints can also build trust and foster collaboration while respecting individual contributions. This cultural openness complements empathy-driven design practices and reinforces active knowledge-building.
The Echo-Chamber Effect
Because AI systems primarily recognize patterns in existing data, they cannot suggest novel solutions. Plus, since we train AI systems on data that derives from earlier patterns, this risks repeatedly reinforcing the same ideas. Without external insights or real-world observations, this iterative data reuse stifles innovation and could degrade creativity and originality over time.
Innovation often requires moving beyond historical patterns through real-world experience and brainstorming with diverse teams of experts. For example, an AI design for accessibility might present existing, approved Web Content Accessibility Patterns (WCAG). In contrast, UX designers observing how users with vision impairments navigate Web pages or users with mobility issues interacting with mouse controls might envision completely new ways of interacting with information—for example, integrating AI with multimedia experiences, voice command–based user interfaces, or gesture-recognition systems. The echo-chamber effect reinforces the importance of human observation and contextual insights. Consider mitigating this challenge through UX design techniques and collaboration activities:
Conduct real-world validation. UX designers and decision-makers must engage directly with real users [12] to validate AI-generated insights and experience users’ painpoints firsthand that data alone cannot convey. Such validations could reveal users’ overlooked needs or emergent behaviors that could drive design differentiation.
Present uniqueness metrics in AI interfaces. Incorporate a feature within AI design tools that calculates a uniqueness score for research hypotheses, technical solutions, or design patterns. This score could evaluate how innovative a solution is by analyzing its similarity to existing datasets. Such a tool could prompt users to explore uncharted creative territories, fostering innovation by encouraging divergence from known patterns. For example, UX designers and product teams could use this uniqueness score to identify areas in which AI suggestions replicate existing work too closely, sparking opportunities for differentiation and originality.
Facilitate cross-functional communication. AI can foster cross-functional communication by providing shared visualizations, prompting interdisciplinary collaboration, or integrating asynchronous feedback features within distributed work environments. Such tools can act as collaboration accelerators, facilitating shared insights and communication between globally distributed teams. Plus, shared AI systems can passively monitor data sets or files to identify patterns that indicate similar problem spaces or opportunities for solution reuse. For example, an AI can suggest relevant team members or expertise, enriching distributed cognition and ensuring the inclusion of diverse perspectives in problem-solving. AI-enabled dashboards showing users’ painpoints can remind teams of real-world contexts. In contrast, user-simulation features such as accessibility simulations can immerse UX designers in user experiences, fostering empathy-driven solutions.
The Psychological Effect of AI Output Speed
The rapid speed at which AI generates outputs can unintentionally contribute to a psychological tendency in which humans prioritize novelty and immediacy over careful review and reflection. This behavior is rooted in heuristic processing and cognitive-load reduction and could increase the risk of errors or encourage surface-level decisions. As users become accustomed to the excitement of quickly developing their ideas, they might disengage from the deliberate, critical evaluation that is necessary to identify nuanced or complex issues, ultimately undermining decision-making and design quality. Consider mitigating this challenge using the following UX design patterns and business processes:
Encourage measured engagement. AI user interfaces could integrate optional review prompts or visual representations of uncertainty to slow down decision-making when necessary, ensuring that users remain cognitively engaged with the task.
Establish quality-review workflows. Robust quality-review workflows, including dedicated editorial staff for written materials and technical reviewers for user interface and interaction designs could offset impulsivity and ensure critical evaluation.
The Isolation of AI-Augmented Workforces
Distributed workforces often rely heavily on people’s independent contributions rather than collaborative design sessions. Developers, in particular, often work in isolation when coding, limiting interactions outside their immediate tasks. The introduction of AI tools could intensify this isolation by offering users instant responses and actionable outputs, making collaboration with colleagues seem slower or less efficient in comparison. Users might develop stronger emotional attachments to an AI, preferring its judgment-free guidance over the opinionated or disagreeable feedback of peers. This shift could erode interpersonal dynamics, disrupt collaboration, reduce knowledge-sharing opportunities, and lead to missing the innovations that would have emerged from group ideation and collaboration sessions. Consider mitigating this challenge through the following business processes, cultural changes, and UX design patterns:
Encourage collaboration. Create processes that emphasize cross-functional teamwork, using AI tools to support, not replace, brainstorming and critical discussions.
Implement AI as a collaboration accelerator. AI can foster cross-functional communication by providing shared visualizations, prompting interdisciplinary collaboration, or integrating asynchronous feedback features in distributed work environments. Such tools can act as collaboration accelerators, facilitating shared insights and communication between globally distributed teams. Plus, shared AI systems can passively monitor data sets or files to identify patterns that indicate similar problem spaces or opportunities for solution reuse. For example, an AI can suggest relevant team members or expertise, enriching distributed cognition and ensuring the inclusion of diverse perspectives in problem-solving. AI-enabled dashboards showing users’ painpoints can remind teams of real-world contexts, while user-simulation features such as accessibility simulations can immerse UX designers in experiences that foster empathy-driven solutions.
Use AI as a team connector. Reframe the role of AI as a facilitator of teamwork rather than as a standalone contributor. AI could identify team members with relevant expertise for specific tasks or highlight areas in which collective input is necessary. By positioning AI as a bridge rather than a substitute for collaboration, these tools can reinforce human connections within workflows.
