GenAI Leadership Launchpad© — a learning framework focused on leadership competencies
GenAI Leadership Launchpad© is Copyright Greg Twemlow, Sept 8, 2024, Sydney, Australia.
How we engage with information has changed, and AI has upended traditional educational models like Bloom’s Taxonomy.
TL;DR
Bloom’s Taxonomy, introduced in 1956, is a hierarchical model that categorises cognitive skills into six levels, from basic remembering to advanced creating. It revolutionised education by providing a roadmap for teachers to develop curricula and assessments that promote higher-order thinking. However, with the advent of Generative AI (GenAI), Bloom’s Taxonomy is now less relevant. GenAI allows learners to bypass lower-level tasks like memorisation, enabling them to engage directly in higher-order thinking like analysis, evaluation, and creation. This shift calls for a new framework, a GenAI Leadership Launchpad©, emphasising Prompt Design, Experiential Learning, and the vital human skill of Persuasion. The GenAI Leadership Launchpad© serves as a dynamic, non-linear learning model for the future, where learners collaborate with AI in real-time, focusing on applying Knowledge rather than memorising it.
Introduction for Those Unfamiliar with Taxonomy
Education has to evolve rapidly and adapt to technological advancements like Generative AI (GenAI), changing how we think, learn, and work. One of the foundational concepts in education is Bloom’s Taxonomy — a framework introduced in 1956 that categorises different levels of learning, from basic skills like memorising facts to more complex tasks like creating new ideas. For decades, this taxonomy has helped teachers design lessons that encourage students to develop critical thinking and problem-solving skills in a structured way.
But here’s the catch: how we engage with information changed forever in late 2022, and AI has upended the traditional model. We no longer need to start with rote memorisation before moving on to complex thinking. With tools like ChatGPT and other AI systems, learners jump right into solving problems and creating solutions by using AI to fill in the gaps.
Introducing the GenAI Leadership Launchpad© Concept
- GenAI Leadership Launchpad©, designed by Greg Twemlow, is a learning framework that focuses on developing leadership competencies in the age of AI. It takes learners from foundational skills, such as Prompt Design, through experiential learning to apex skills like Leadership and Persuasion.
- The model emphasises how AI can accelerate learning and decision-making processes, helping individuals quickly build the skills they need to lead in a world shaped by AI technologies.
- With AI as a collaborator, the Leadership Launchpad equips individuals to navigate complex environments, make strategic decisions, and, most importantly, lead and influence others effectively.
This new approach calls for a rethink of Bloom’s original hierarchy, where the concept of a GenAI Leadership Launchpad© comes in. This new model reflects how learners interact with AI today — focusing on skills like prompt design, creative problem-solving, and the critical human ability to persuade and communicate ideas effectively.
In this new learning paradigm, AI is a powerful partner in the cognitive process, helping learners experiment, apply Knowledge, and develop the skills needed to succeed in a world where information is at your fingertips, but the confidence and ability to make sense of it and communicate it well is more important than ever.
Bloom’s Taxonomy Is No Longer Relevant — Here’s What Comes Next
In 1956, Benjamin Bloom introduced Bloom’s Taxonomy, a framework that became the backbone of educational practice for decades. Bloom’s hierarchical model of cognitive development organised learning into six levels, beginning with Knowledge (later revised to Remembering) and culminating in Synthesis (later revised to Creating). Bloom’s Taxonomy revolutionised education by providing a roadmap for how learners should progress through increasingly complex cognitive tasks. (See Appendix: How Bloom’s Taxonomy Was Received by the Education Industry).
The influence of Bloom’s Taxonomy extended beyond classroom lesson plans to teacher training, assessment design, and professional development. It provided a structured method for advancing students from basic memorisation to higher-order thinking skills, such as evaluation and creation. Yet, its rigid hierarchy, requiring that learners first master lower-order skills like recall before advancing to analysis and creation, became a limitation as education evolved — particularly with the rise of Generative AI (GenAI).
Bloom’s Model vs. GenAI: An Entirely New Learning Paradigm
With the advent of GenAI, Bloom’s hierarchical structure no longer holds the same relevance. The core assumption of Bloom’s model — that learners must sequentially master cognitive skills — falls apart in a world where AI democratises access to Knowledge. Today’s learners are empowered to bypass rote memorisation entirely and jump directly to higher-order tasks like critical thinking and creation by utilising AI as a cognitive collaborator. This shift calls for a new taxonomy that reflects learners’ interactions with AI in a non-linear, real-time manner.
