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.
Try using AI Personalized Podcasts to Drive Retention & Employee Development
Reference:
Do, T. D., Bin Shafqat, U., Ling, E., & Sarda, N. (2024). PAIGE: Examining learning outcomes and experiences with personalized AI-generated educational podcasts (arXiv preprint arXiv:2409.04645). https://doi.org/10.48550/arXiv.2409.04645
Annotation:
The researcher take a deep dive into how generative AI can convert textbook chapters into personalized educational podcasts for a group of 180 college students. The researchers compared traditional textbook reading with both generalized and personalized AI-generated podcasts across multiple subject areas. Their findings showed that students overwhelmingly preferred podcasts to reading, and that personalized podcasts tailored to learners’ backgrounds and interests improved comprehension in several disciplines.
The takeaway is clear: AI-driven, personalized audio content can enhance learning engagement and outcomes when designed with relevance and learner context in mind.
The study’s methodology, integrating AI-driven podcast generation with validated user experience measures, models exactly the kind of data-informed experimentation L&D professionals can use to evaluate their own digital learning tools. It also underscores the importance of delivery design, such as the conversational tone, pacing, and modality that can have a deep influence in learner motivation. Consultants working with clients on upskilling strategies can take from this that AI isn’t just a content generator; it’s an adaptive facilitator that can align learning experiences to individual needs and organizational culture.
At Allegiant, our consulting work centers on helping organizations create inclusive learning environments that make workplace learning more effective for all employees, particularly those whose neurodivergence offers unique cognitive strengths. Studies like this one inform how we think about designing micro-learning and leadership development content that doesn’t just “teach,” but connects meaningfully with how diverse minds engage with information.
We also see a connection between this research and how business leaders who host industry podcasts can influence engagement and retention. A 2023 LinkedIn Workplace Learning Report found that employees who feel connected to their organization’s thought leadership (through podcasts or leadership-led storytelling) are 33% more likely to stay with the company. Integrating AI-generated podcasts or internal learning channels can give employees that same sense of inclusion and relevance.
As our research and consulting practice evolves, we’re exploring how personalization, audio learning, and neurodivergent engagement strategies can converge to make corporate learning both equitable and deeply human.
Using Storytelling and AI Podcasts to Unlock the Power of Neurodiverse Learning
Reference:
Hung, C.-M., Hwang, G.-J., & Huang, I. (2012). A Project-based Digital Storytelling Approach for Improving Students' Learning Motivation, Problem-Solving Competence and Learning Achievement. Educational Technology & Society, 15(4), 368–379.
Annotation:
Hung, Hwang, and Huang (2012) explore how blending project-based learning (PBL) with digital storytelling (DST) can transform students’ engagement and performance in science education. Conducted with 117 fifth-grade students in Taiwan, the study found that students who learned through digital storytelling exhibited significantly higher learning motivation, problem-solving competence, and academic achievement than those who participated in traditional project-based instruction. The research demonstrated that combining structured inquiry with creative expression enhances both comprehension and emotional connection to learning.
What makes the Hung study particularly compelling is its methodical approach. The quasi-experimental design, with both pre- and post-tests, allowed for robust comparisons between groups and yielded quantifiable evidence of learning gains. The use of validated scales for measuring motivation and problem-solving competence strengthened reliability, while the incorporation of student interviews added valuable qualitative depth.
While Hung et al. grounded their study in the K–12 context, the implications extend naturally to adult workplace learning, particularly in environments striving to leverage neurodiverse talent. The 2024 study “PAIGE: Examining Learning Outcomes and Experiences with Personalized AI-Generated Educational Podcasts” (Do, Shafqat, Ling, & Sarda) complements Hung’s findings by showing how AI-generated podcasts can personalize learning experiences, improving retention and motivation among adult learners. Together, these studies underscore a key insight for organizations: personalization and storytelling are powerful equalizers in learning.
For neurodivergent professionals, who often think visually, narratively, or auditorily, these project-based tools for storytelling or adaptive podcasts can transform potential “differences” into competitive strengths. At our firm, we help organizations design inclusive learning ecosystems that combine these principles: using narrative frameworks to engage emotion and AI to tailor pacing, modality, and delivery to individual cognitive profiles. The next frontier of workplace learning isn’t just digital — it’s deeply human, driven by empathy, adaptability, and design thinking that turns neurodiversity into innovation.