Executive Summary

Unlocking the Future with Bloom’s Taxonomy

In a world where generative AI promises to transform industries, EdTech faces a pivotal moment, where it can leverage the foundational principles of educational psychology to deliver on its breakthrough potential of upskilling learners and usher in transformative learning experiences.

The key lies in reconnecting today’s proven learning science-based tools with foundational learning theories like Bloom’s Taxonomy, which offers a roadmap for moving learners from basic knowledge acquisition to higher-order skills like critical thinking and creativity.

Too often, EdTech focuses on scalability at the expense of depth, emphasizing memorization over meaningful engagement. Yet, modern AI platforms can invert this trend, enabling adaptive, personalized pathways that reflect Bloom’s vision for mastery and 1:1 learning. By leveraging the full spectrum of Bloom’s hierarchy, EdTech can enhance learner outcomes, address disengagement, and improve the relevance of workforce training.

Grounded in decades of educational research, this paper offers a framework for integrating classic theories with cutting-edge tools. For leaders shaping the future of learning, it outlines strategies to transform training systems into dynamic, learner-centric platforms that not only engage but empower.

If your goal is to align state-of-the-art Agentic AI technology with learning science to achieve transformative upskilling outcomes, this article provides the blueprint.

Introduction: From Ed-Theories to EdTech Theories

Fifty years ago, during the Apollo 13 mission, the phrase “Houston, we have a problem” captured the world’s attention, symbolizing the intersection of human ingenuity and technological challenge. Today, EdTech—educational technology—faces its own pressing issue: a disconnect between rapid technological advancement and the application of robust learning theories to guide its evolution. Despite a century of progress in educational psychology and instructional design, many foundational theories, such as Bloom’s Taxonomy, have the opportunity to be revised in the digital learning landscape. In an era shaped by generative AI, the stakes are higher than ever. EdTech now has a unique opportunity to align its tools with proven theories to achieve greater efficiency and impact. This article examines how Bloom’s Taxonomy, a cornerstone of educational framework, can have innovations into modern online learning platforms, exploring its potential to reshape how educators teach and learners thrive in the GenAI digital age.

In the early 20th century, educational theories began to shift away from passive, teacher-centered methods to more student-centered approaches, emphasizing active engagement and critical thinking. For instance, John Dewey, a pioneer of progressive education, advocated for experiential learning, where students learn best through active participation and real-world experiences. Dewey’s theories laid the groundwork for constructivist approaches, which posit that learners construct knowledge through their own experiences rather than passively receiving information. This perspective was further developed by scholars like Jean Piaget and Lev Vygotsky, who highlighted the role of developmental stages and social interaction in the learning process, respectively. Piaget’s theory of cognitive development focused on how learners move through stages of intellectual growth, while Vygotsky introduced the concept of the “zone of proximal development,” emphasizing the importance of social context and collaborative learning.

Moving from these early theories to the mid 1900s, one of the most significant frameworks from this period is Bloom’s Taxonomy, developed by Benjamin Bloom in 1956. This taxonomy was initially created to provide a structured way of categorizing educational objectives, focusing on promoting higher forms of thinking beyond mere rote memorization. It combined the best from the three frameworks above and applied them in progression; making sure that the content and experiences match the developmental level and stage of the learner. Since its introduction, this framework has evolved to fit new educational contexts and technologies. In this we will explore the evolution of Bloom’s Taxonomy in the Generative AI era and how new Educational Technologies can enhance the adult learner’s capabilities.

Overview of Bloom’s Taxonomy

Bloom’s Taxonomy is a model that organizes learning objectives into levels of complexity and specificity. In Bloom’s Taxonomy you will recognize the elements of many of the original educational theories. Bloom did something overlooked in today’s applications of educational theory in EdTech. He knew that these theories apply to individual learners at appropriate times. That timing references what we learned from Vygotsky. As learners progress through the taxonomy, they get more social and experiential. They take on more responsibility and subsequently more autonomy. This aligns with Piaget and Dewey.

Fig 1. Bloom’s Taxonomy (revised in 2001) with lower-order and higher order thinking skills. Initially the model started at the base foundation and the goal to higher skilling is moving up to higher order.