GenAI Enables Non-Linear Learning
GenAI fundamentally changes how learners engage with information. Tools like ChatGPT and Wolfram Alpha liberate learners from the shackles of rote memorisation, providing instantaneous access to vast data. A student no longer must memorise dates before analysing historical trends — AI provides the context in real-time, freeing the learner to focus on higher-order thinking like analysis and evaluation.
For example, a student working on a business plan might use AI to instantly generate creative marketing strategies while simultaneously evaluating their financial feasibility. This fluidity of cognitive tasks — moving between creation, evaluation, and application — mirrors real-world problem-solving and bypasses the need for a rigid, step-by-step learning process.
The Fluidity of Cognitive Tasks
In Bloom’s original model, cognitive processes are arranged hierarchically, implying that learners must conquer lower-order tasks before moving on to more complex ones. GenAI disrupts this paradigm by enabling a non-linear, iterative approach to learning. With AI’s assistance, a learner can simultaneously create, analyse, and evaluate, effectively blurring the boundaries between different levels of cognitive skills.
This is where Prompt Design emerges as a crucial skill. Learners must master formulating practical questions and inputs for AI to produce valuable outputs. As they experiment with prompts, reflect on AI responses, and refine their questions, they engage in an experiential learning process that skips over rote memorisation and instead focuses on higher-order cognitive tasks from the outset.
AI as a Cognitive Collaborator
AI’s role in education has evolved beyond information retrieval to that of a cognitive partner, co-creating solutions with learners. This collaborative relationship challenges the hierarchical nature of Bloom’s Taxonomy, which was developed in an era when knowledge acquisition was a solitary, human-driven process. Today, AI performs lower-order tasks, freeing learners to focus on higher-order thinking skills.
This partnership challenges the assumption that creation is the final step in learning. With AI, learners can begin with creative tasks — such as generating business models or drafting essays — and use AI feedback to evaluate and refine their work in real-time.
This back-and-forth interaction between AI and human cognition renders the hierarchical nature of Bloom’s Taxonomy obsolete.
Context Over Sequence
Bloom’s Taxonomy relies on a linear progression through cognitive tasks, but GenAI supports a context-driven approach to learning. Instead of memorising foundational information, learners can use AI to provide context on demand, allowing them to engage in problem-solving and creative Synthesis from the beginning. This shift acknowledges that real-world problem-solving is rarely a sequential process — it requires flexibility, adaptability, and the ability to switch between cognitive tasks fluidly.
The Case for a GenAI Leadership Launchpad©
In the GenAI era, the limitations of Bloom’s Taxonomy reveal a need for a new cognitive framework that reflects the non-linear and experiential nature of learning with AI. The GenAI Leadership Launchpad© prioritises real-time collaboration, fluidity between cognitive processes, and the centrality of Prompt Design as a foundational skill. Here’s what this new taxonomy could look like:
Prompt Comprehension and Design:
- The ability to craft effective prompts for AI is the foundational skill in this taxonomy. Learners must understand how to ask questions and frame inputs to maximise AI’s utility. This skill requires ongoing experimentation, reflection, and refinement.
Experiential Learning through AI Interaction:
- Learners engage in hands-on experimentation with AI, refining prompts and analysing outputs. Learning becomes a dynamic feedback loop where learners can move fluidly between cognitive tasks like analysis, evaluation, and creation.
Application and Analysis in Real-Time:
- Learners apply AI-generated outputs in real-world contexts and analyse the results. AI takes on lower-order tasks like information retrieval, allowing learners to focus on problem-solving and critical thinking.
Dynamic Evaluation and Creation:
- In this taxonomy, evaluation and creation are no longer final steps — they happen concurrently with other cognitive tasks. Learners evaluate AI outputs, refine their ideas, and co-create solutions with AI, engaging in an ongoing learning cycle.
The Role of Persuasion in the GenAI Leadership Launchpad©
Persuasion is an essential skill that remains overlooked in traditional education and AI-enhanced learning models. Presenting ideas effectively—convincing others of their value and clearly articulating complex concepts—is a higher-order cognitive skill crucial to making AI-generated Knowledge actionable.
