The Things AI Can’t Teach: The Value of Huamnity
Reference:
DeSchryver, M., Henriksen, D., Leahy, S., & Lindsay, S. (2024). Beyond automation: Intrinsically human aspects of creativity in the age of generative AI. Central Michigan University & Arizona State University.
Annotation:
In a world where GenAI is getting better at writing, designing, analyzing, and even “creating,” this article asks a surprisingly grounding question:
What parts of creativity are still fundamentally human and why should we care?
The authors argue that while AI can mimic creative output, it cannot replicate the experience of creativity. They highlight six intrinsically human creative capacities:
Curiosity
Intuition
Mindfulness/Patience
Imagination
Empathy
Embodied Thinking
Each of these capacities is shown to stem from lived experience, emotion, bodily awareness, and cultural or ethical context, things AI cannot meaningfully possess.
The article concludes with a bold call for education and training programs to prioritize these human strengths, especially as workplaces adopt more AI tools. What makes this article compelling for L&D practitioners is how clearly it demonstrates that the deepest forms of learning transfer rely on human senses and embodied cognition, not just content delivery.
Even in corporate e-learning or hybrid training, learners use their:
sense of movement
perception of space
emotional resonance
curiosity-driven discomfort
intuitive pattern recognition
empathetic social awareness
reflective stillness
These are not “nice to have” elements. They are the mechanisms through which information becomes memory, memory becomes understanding, and understanding becomes real-world behavior change. AI can support training, but it cannot replace these body-anchored processes.
The article’s strengths lie in its clear framework of six human creative traits, which provides educators with a practical structure for evaluating AI’s role in learning environments. It also connects theory to real educational practice, offering concrete implications for classrooms and instructional design.
The authors thoughtfully distinguish between AI’s ability to mimic creative outputs and the uniquely human experience of creativity, and they incorporate cultural and embodied perspectives that highlight AI’s current limitations. However, the article can be dense at times, relying heavily on academic theory, which may feel abstract for practitioners seeking immediate application. Its cultural analysis leans largely on Western research, leaving room for broader global insight, and while it acknowledges that AI may evolve toward more human-like traits, it stops short of fully exploring emerging areas such as embodied robotics and multimodal agentic systems.
The article does not explicitly frame creativity in terms of the body’s senses—but it could, and doing so makes the implications for learning transfer even more powerful.
Below is a reframing of the six traits through the lens of innate human sensory faculties, capacities AI cannot authentically replicate.
1. Curiosity → The Sense of “Cognitive Hunger”
Linked to dopamine systems, orientation reflexes, and the brain’s drive toward novelty.
In training, curiosity sparks attention — the first gateway to learning transfer.
2. Intuition → Gut Sense (Interoception) + Pattern Experience
Humans feel intuition physically: tightness, ease, resonance.
AI has no interoceptive system and no lived experiences to shape intuitive judgment.
3. Mindfulness/Patience → Temporal Sensory Awareness
Humans perceive time through emotional and physiological regulation.
Incubation, the moment when learning quietly consolidates, depends on embodied calm, not computational speed.
4. Imagination → Mental Imagery + Visuospatial Processing
When we imagine, sensory cortices light up as if we are seeing or hearing.
AI recombines text and image data but does not experience imagery.
5. Empathy → Emotional Resonance (Affective Sensing)
Humans detect microexpressions, tone, posture, and relational energy unconsciously.
AI can label emotions but cannot feel them or use them for moral discernment.
6. Embodied Thinking → The Entire Sensorimotor System
Creativity is deeply body-based: gesture, movement, rhythm, weight, balance.
These physical cues are essential for problem-solving, skill acquisition, and long-term memory encoding.
AI as the New Workplace Assistant—Promise, Limits, and Practical Realities
As organizations increasingly experiment with AI tools to answer employee questions, interpret policy documents, or guide internal procedures, it is tempting to see AI as a kind of universal workplace assistant: always available, endlessly patient, and capable of reducing administrative burden. But this week’s readings reminded me that using AI as a catch-all solution requires a far more grounded approach. Nemorin et al. (2023) highlight how AI is often surrounded by inflated promises, and this made me more cautious about positioning an AI assistant as a complete replacement for human judgment. Just the way that these AI bots take things so literally makes me think of the old Amelia Bedelia books!
If an internal AI tool provides incorrect information about procedures or compliance requirements, the consequences can be far more serious than a simple technology glitch. The authors also note that AI hype often conceals deeper issues related to privacy and surveillance, which pushed me to consider how internal search tools might inadvertently track or profile employees based on the questions they ask. This may inherently make the AI assistant bias as it collects information on the employee population and the types of questions they may be asking. Can you imagine an AI chatbot telling a high-performing employee they should just quit?
Similarly, Sofia et al. (2023) argue that AI is reshaping workforce expectations by creating constant demands for reskilling. This made me rethink the assumption that AI assistants automatically reduce workload; instead, employees need training to use these tools effectively and to understand their limitations, especially when the AI is interpreting policies or guiding procedural decisions. Their discussion on employee trust also resonated with me. Deploying AI internally is not just a technical decision, it is a cultural one. Employees are far more likely to rely on an AI assistant when the organization communicates clearly about how it works, what data it uses, and where human oversight still matters.