Bloom’s hierarchy organizes cognitive processes into six levels: knowledge, comprehension, application, analysis, synthesis, and evaluation, which was later revised to remember, understand, apply, analyze, evaluate, and create. The taxonomy emphasizes moving from basic recall of information to more complex, abstract forms of thinking, encouraging educators to design curricula that challenge students to apply, analyze, and create rather than simply memorize facts. The learning outcomes at the focus of the taxonomy are what the learners are expected to learn either as a result of a specific lesson or the entire course. To organize and assist with meeting these outcomes and to be effective, the taxonomy requires that the taxonomy address three learning domains. Learning programs and experiences that fall on any level of the taxonomy should include these three areas:

Cognitive: Knowledge or thinking
Affective: Growth in feelings or emotional areas – value, appreciation
Psychomotor: Manual or physical skills

When looking at the shortfalls and challenges of current training programs at any level, you can begin to appreciate the value of these domains. Bloom is suggesting that every experience should require the learner to remember and recall information, appreciate and value that information, and physically apply that knowledge in various situations.

Bloom’s taxonomy can be used as a checklist to ensure that all levels of a domain have been assessed and align assessment methods with the appropriate lessons and methodologies.

Within the taxonomy, each level of learning is considered hierarchical, meaning each level builds on the previous one. These levels are commonly classified into groups, higher level and lower level. See Fig 1.

Lower-level skills (e.g., memorizing factual knowledge) can be developed before higher-level skills are introduced (e.g., analysis of relationships). Bloom’s taxonomy offers a guiding framework for breaking down the expectations of learning programs into accessible parts which can be used to direct learning experiences. Different levels require different instructional delivery methods; they also require different assessment methods. This in turn improves consistency between assessment methods, content, and instructional materials and helps the leadership to identify weak areas.

As stated earlier, each level guides learning outcomes. To better explain this, many people add verbs that refer to the actual behavior a learner would demonstrate at each level. These verbs must be observable, which inherently makes them measurable. Fig 2 is a visualization of the levels of blooms, the verbs that guide the practical application of each level, and the discrepancy between higher and lower-level thinking.

FIG 2. BLOOM’S TAXONOMY VERBS USED TO HELP CHARACTERIZE THOUGHT EXERCISES AT EACH LEVEL. https://www.valamis.com/hub/blooms-taxonomy

To take the taxonomy further, in 2001, a team of researchers revised the taxonomy. Originally, Bloom’s taxonomy was one-dimensional with an exclusive focus on the knowledge domain. The current version developed by Anderson and Krathwohl (2001) reorganizes, and highlights the interactions between, two dimensions: cognitive processes and knowledge content. Anderson and Krathwohl identify two reasons for updating the original handbook. They emphasize a refocusing of educational outcomes back to the original handbook, which was ahead of its time and can still offer assistance to modern educators and to incorporate new findings in psychology and education into the framework. In their revision, cognitive processes are presented as verbs and the knowledge content are presented as nouns. Along with exchanging the levels of Evaluation and Synthesis (which they rename to Creation), Anderson and Krathwohl redefine the knowledge dimension to include four types:

Factual Knowledge: Basic elements of a discipline that a student must know and be able to work with to solve problems including basic terminology and specific details and elements.
Conceptual Knowledge: Interrelationships between basic factual knowledge that demonstrate how elements work together, for example, classifications and categories, principles and generalizations, and theories, models, and structures.
Procedural Knowledge: How something is done including the methods of inquiry, skills, algorithms, techniques, and methods needed to investigate, apply, or analyze information.
Metacognitive Knowledge: Awareness and knowledge of one’s own cognition including strategies for learning, contextual and conditional knowledge about cognitive tasks, and self-knowledge.