Persuasion as a Cognitive Skill
Presenting and persuading go beyond understanding and recalling information and involve synthesising data, engaging with an audience emotionally and intellectually, and adapting communication styles to convey ideas effectively. Traditional frameworks like Bloom’s Taxonomy do not adequately address this blend of cognitive, emotional, and social intelligence.
In the GenAI Leadership Launchpad©, persuasion plays a central role. Learners must create and analyse ideas using AI and present and defend those ideas to others. With the ability to translate AI-generated Knowledge into persuasive, actionable insights, much of AI’s power will be tapped.
Presentations as Demonstrations of Understanding
Incorporating presentations into the learning process positions persuasion as a demonstration of Knowledge. After using AI tools to create and evaluate, learners must present their findings, making the case for their conclusions. This skill becomes particularly relevant in business, academia, or public policy, where success depends on having the correct answers and convincing others of their value.
Persuasion as an Overarching Skill
While persuasion fits into a specific cognitive process stage (such as evaluation or creation), it could also be viewed as an overarching skill that connects all taxonomy elements. Whether crafting prompts, evaluating AI-generated content, or applying solutions in real-world contexts, the ability to persuade ensures that human-AI collaborations lead to real-world impact.
GenAI Leadership Launchpad as a Pedagogical Model
This model is undoubtedly a pedagogical framework that aligns with contemporary educational trends, particularly in its focus on Prompt Design, experiential learning, and persuasion as vital skills for the future. As a pedagogical model, it introduces structure to a non-linear learning process that emphasises real-time interaction between human cognition and AI. By encouraging learners to design effective prompts, experiment with AI-generated outputs, and refine their understanding through iterative feedback, the GenAI Leadership Launchpad© fosters a more dynamic, adaptive approach to learning.
Integrating persuasion into the taxonomy also addresses a crucial gap in traditional education — recognising that communicating and defending AI-generated insights is just as important as generating those insights in the first place. In an increasingly AI-driven world, where collaboration with intelligent systems is becoming the norm, the ability to present and persuade will determine the success of both individuals and organisations.
Moreover, this model reflects a broader shift in education, where experiential learning and real-time feedback loops are becoming central to teaching methodologies. Like other contemporary pedagogical frameworks that emphasise hands-on learning and real-world application, the GenAI Leadership Launchpad empowers learners to take control of their education, using AI for creative problem-solving, critical analysis, and effective communication. This taxonomy is not just a revision of Bloom’s work — it’s a forward-looking pedagogical model that prepares learners for the complex, AI-enhanced future.
Toward a New GenAI-Powered Learning Paradigm
Bloom’s Taxonomy, while influential, is no longer suited for the dynamic learning environments enabled by GenAI. The rise of AI transforms how we engage with Knowledge, making real-time access, collaborative creation, and fluid cognitive processes the new standard. A GenAI Leadership Launchpad© — emphasising prompt design, experiential learning, and real-time application — better reflects this reality.
Crucially, this new taxonomy must also recognise the overarching importance of persuasion. The ability to present and defend AI-generated insights bridges the gap between AI-driven Knowledge and human understanding. By integrating persuasion into the GenAI Leadership Launchpad©, we ensure learners generate Knowledge and make it actionable and meaningful in the real world.
In this new learning paradigm, GenAI and human intelligence work together as co-creators of Knowledge, partners in Persuasion, and agents of change.
Appendix: How the Education Industry received Bloom’s Taxonomy
Bloom’s Taxonomy was introduced in 1956 as part of Benjamin Bloom’s broader effort to systematise educational objectives and improve curriculum design. From its inception, the taxonomy was embraced by educators and quickly became one of the most influential frameworks for categorising and assessing cognitive development in students. Here’s an overview of how the education industry received and applied Bloom’s Taxonomy:
Widespread Adoption of Curriculum Design and Assessment
Bloom’s Taxonomy was rapidly adopted across various levels of education, from K-12 schools to higher education institutions. Its hierarchical structure of cognitive skills, from basic knowledge recall to the more advanced skills of evaluation and creation, provided a clear framework for lesson planning, curriculum development, and assessment. Educators valued the taxonomy because it helped:
- Clarify learning objectives: Teachers could set goals for their students based on different levels of cognitive achievement, ensuring that lessons progressed from foundational Knowledge to higher-order thinking.