Touretzky et al. (2019) reinforce this human-centered approach by emphasizing the importance of AI literacy. Their argument that foundational AI understanding is essential made me realize that workplace AI assistants should not merely give answers but should support the development of employee judgment. When people understand how AI models process information, they become more discerning and less likely to accept outputs uncritically. The authors’ focus on ethical reasoning also shaped my thinking about internal AI tools. If an AI assistant is delivering guidance on workplace policies, the organization has a responsibility to ensure the system does so ethically, accurately, and in ways that support, not undermine, employee autonomy. Sometimes, this may expose initiatives in the organization such as a RIF (reduction in Force) inadvertently since AI tools don’t understand how to execute or properly incorporate the concept of timing in employee matters.
Overall, these readings helped me see AI assistants not as a replacement for employee work, but as a carefully governed support tool that requires human literacy, ethical design, and transparent communication. As I read my classmates’ reflections later this week, I’m curious how others are considering the balance between efficiency and responsibility in AI integration, and what they believe organizations owe employees when deploying such tools.
Applying Activity Theory to Transform Learning Impact
Reference:
Marroquín, E. M. (2025). Activity theory as framework for analysis of workplace learning in the context of technological change. Learning and Teaching: The International Journal of Higher Education in the Social Sciences, Elsevier.
https://doi.org/10.1016/j.later.2025.1000083
Annotation:
The rise of AI has happened faster than businesses and experts can adapt to the changes it has inevitably caused. Marroquín (2025) explores how Activity Theory can serve as a powerful framework for understanding how workplace learning evolves within technologically mediated environments. The author argues that as artificial intelligence and automation transform job functions, learning must be viewed not as a discrete event but as an integral part of the work activity system (comprising tools, rules, roles, community, and the object of work).
Rather than focusing on isolated training sessions, the study suggests that learning occurs through the contradictions and adaptations that arise as employees interact with new tools and changing structures. By examining these tensions, the article highlights how organizational learning can drive systemic transformation and measurable performance outcomes making this incredibly relevant to the field of organizational development.
Marroquin’s use of Activity Theory offers a rich, systems-level analysis that transcends traditional learning frameworks focused on individual cognition. The methodology draws on the framework’s core elements such as mediation, contradictions, and expansive learning which provides a structured yet flexible lens to analyze real-world complexity in workplace settings.
The strength of this article lies in its integration of theory and practice: it effectively links conceptual depth with practical implications for managing learning in AI-enabled environments. At Allegiant Professional Resources, our learning and development initiatives echo Marroquin’s perspective: learning is only valuable if it changes work outcomes. We’ve moved away from counting inputs such as “2 hours of training completed” or “5,000 skills tagged” and instead focus on impact measures, such as reduced error rates, faster cycle times, or improved decision accuracy after interventions.
Activity Theory helps us trace how those results occur by analyzing the full activity system like what tools employees use, which rules or norms guide their work, how their roles interact, and what the shared object of their activity is. When contradictions emerge (for example, when a new AI dashboard changes reporting workflows), we view them as learning opportunities rather than inefficiencies. Marroquín’s work reinforces our philosophy that training is not the outcome but instead - performance improvement is. It provides a theoretical foundation for measuring not activity, but transformation within the work system, a principle that continues to shape Allegiant’s evidence-based approach to organizational learning and impact measurement.
The Cost of Ineffective Employee Training
References:
Durgungoz, F.C., Durgungoz, A. “Interactive lessons are great, but too much is too much”: Hearing out neurodivergent students, Universal Design for Learning and the case for integrating more anonymous technology in higher education. High Educ (2025). https://doi.org/10.1007/s10734-024-01389-6
Kessler, R. C., Adler, L., Barkley, R., Biederman, J., Conners, C. K., Demler, O., … Walters, E. E. (2006). The prevalence and correlates of adult ADHD in the United States: Results from the National Comorbidity Survey Replication. The American Journal of Psychiatry, 163(4), 716-723. https://doi.org/10.1176/appi.ajp.163.4.716
Annotation:
Durgungoz’s, et al, study explores how technology-enhanced learning environments grounded in the Universal Design for Learning (UDL) framework can improve engagement for neurodivergent learners, including those with ADHD, while cautioning against overstimulation from excessive interactivity. Interestingly, the findings suggest that digital training programs are most effective when they provide flexibility, anonymity, and multiple ways to engage neurodivergent employees. The most effective programs allowed the employees to control pacing, choose preferred interaction modes, and reduce cognitive overload.
Why is this relevant to employee learning and development? According to Kessler, et al, (2006) the current adult ADHD prevalence at ~4–4.4%, and workplace studies show ADHD is associated with measurable reductions in job performance, higher absence and accident odds, and a quantifiable human-capital loss per affected worker (for example, a study of a large employer found ADHD workers averaged a 4–5% reduction in work performance and an estimated lost productivity value of roughly US$4,300 per affected worker per year).