Integrating these thoughts, here is a diagram combining all that encompasses all of the effects and potential of Bloom’s Taxonomy:

Fig 3. This 3D view of Blooms resembles a 3D chess board, the goal to move up to higher levels of Blooms. https://iastate.app.box.com/s/z0otio95lflaii1l2ro3h42kp8q6fdmm

This diagram depicts the two dimensions, the levels, and the verbs. What is great about this diagram is that it depicts the complexity of the taxonomy. This is what many companies or educational institutions take for granted when they implement the taxonomy. There are many variables, many versions, and many degrees of possibilities. You may recognize the similarity of that diagram with a 3-D chess game board. 3-D chess, as you can imagine simply by the name, requires new rules to be applied in unpredictable situations and is a complex and dynamic version of standard chess. Players and pieces are constantly moving between various levels and dimensions. This is exactly what Bloom’s Taxonomy can be to regular education and to EdTech. But ever since he designed his taxonomy, Bloom always wondered if it was good enough. If it was working. He always wondered what methodology was the best, and if he could create a scalable learning model to match it. He eventually got his answer.

Bloom’s Two Sigma Objective | The gold standard

In 1984, Bloom wrote a paper based on the dissertation results of University of Chicago PhD students Joanne Anania and Joseph Arthur Burke. In their paper, they found that students who were tutored in a 1:1 setting had the highest levels of achievement compared to a control class of typical teaching methods. As quoted by Bloom: “the average tutored student was above 98% of the students in the control class”. Additionally, “about 90% of the tutored students attained the level of summative achievement reached by only the highest 20%” of the control class. See the curve on the right side in Fig 4. Bloom wrote in his paper a challenge to see what other educational experiences would have the same results as 1:1 tutoring.

He called this challenge, the 2-Sigma Problem. In typical education settings, instructors strive for a perfect Bell Curve. As depicted in the left curve in Fig 4, an effective learning experience according to research has the same number of people on either side of the mean. This curve represents the statistical distribution of learners’ scores. Within this curve, there are deviations, or lines that represent percentages of individuals who are evenly distributed away from the average/mean score. So, for example, in a Bell Curve, 68% of the values fall within one standard deviation, 95% within two, and 99.7% within three standard deviations from the mean. This is known as the Empirical Rule. According to the findings in Anania and Burke’s research, the students who were involved in 1:1 tutoring all fell within the upper 2-standard deviations. Another word for 2-standard deviations, is 2-Sigma.

Mastery Learning

In order to shift his processing toward the gold standard of 1:1 tutoring, Bloom adopted what’s known as Mastery Learning. Most instructors were happy with the Bell Curve. But not Bloom. He wondered why anyone would be satisfied if half of their students failed, or only half of them were successful. He wanted a learning strategy to shift this curve to the right, toward 1:1 tutoring. See the middle curve in Figure 4. He used his experience with prior learning theories and developed guidelines for how to reach this level of mastery learning. He realized that giving multiple assessments, combined with instructor feedback, and multi-tiered experiences based on his taxonomy were the solution. Since then, the list has been refined, so here are the latest guidelines for Mastery Learning:

Pre-assessments
Group-based initial instruction
Regular formative assessments
Corrective instruction,
Parallel formative assessments
Enrichment activities. Baseline, or diagnostic testing

Fig 4 depicts the relationship between the normal curve, the mastery curve, and 1:1 tutoring the “gold standard”

The challenge with Mastery Learning in a typical educational setting is the reason why education is currently less effective than it could be: Mastery-based instruction, while effective, has been seen to be exhausting, since it requires more effort both from the teachers and the students. Aside from this, the model requires a large amount of time to ensure that all students deliver mastery of the topic. Students progress at individual rates. Educational resources, particularly including training time, and teacher’s attention are also denied to strong learners and bestowed on weak ones (Sajadi et al., 2015). Many researchers and practitioners have not had the tools or technology to overcome some of these challenges. In response, some have looked at different ways to apply Bloom’s. One alternative iteration is to invert the taxonomy, or start at higher order thinking, and work toward lower order.