- Structure assessments: By aligning exam questions and assignments with specific cognitive levels, educators could assess not only what students knew but also how well they could apply, analyse, and evaluate that Knowledge.
- Design comprehensive curricula: Schools could develop multi-year curricula that advance students through the stages of cognitive development outlined in Bloom’s Taxonomy.
Impact on Teacher Training and Professional Development
Teacher training programs and professional development courses quickly integrated Bloom’s Taxonomy into their instruction. Aspiring teachers were trained to use the taxonomy to:
- Design lesson plans that would challenge students at various cognitive levels.
- Create differentiated learning opportunities so that students at different skill levels can engage with the material in a way that is suited to their current cognitive development.
- Craft formative and summative assessments that ensured students memorised facts and developed critical thinking skills.
The taxonomy also provided a common language for educators, helping to streamline discussions about learning objectives and student progress across disciplines. In this sense, Bloom’s Taxonomy standardised education discourse, making it easier for teachers, administrators, and curriculum designers to collaborate on the design and delivery of education.
Application in Standardised Testing
The adoption of Bloom’s Taxonomy also influenced the design of standardised tests, particularly in the United States. Test creators used the taxonomy’s structure to ensure that questions covered a range of cognitive levels, from basic knowledge recall to more complex tasks like analysis and problem-solving. The taxonomy’s influence on standardised testing shaped how educators taught and assessed students, encouraging schools to focus on higher-order thinking in preparation for national assessments.
Adaptation Across Disciplines
One of the reasons for Bloom’s Taxonomy’s widespread acceptance was its flexibility across disciplines. The taxonomy could be adapted to structure learning in virtually any subject area, whether used in math, science, literature, or social studies. Educators appreciated its ability to:
- Standardised learning goals: The taxonomy’s universality allowed it to be applied in multiple contexts, allowing cross-curricular coherence.
- Provide a roadmap for student progression: Students could steadily build the skills needed to engage with complex material across various subjects by progressing through the taxonomy’s levels.
Later Critiques and Revisions
Despite its initial widespread adoption, Bloom’s Taxonomy was not without its critiques. Over time, some educators and scholars began to question the rigidity of the hierarchical model. Critics argued that:
- Cognitive tasks are sometimes hierarchical: Some educators noted that cognitive processes like analysis and evaluation often happen simultaneously rather than in a strict progression. They questioned whether higher-order thinking necessarily depended on mastering lower-order skills first.
- Focus on cognitive skills alone: The original taxonomy primarily focused on cognitive skills, which led some educators to argue that it overlooked the emotional and social aspects of learning.
These critiques led to a 2001 revision of Bloom’s Taxonomy by Bloom’s former students, including Lorin Anderson and David Krathwohl. The revised taxonomy shifted from focusing on nouns (e.g., knowledge, evaluation) to verbs (e.g., remembering, analysing, creating), emphasising active cognitive processes. It also reordered some stages, placing “Creating” as the highest form of thinking, reflecting the importance of innovation and problem-solving in modern education.
Ongoing Legacy
Despite some criticisms and the rise of new educational models, Bloom’s Taxonomy has retained its influence in education. Today, it continues to be:
- A cornerstone of educational psychology and curriculum development.
- A foundational framework in teacher education programs worldwide.
- A widely recognised tool for designing assessments and evaluations that ensure students engage with material at various levels of complexity.
In many ways, Bloom’s Taxonomy revolutionised how educators think about learning, making it easier to categorise and scaffold cognitive skills while providing a consistent framework for student assessment. Even as newer models, like those that integrate AI and experiential learning, evolve to address the needs of modern education, Bloom’s Taxonomy remains a seminal framework that helped shape 20th- and 21st-century teaching methodologies.
About the author: Greg Twemlow, Founder of XperientialAI©.
Greg Twemlow: Sharing what I’ve learned from my career of 35 years as a citizen of the world, parent, corporate executive, entrepreneur, and CEO of XperientialAI, focused on experiential learning for maximum impact with AI. Contact Greg: greg@xperiential.ai