Reward collaboration. Implement gamified metrics that incentivize collaborative behavior. Features could track and reward actions such as sharing AI outputs with peers or including team members in refining decisions. Recognition of such behaviors could cultivate a culture that values teamwork over individual AI interactions.
Provide social AI features. You could enhance AI tools to facilitate teamwork by allowing multiple users to discuss, rate, or interact with the same dataset or to refine AI-generated suggestions collectively. This would not only foster teamwork but also ensure the inclusion of diverse perspectives in problem-solving.
Occasionally force the prioritization of deliberation over speed. For critical or complex tasks, intentionally slowing AI response times could nudge users toward consulting with colleagues. A slight delay could redirect users toward leveraging human collaboration, maintaining a balance between efficiency and shared problem-solving.
Cognitive Frameworks and Biases
For software designers and business analysts who are implementing AI solutions, knowing key cognitive frameworks and biases can improve decision-making and foster innovation. Here are some essential concepts to keep in mind:
Cognitive-load theory—This theory posits that humans have limited working memory and endeavor to minimize cognitive effort. Therefore, UX designers should design user interfaces that simplify low-value tasks, but encourage meaningful engagement in high-value tasks. [13]
Effort-reduction framework—This framework emphasizes people’s natural tendency to seek ways of reducing mental effort. UX designers can counter this tendency by building features that reward exploration and problem-solving. [14]
Heuristics and biases—These describe common decision-making shortcuts that can lead to errors such as over-trusting familiar patterns. To discourage shortcuts, implement feedback loops that prompt reflection and questioning. [15, 16, 17]
Cognitive offloading—This refers to the use of external tools to store and process information. To diminish cognitive offloading, encourage the balanced use of AI, encouraging users to interact and reflect rather than passively accept the AI’s outputs. [18]
Dunning-Kruger effect—This bias describes how people with limited knowledge often overestimate their competence. To counteract this bias, include self-assessment prompts and collaborative-feedback tools. [19]
Distributed cognition—This theory describes how cognitive processes are spread across people, tools, and an environment. It’s a way of thinking about cognition that extends beyond the individual brain. [20]
Situational awareness in dynamic systems—This theory focuses on people’s ability to perceive, comprehend, and anticipate changes in a constantly evolving environment, enabling them to make well-informed decisions in real time. It emphasizes the continuous updating of people’s understanding that is based on sensory input, prior knowledge, and situational context. [21] By integrating such frameworks into an AI system’s design and implementation phases, teams can create AI systems that support distributed cognition, foster transparency, and improve user understanding.
Conclusion
AI has the potential to enhance productivity and innovation across industries. However, we must not mistake an AI as an infallible expert. The illusion of expertise can lead to dangerous overconfidence in machine-generated insights, undermining both human agency and institutional integrity. By fostering transparency, accountability, collaboration, and human centricity—including the use of AI as a facilitator of distributed cognition and cross-functional engagement—we can harness AI’s strengths while mitigating its risks.
To fully realize AI’s promise, organizations must cultivate processes that prioritize real-world validation, collective ideation, and cultural openness to identifying knowledge gaps. By integrating robust assessments, fostering a culture of psychological safety, and encouraging collaborative ideation, organizations can mitigate risks at both ends of the equation—the inputs from users and the outputs from AI—ensuring that AI remains a tool for enhancement rather than a crutch that diminishes the human experience. Ultimately, the goal should not be to replace human expertise but to augment and develop expertise, ensuring that technology remains a servant to human progress rather than a deceptive master of knowledge that reduces human competence.
John Hayes. Chapter 7, “Cognitive Processes in Creativity.” In: John A. Glover, Royce R. Ronning, and Cecil R. Reynolds, eds. Handbook of Creativity. Boston: Springer, 1989.
Malcolm Gladwell. Outliers: The Story of Success. New York: Little Brown and Company, 2008.
Don Norman. The Design of Everyday Things, Revised and Expanded Edition. Basic Books, 2013.
Ruth Colvin Clark, Frank Nguyen, and John Sweller. Efficiency in Learning: Evidence-Based Guidelines to Manage Cognitive Load. San Francisco: Pfeiffer, 2005.
Daniel Kahneman. Thinking, Fast and Slow. Danvers, MA: Farrar, Straus and Giroux, 2011.
Jolie specializes in human-computer interactions, particularly UX design and research, with a foundation in human factors and systems engineering. Her career spans government, healthcare, and cybersecurity sectors, in which she has consistently translated complex user needs into easy-to-use, efficient systems. Jolie has applied machine learning (ML) to improve threat alerts in cybersecurity-operations centers, enhancing analysts’ decision-making and response times. In healthcare, she has explored probabilistic modeling using Veterans Health Administration (VHA) data to support clinical decision-making and surface actionable insights for medical teams. Plus, she has leveraged ML to align cybersecurity training courses with key knowledge, skills, and abilities (KSAs), optimizing career-path development. Currently, Jolie’s focus is on Customer Experience (CX), which is emerging as a transformative paradigm in the federal space, reshaping how agencies interact with the public and deliver services. By integrating human-centered design with AI capabilities, she aims to bridge the gap between technical innovation and user empowerment, ensuring that emerging technologies enhance rather than complicate user experiences. Read More