Inverting Bloom’s Taxonomy: Starting at the top

Invert, inverse, reverse, flipped…they all mean the same thing. Looking at Bloom’s taxonomy from the opposite direction. This process has become more and more popular and proven over the years. The critical takeaway is what is referred to as a pre-test. In normal settings, teacher’s pre-test students, typically with a low-order thinking assessment like multiple choice, to measure the student’s aptitude. Then, the instructor typically uses that data to create learning programs based on what students need. When we invert the taxonomy, we essentially do the same thing. The only difference is that instead of a lower-order multiple choice, recall information type of pre-test, flipping the taxonomy means having learners complete a learning activity based on higher-order thinking.

Fig 5 Visualizing Bloom’s Taxonomy in GenAI Age, the core concepts of upper-level and lower-level blooms with AI enabling more higher-level 1:1 Interactivity and the ability to place learners at higher levels and dynamically assess learner skill, rapidly adjust to envision a faster path to mastery.

See Figure: 5. For example, early in the educational process, students are asked to create original works of art and apply their current knowledge to a higher-order task. Based on the results of that task, lower-level learning skills are identified and subsequent activities are presented. Recent research suggests that focusing on higher-order thinking first—such as creativity, analysis, and application—can enhance overall learning outcomes (Domínguez-González et al., 2023; Saliklis et al., 2009). And based on what we know about learning theories, this makes perfect sense. The learning is tailored to the individual, the learner recognizes the shortcomings, sees how new activities are connected to training those skills, and is motivated to complete the training process.

The Evolution of Corporate Training: From Efficiency to Meaningful Engagement Era

Corporate training began to take shape in the mid-20th century, rooted in industrial-era ideals of scalability and efficiency. By the 1960s and 70s, structured classroom-based programs emphasized onboarding and management development, targeting foundational levels of Bloom’s Taxonomy such as knowledge and comprehension. These approaches prioritized the transfer of facts and procedures but neglected higher-order thinking skills like analysis and synthesis.

The 1980s and 90s saw the emergence of Computer-Based Training (CBT), offering self-paced learning on personal computers but continuing to focus on rote memorization and basic application. Companies like General Electric pioneered internal “universities,” combining in-person and digital training while remaining anchored in structured, scalable models. The 1990s revolutionized training with e-learning platforms, and by the 2000s, Learning Management Systems (LMS) streamlined content delivery and tracking. However, most LMS implementations failed to progress beyond basic learning objectives, ideal for structured training and mandatory courses or compliance training, and leaving advanced skills like Bloom’s level “evaluation and creation” to human facilitation through role-playing and human board reviews and certifications. Learning Experience Platforms (LXP) emerged as a learning library with the goal to offer more for the self-directed learner choosing interest resulting in a customized pathway.

Thought Checkpoint: Assume you are considering learning to swim, the lower-level blooms would be reading a book on swimming, memorizing the terms and techniques. In a “remember – understand” test you perform excellent. In the Bloom’s analogy, upper-level blooms involve the real hands on “creating”, or in this case, performing the swimming exercise in the water and progressing to swimming in the ocean, learning by doing. Does a lower-level blooms assessment accurately qualify you for an ocean swim?

The 2010s introduced AI and adaptive learning tools, promising personalization and efficiency. Yet, many implementations prioritized scale over depth, delivering superficial training to large employee populations quickly and cheaply. This relentless focus on quantity undermined training quality, leaving employees disengaged and disconnected from the learning process. Ineffective programs contributed to high turnover rates, with companies caught in a costly cycle of rapid onboarding, disengagement, and retraining.

Gamification and flashy interfaces have attempted to address these issues but often fail to challenge employees to think critically, collaborate, or innovate. Training systems based on outdated interpretations of Bloom’s Taxonomy continue to prioritize cost savings over fostering meaningful learning experiences. This approach erodes the value of corporate training, perpetuating inefficiency and dissatisfaction.

To break this cycle, companies must shift from one-size-fits-all solutions to adaptive models that span Bloom’s full taxonomy. While foundational skills remain essential for specific contexts like compliance and safety, modern workplaces demand higher-order thinking, creativity, and adaptability. By embracing a holistic approach that integrates advanced tools and thoughtful design, corporate training can evolve into dynamic systems that engage employees, foster meaningful growth, and align with the demands of a complex, ever-changing world.

