Knowledge is the foundation of human progress. From ancient philosophy to modern scientific advancements, understanding how we acquire, categorize, and apply knowledge has been at the center of intellectual inquiry. As we navigate an increasingly complex world shaped by technology, globalization, and artificial intelligence, the ways in which we define and distinguish between different types of knowledge are more relevant than ever.
In this comprehensive blog, we take a deep dive into the types of knowledge that have shaped human understanding, their relevance in today’s society, and how emerging technologies are transforming our relationship with knowledge itself. We approach this topic from an interdisciplinary lens, drawing from philosophy, education, cognitive science, and artificial intelligence to offer a holistic exploration of what knowledge is, how it functions, and why it matters.
Defining knowledge is difficult, but knowledge can be seen as an awareness of facts, ideas, or situations. It includes familiarity with different subjects, people, and experiences.
A research paper defines knowledge as “Knowledge is a familiarity, awareness, or understanding of someone or something, such as facts, information, descriptions, or skills, which is acquired through experience or education by perceiving, discovering, or learning.”
Wikipedia defines knowledge as Knowledge is an awareness of facts, a familiarity with individuals and situations, or a practical skill.
Before diving into the different types of knowledge, it’s essential to understand what knowledge itself means. Emerging technologies are transforming our relationship with knowledge. Philosophically, knowledge has often been defined as "justified true belief." This classic definition implies that for someone to know something, the belief must be true and there must be justification for it. While this definition has evolved and been debated extensively, it forms a foundational framework for differentiating between types of knowledge.
Knowledge is not monolithic. It exists in various forms, each with its characteristics, modes of acquisition, and application. In 2025, this diversity is even more pronounced due to the proliferation of digital platforms, access to big data, and the evolving role of AI in knowledge management.
On a side note: Did you know data is different from information which is in turn different from knowledge and wisdom?
In a research paper, Ramona Nelson mentioned how data, information, knowledge and wisdom differ. Data is nothing but raw facts, numbers or facts which is further used to draw insights from. When the raw facts are processed together to produce meaning or insight - it is called information. Now, when we piece together multiple information pieces - we get knowledge. Think of it as having all chapters of a textbook - where every chapter is information and every chapter’s facts are data. The true benefit of doing this exercise is when multiple knowledge pieces are used together to apply the knowledge to human problems - example using FAQs to solve customer queries or more.
Our main motto of accumulating every knowledge source is to ultimately take action - which is wisdom.
Let’s clear up a common confusion:
Now that we know what knowledge is, let’s explore the 7 types of knowledge that you need to be aware about:
We’ll discuss all of them in detail below with a definition, explanation and examples for each one of them.
Explicit knowledge is the most tangible form of knowledge. It is codified, documented, and easily transferable. Examples include manuals, textbooks, procedures, and databases. In business environments, explicit knowledge is captured in standard operating procedures (SOPs), training materials, and policy documents.
With the growth of knowledge management systems and enterprise AI tools in 2025, explicit knowledge is now more searchable and accessible than ever before. Organizations are investing in advanced tagging, metadata frameworks, and natural language search to ensure this type of knowledge is efficiently leveraged.
Here are some examples of explicit knowledge:
Instruction Manuals: A user manual for a smartphone that details its features, operation, and troubleshooting steps serves as an example of explicit knowledge. The manual codifies the device's functionalities in a clear, accessible manner, allowing users to understand and utilize the product effectively.
Scientific Papers: Research articles published in academic journals represent explicit knowledge. These papers detail the methodology, findings, and conclusions of scientific investigations, offering a structured and documented account of research endeavors. They facilitate the dissemination of knowledge within the scientific community and beyond, enabling further research and application.
In 2025, explicit knowledge is leveraged via ontologies and taxonomies embedded into enterprise knowledge management systems. AI models are trained on structured datasets extracted from databases, APIs, and content repositories. For instance, large language models (LLMs) use embedding-based retrieval over explicit knowledge sources to answer queries in enterprise virtual assistants.
For companies, explicit knowledge can be FAQs, one sheets, pitch decks, knowledge base, privacy policy, etc. Here is an example of documented explicit knowledge used by companies.
Tacit knowledge is personal, experience-based knowledge that is difficult to articulate or formalize. It includes intuitions, insights, and know-how. Unlike explicit knowledge, tacit knowledge is acquired through practice and often shared through social interaction, mentorship, or collaboration.
For example, a master carpenter might "just know" how to choose the right wood or angle a cut perfectly. This know-how cannot be fully captured in writing. In 2025, capturing and sharing tacit knowledge has become a strategic priority for organizations, especially as experienced employees retire or transition.