The studies strength lies in combining both qualitative and quantitative approaches by collecting feedback from neurodivergent adults in higher education to assess emotional and cognitive engagement across different instructional formats. The researchers clearly outlines how UDL-driven technology design enhances inclusion by offering multiple means of engagement and representation, while also noting that excessive interactivity can overwhelm participants. The presentation is balanced, integrating participant voices with data analysis, and uses well-structured arguments supported by empirical findings. This approach strengthens its case for adapting UDL to corporate training by emphasizing flexibility, anonymity, and learner choice.
Allegiant Professional Resources’ mission to design corporate learning programs that genuinely enhance employee skillsets rather than simply deliver information makes the UDL approach a valuable tool in our repertoire. The study’s emphasis on UDL provides a research-based framework that supports our approach of tailoring training experiences to diverse cognitive styles and engagement preferences. Just as the article highlights the importance of balancing interaction with structure for neurodivergent adult learners, our team applies similar principles when developing corporate trainings, integrating technology that allows flexibility, pacing control, and choice in how learners engage with material.
This research reinforces the value of embedding inclusivity and intentional design into skill development programs, ensuring that each training we create is not only accessible but also effective in building lasting competencies that translate directly to workplace performance.
Additional References:
Kessler, R. C., Adler, L., Barkley, R., et al. (2006). The prevalence and correlates of adult ADHD in the United States: Results from the National Comorbidity Survey Replication. American Journal of Psychiatry, 163(4), 716-723. https://doi.org/10.1176/appi.ajp.163.4.716
Is TPACK framework effective for Executive Coaching?
Reference:
Harris, J., & Hofer, M. (2011). Technological pedagogical content knowledge (TPACK) in action: A descriptive study of secondary teachers’ curriculum-based, technology-related instructional planning. Journal of Research on Technology in Education, 43(3), 211–229. https://doi.org/10.1080/15391523.2011.10782570
Annotation:
Judith Harris and Mark Hofer (2011) examined how experienced teachers plan instruction that effectively integrates technology with content and pedagogy. Through qualitative interviews, unit analyses, and reflective journals, the researchers found that teachers’ use of technology became more conscious, strategic, and student-centered after participating in professional development focused on content-based “learning activity types.” Teachers began selecting technologies not for their novelty but for how well they served learning goals, demonstrating that effective integration requires understanding the nuanced “fit” between tools, content, and learners.
The study introduced a replicable framework for developing adaptive expertise through reflective planning and design thinking principles that extend far beyond education - hence why it is relevant to executive coaching!
For leadership and management consultants, Harris and Hofer’s study offers a powerful parallel to the challenges of coaching and organizational learning. Their methodology is deeply interpretive, reflective, and evidence-based which mirrors the way executive coaches guide leaders through awareness, experimentation, and refinement. By mapping how teachers evolve from “technocentric” to “learner-centric” thinking, the research models how leaders can move from “tool orientation” (e.g., implementing AI dashboards or engagement platforms) to behavioral fluency like integrating technology with strategy, culture, and context.
The study’s TPACK framework can be adapted for leadership enablement, where:
content = strategy
pedagogy = leadership approach
technology = tools
These facilitate decision-making and communication. HR professionals can use this lens to design more effective coaching interventions shifting from system implementation to skill integration, much like educators learned to shift focus from software features to meaningful outcomes.
Traditional Executive Coaching example:
Coach: “You’ve mentioned frustration with your team’s resistance to the analytics platform. What emotions come up for you when you see that resistance?”
COO: “It feels like they’re not moving fast enough, like they’re clinging to old ways.”
Coach: “What leadership behaviors could help model the adaptability you’d like to see?”
COO: “Maybe I could be more transparent about my learning curve too.”
Coach: “Excellent. Let’s develop a communication plan that frames your learning story and sets expectations.”
Result: The coach helps the leader become more self-aware, emotionally intelligent, and strategic in communication, but the technology integration challenge remains largely unaddressed.
TPACK Framework Executive Coaching Example:
Coach: “You’re leading a transformation that depends on your team’s ability to use data strategically. Let’s explore how your communication methods and tool use align with that goal.”
COO: “I’ve asked them to adopt the dashboard, but they still default to old reports.”
Coach: “That’s an example of a content-technology gap. What if we designed learning sessions that focus not just on using the tool but on interpreting data for strategic decisions? You could co-facilitate those sessions modeling the kind of data-driven thinking you expect.”
COO: “That makes sense. I can use our next operations meeting to walk through how I’m using the data for forecasting.”
Coach: “Exactly. That integrates the technology into your leadership pedagogy turning the tool into a platform for shared sense-making, not compliance.”
Result: The coaching moves from personal reflection to adaptive system design aligning how the leader teaches, communicates, and models behavior through the actual technology being adopted.
Coach’s Focus:
Technology = digital tools and data systems being implemented.
Pedagogy = the coaching approach or facilitation method (how the leader learns).
Content = the business strategy, goals, or leadership outcomes being developed.