Blooms and Enterprise Training in the Agentic AI Era

For nearly 70 years, Bloom’s Taxonomy has served as a guiding framework for learning, yet its implementation remains a challenge. The tech in EdTech hasn’t delivered much more than lower-level Bloom’s focusing on remembering and understanding knowledge checks.

Advances with GenAI have made it possible to address these limitations, particularly in enterprise training. Agentic goal driven AI now enables exceeding Mastery Learning and delivering 1:1 learning at scale, offering intelligent and dynamic assessments, personalized feedback, and adaptive content. Previously difficult tasks—like creating, assessing, and personalizing higher-order skills—are now seamless and scalable. They enable learners to add natural language interactions to converse fluidly and fill gaps in foundational knowledge while accelerating their learning growth. GenAI though human conversion can enable multiple disciplinary assessments addressing multiple skills as a human conversion assessment, unlocking EdTech’s utilization of upper-level Bloom’s engaging learners in creative and analytical tasks.

In the past, the upper-level skills were difficult to create, assess, and personalize. Now, with AI, unscripted unplanned interactions such as role play are personal, human-like, natural and free flowing. They are easy for AI to create, and easy to assess. Foundational theories have been adapted and extended to new digital learning environments using today’s learning platforms: LMSs and the latter evolve LXPs, that enable self-directed learners to select courses to customize their learning pathway. They utilize quizzes and simulations that target lower-order Bloom’s skills, while projects and discussions foster deeper engagement and left to human interactions. Yet, many systems remain static, unable to adapt quickly to diverse learner needs, leaving employees disconnected and disengaged. Agentic AI teamed with the content of enterprise knowledge are the two ingredients to enable this AI future.

GenAI ReEvaluating Learning Progression

Original principles connected to Bloom’s was a ladder of progression, like learning skiing starting on Green level beginner runs the progression up to Blue (intermediate), then the Black (expert). Starting at focusing on the lower level “remembering” then stepping up to “understanding” checks. With GenAI and bespoke learning principles, using the underlying Bloom’s Taxonomy, for example, learners can now be presented with optimized custom learning pathways with the help of an AI guide navigating through progressively complex upper-levels of cognitive engagement while the AI analyzes and optimizes the maximal learning in the minimal time.

Modern EdTech platforms extend Bloom’s principles into digital environments, guiding learners from basic comprehension to critical thinking and creativity. Learning concepts such as scaffolded learning, advancing through progressively complex cognitive tasks.

Today’s workplace, employees require more than rote memorization. They need what New York Times best-selling author David Epstein calls “range”—the ability to adapt across broad, unpredictable challenges. They are constantly asked to learn more skills, T-shaped skills, having breadth AND depth; the ability to apply skills in more unique, challenging, unpredictable, open or “wicked” environments.

Enterprises now seek training that moves beyond Bloom’s lower levels, beyond the compliance requirements and checking boxes on compliance requirements of fire escape procedures. With AI it’s easy to intake documents and offer advanced fast search tools. For effective workforce upskilling, the focus is on cultivating learner skills in advanced problem-solving, collaboration, and creativity; the upper-level Blooms is what is valued. Dynamic, adaptive training programs must engage learners at their pace, accommodating individual goals while maintaining relevance in rapidly evolving environments.

With AI-driven adaptability, the path to personalized, impactful learning is no longer theoretical. With the proper pace and challenge, learners will go up and down the taxonomy depending on the individual experience and interest in the various hard and soft skills required to do the work. Organizations that embrace this approach can empower employees to navigate complex challenges, delivering both immediate engagement and long-term skill development.

Mollick’s AI Co-Intelligence Example
Here’s an excerpt from Wharton professor Ethan Mollick’s 2024 New York Times best seller book, “Co-Intelligence: Living and Working with AI,” where Mollick shares an example of two architects and how their human vs AI mentoring experiences compare…

Envision two budding architects, Alex and Raj. Both have just graduated from top-tier architecture schools, brimming with fresh ideas and an eagerness to design.

Alex begins his journey by drafting designs using traditional methods. He frequently reviews famous architectural blueprints and gets feedback from a senior architect in his firm once a week. He believes that by continuously sketching and refining his designs, he will gradually improve. While this process does help him learn, it’s limited by the frequency of feedback and the depth of analysis that his mentor can provide in a short period.

Raj, conversely, integrates an Al-driven architectural design assistant into his workflow. Each time he creates a design, the Al provides instantaneous feedback. It can highlight structural inefficiencies, suggest improvements based on sustainable materials, and even predict potential costs. Moreover, the Al offers comparisons between Raj’s designs and a vast database of other innovative architectural works, highlighting differences and suggesting areas of improvement. Instead of just iterating designs, Raj engages in a structured reflection after every project, thanks to the insights from the Al. It’s akin to having a mentor watching over his shoulder at every step, nudging him toward excellence.

Over several months, the difference between Alex’s and Raj’s growth trajectories becomes evident. While Alex’s designs do mature and evolve, the pace of his growth is significantly slower. His once-a-week feedback sessions, although valuable, don’t provide the immediate, in-depth analysis that. Raj benefits from after every single design iteration. Raj’s approach, with the aid of Al, embodies the essence of deliberate practice. His consistent, rapid feedback loop, combined with targeted suggestions for improvement, ensures that he’s not just practicing more; he’s practicing better. In this context, the Al is more than just a tool for Raj; it serves as an ever-present mentor, ensuring that each attempt isn’t just about producing another design, but about consciously understanding and refining his architectural approach.

Here’s an excerpt from Wharton professor Ethan Mollick’s 2024 New York Times best seller book, “Co-Intelligence: Living and Working with AI,” where Mollick shares an example of two architects and how their human vs AI mentoring experiences compare…

Envision two budding architects, Alex and Raj. Both have just graduated from top-tier architecture schools, brimming with fresh ideas and an eagerness to design.
Alex begins his journey by drafting designs using traditional methods. He frequently reviews famous architectural blueprints and gets feedback from a senior architect in his firm once a week. He believes that by continuously sketching and refining his designs, he will gradually improve. While this process does help him learn, it’s limited by the frequency of feedback and the depth of analysis that his mentor can provide in a short period.
Raj, conversely, integrates an Al-driven architectural design assistant into his workflow. Each time he creates a design, the Al provides instantaneous feedback. It can highlight structural inefficiencies, suggest improvements based on sustainable materials, and even predict potential costs. Moreover, the Al offers comparisons between Raj’s designs and a vast database of other innovative architectural works, highlighting differences and suggesting areas of improvement. Instead of just iterating designs, Raj engages in a structured reflection after every project, thanks to the insights from the Al. It’s akin to having a mentor watching over his shoulder at every step, nudging him toward excellence.
Over several months, the difference between Alex’s and Raj’s growth trajectories becomes evident. While Alex’s designs do mature and evolve, the pace of his growth is significantly slower. His once-a-week feedback sessions, although valuable, don’t provide the immediate, in-depth analysis that. Raj benefits from after every single design iteration. Raj’s approach, with the aid of Al, embodies the essence of deliberate practice. His consistent, rapid feedback loop, combined with targeted suggestions for improvement, ensures that he’s not just practicing more; he’s practicing better. In this context, the Al is more than just a tool for Raj; it serves as an ever-present mentor, ensuring that each attempt isn’t just about producing another design, but about consciously understanding and refining his architectural approach.

The takeaway with this Mollick’s example is that Raj is using what Bloom’s and AI and has optimized his effectiveness upskilling in record time, while the prior, Alex is a traditional human mentorship, it is 1:1 which is the gold standard, but due to human availability is not as effective. Enterprises have reported that a typical knowledge worker can take over two years to become expert level proficient. Using the tools available with AI allow instructors and learners to receive the benefits of a Mastery Learning model at scale in accelerated time. They also get us closer to reaching the challenge presented by Bloom’s 2 sigma problem. Agentic AI digital platforms offer diverse ways to engage learners at different cognitive levels, leveraging multimedia, interactive elements, and data analytics to support deeper learning. To that end, what else can AI do for EdTech and e-learning?

Fig 6 Examples of GenAI with Bloom’s Taxonomy Verbs for discussion and personalized feedback.

Here’s a summary for modern E-Learning:

1. Incorporating Bloom’s Taxonomy in E-Learning Design

Online learning platforms utilize Bloom’s Taxonomy to design courses and assessments that cater to a range of cognitive skills. Here’s how each level of the taxonomy is being addressed:

Remembering: Digital platforms use quizzes, flashcards, and interactive content to help learners recall facts and basic concepts. Adaptive learning technologies personalize these tools, presenting content based on individual performance.
Understanding: Online courses leverage multimedia resources such as videos, infographics, and animations to explain concepts in multiple formats. Discussion forums and peer interactions facilitate deeper comprehension and clarification.
Applying: Simulations, virtual labs, and scenario-based learning allow learners to apply knowledge in practical contexts. Gamified elements, such as simulations and role-playing games, provide engaging ways to practice skills.
Analyzing: Platforms offer analytical tools that help learners break down complex information. Data-driven insights from assessments help learners understand their strengths and weaknesses, allowing for targeted practice and improvement.
Evaluating: Online assessments and peer review features enable learners to critique and provide feedback on various projects and solutions. Platforms use rubrics and structured feedback to guide learners through the evaluation process.
Creating: Learners are encouraged to produce original content through blogs, videos, and collaborative projects. Online platforms provide tools for creative expression and innovation, from digital storytelling to coding projects.

2. Adaptive Learning and Personalization

One of the most significant advancements in online learning platforms is the integration of adaptive learning technologies. By utilizing algorithms and data analytics, these platforms tailor the learning experience to individual needs. Bloom’s Taxonomy is embedded in these systems to ensure that content and assessments align with varying cognitive levels, allowing for personalized learning pathways.

Adaptive platforms assess learners’ proficiency in real-time and adjust the difficulty of tasks accordingly. For instance, if a learner struggles with a concept, the system might offer additional practice or simpler explanations before advancing to more complex material. This ensures that learners are engaging with content that matches their current level of understanding and cognitive ability.

3. Gamification and Interactive Learning

Gamification has become a prominent feature in online learning, enhancing engagement and motivation. By incorporating game-like elements—such as points, badges, and leaderboards—platforms encourage learners to progress through different cognitive levels. For instance, achieving mastery in basic recall (Remembering) might unlock more challenging tasks that require higher-order thinking (Analyzing and Creating).

Interactive learning environments, from solutions as virtual reality (VR) and augmented reality (AR), also play a role in applying Bloom’s Taxonomy. These technologies can create immersive experiences that allow learners to explore concepts and apply their knowledge in simulated real-world contexts.

AI can enable this and with bespoke learner personalization from their personal knowledge graph (pKg) can apply the methods that are appropriate learner. For example, some people are gamers, others are not, the AI can implement the tools and assessments to suggest the learning methods that are measured to be most effective.

4. Data-Driven Insights and Analytics

Data analytics have transformed how educators and learners interact with online platforms. By analyzing engagement patterns, completion rates, and performance metrics, platforms provide actionable insights into the effectiveness of instructional strategies. Tools such as Computer Adaptive Testing (CAT), speech rate analyzers, engagement assessments, help structure these insights by categorizing data according to cognitive processes, enabling more targeted interventions and improvements.

Using this data to refine learning approaches, offering additional support where needed and utilizing the optimal results driven learning methods in an iterative process ensures that the learning experience is continuously optimized, enjoyable and motivational resulting in better learning outcomes faster.

Summary

Although educational theories began to shift away from passive, teacher-centered methods to more student-centered approaches in the 1900’s their adoption is still limited. Instructors still don’t have the time and resources to overcome the challenges within their systems and most EdTech tools and approaches are also still very passive and teacher-centered. The tools and technology that do claim to incorporate Bloom’s Taxonomy are likely not allowing their products to adapt and approach the levels fluidly, or accommodate the different domains, in the different dimensions, like a 3-D chess board. What EdTech needs to do, ironically, is slow the learning process down.

Fig 6 The power of GenAI is like placing the learner as if in a 3D chess board, then using state-of-the-art assessment, guidance and analysis for understanding the learner’s individual personal knowledge identifying the goal and find the fastest learning pathway up towards mastery.

Give learners time to actually learn, and allow them to apply that learning so it sticks. Their learning should follow a progression; as far up Bloom’s Taxonomy as necessary. If done correctly, the rates of forgetfulness, attrition, and turnover all come down. The rates of engagement, completion and achievement all go up. People forget most of what they learn. “The Forgetting Curve,” defined by German psychologist Hermann Ebbinghaus, finds that if new information isn’t applied, people forget about 75% of it after just six days. Not so when the student participates and an appropriately timed higher-order thinking activity. All of these adaptations end up saving the company valuable time and money in the long run.

The influence of constructivist theories, particularly those of Dewey, Piaget, and Vygotsky, is also evident in today’s EdTech landscape, which increasingly focuses on active learning and social collaboration. Platforms like Google Classroom, Zoom, and collaborative online tools facilitate peer-to-peer learning and real-time interaction, aligning with Vygotsky’s emphasis on social learning. The rise of gamification, virtual reality (VR), and augmented reality (AR) in education echoes Dewey’s call for experiential learning, allowing students to engage in immersive simulations that make abstract concepts tangible. These technologies enable students to “learn by doing,” an approach that has proven to be more effective than traditional lecture-based learning in many contexts. All of which are built into the taxonomy.

Moreover, personalized learning, which has gained traction in recent years, reflects Piaget’s focus on developmental stages and individual learning needs. Adaptive learning technologies use data analytics to tailor educational content to the unique progress of each student, ensuring that learners are working within their zone of proximal development as Vygotsky suggested. This kind of personalization was impossible in traditional classroom settings but is now feasible with AI-driven platforms that adjust content and difficulty based on real-time assessments of a student’s strengths and weaknesses.

One of the key intersections between traditional learning theories and modern EdTech is the concept of metacognition, which Bloom emphasized in the higher levels of his taxonomy. Metacognitive strategies—such as self-reflection, setting goals, and evaluating one’s own learning processes—are increasingly supported by digital tools that provide immediate feedback and analytics. Platforms that track student progress, such as Khan Academy or Coursera, encourage students to reflect on their learning journey, fostering self-regulation and independent learning. AI Coaching tools like KhanMingo help accelerate learners though the Khan course content and fill gaps, then recommend next step courses in the library.

Fig 7 Best Practices in impactful development of EdTech L&D Source: https://citt.ufl.edu/resources/the-learning-process/applying-learning-theories/

In Conclusion

What scholars in traditional education have done since the 1950s is find niche applications and variations of foundational learning theories. Since then, “new theories” aren’t really doing anything transformational, they are just repackaging these original theories. New educational technologies should take the approach that Bloom did, which is to not create a “new theory” just to take advantage of the novelty, but combine the best of these foundational theories, and find a way to present them, when the learner needs them, and when the learner wants them.

The EdTech space doesn’t need another solution that claims to do the same thing that has been done over the past 100 years. What is needed is a solution that can maximize the impact of all of these theories. One that can adjust the timing, the pace, the content, and adapt as necessary, specific to the learner. Bloom’s Taxonomy remains a foundational model for structuring educational content and assessments, while the constructivist emphasis on active, social, and personalized learning has been greatly enhanced by digital technologies. The interplay between these classic theories and modern technological advancements highlights an ongoing evolution where the core principles of learning remain relevant, even as the tools and methods to facilitate learning become more sophisticated and data-driven.

Learn & Read More:

Understanding Bespoke Learning

Bespoke Learning | Mentor126.ai Learning Science

APPENDIX

Comparison of Bloom’s Taxonomy and the 2001 Revised Bloom’s Taxonomy

http://edorigami.wikispaces.com/Bloom%27s+Digital+Taxonomy

RESOURCES

Co-Intelligence Living and Working with AI By Ethan Mollick 2024 ISBN 9780593716717

Range: Why Generalists Triumph in a Specialized World by David Epstein ISBN: 978-1-5098-4349-7

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