Artistic Skill: An artist's ability to create art is an example of tacit knowledge. This includes understanding how different colors interact, the impact of light and shadow, and the expression of emotion through brushstrokes—all of which are learned through practice and experience, not easily articulated or taught.
Leadership Abilities: Effective leadership often relies on tacit knowledge. A seasoned leader's ability to motivate a team, navigate complex organizational politics, and make strategic decisions is rooted in experiences and insights that are difficult to codify but are critical for success.
In 2025, capturing tacit knowledge involves multimodal sensing (e.g., eye-tracking, biometrics) and behavioral logging. AI tools analyze repeated behavioral patterns to infer tacit rules. Knowledge elicitation via techniques like cognitive task analysis (CTA) and structured interviews is also used to convert some tacit knowledge into semi-explicit forms.
Procedural knowledge refers to knowing how to perform certain tasks or procedures. It is closely related to tacit knowledge but can be partially codified. Examples include knowing how to ride a bicycle, cook a recipe, or operate a machine.
In education and corporate training, procedural knowledge is often taught through simulations, demonstrations, and hands-on practice. Virtual Reality (VR) and Augmented Reality (AR) tools are increasingly used to teach procedural knowledge in immersive environments in 2025.
Here is an idea of what procedural knowledge tackles:
Riding a Bicycle: The knowledge of how to balance, pedal, and steer a bicycle is a form of procedural knowledge. These skills are acquired through practice and become ingrained in the rider's actions, allowing them to ride a bike without consciously thinking about the steps involved.
Programming: Writing computer code involves procedural knowledge. A programmer knows the syntax and structure required to create functional software, but this knowledge is applied through the process of coding, debugging, and testing, which are learned through experience.
Procedural knowledge is embedded in expert systems, robotics, and training simulators. In 2025, digital twins and AR/VR interfaces are used to simulate procedural knowledge environments. Reinforcement learning models are a computational analogue—they learn procedural knowledge by optimizing policy functions through repeated task execution in simulated or real environments.
Declarative knowledge is factual knowledge. It consists of information that can be stated or declared, such as historical dates, mathematical formulas, and factual data. This type of knowledge is essential in academic education and forms the backbone of many standardized tests.
With AI-powered learning platforms, declarative knowledge is now customized to individual learning styles. Adaptive learning algorithms in 2025 can dynamically adjust difficulty levels, recommend revision schedules, and identify gaps in a learner’s declarative knowledge.
Declarative knowledge is the foundation of semantic knowledge graphs, ontologies, and AI search engines. In education technology, adaptive learning platforms leverage student profiles to dynamically sequence declarative content based on knowledge mastery curves and spaced repetition models.
Conceptual knowledge involves understanding the relationships between concepts. It goes beyond facts and focuses on understanding frameworks, theories, and structures. For instance, understanding the concept of gravity involves recognizing how it relates to mass, force, and acceleration.
This type of knowledge is crucial for innovation and problem-solving. In research and development, conceptual knowledge enables scientists and engineers to connect seemingly unrelated ideas and create novel solutions. AI tools like concept mapping software and semantic search are accelerating the development of conceptual knowledge in 2025.
Conceptual knowledge is increasingly modeled through vector semantics and graph-based reasoning in 2025. Concept mapping algorithms visualize nodes (concepts) and edges (relationships). In generative AI, prompt engineering often requires a deep understanding of conceptual structures to ensure effective output generation across contexts.
Contextual knowledge refers to understanding the context in which information is used. It includes cultural, historical, and situational awareness. For example, knowing how to behave in a foreign country requires contextual knowledge of local customs, language, and etiquette.
In 2025, contextual knowledge is vital in global business operations, diplomacy, and cross-cultural collaboration. AI-powered translation tools, cultural intelligence apps, and scenario-based training modules are helping individuals and organizations build contextual knowledge.
AI agents in 2025 integrate contextual knowledge via situational embeddings, location-aware modeling, and cultural ontologies. Contextual bandits and meta-learning algorithms personalize outputs based on dynamic user and environmental data. This knowledge is critical for LLMs deployed in multilingual, multicultural, and multijurisdictional settings.
Experiential knowledge is gained through personal experience. It encompasses emotions, memories, and firsthand encounters. This type of knowledge is often visceral and deeply ingrained.
Healthcare, counseling, and customer service are domains where experiential knowledge plays a critical role. In 2025, experience-based learning is supported by reflective journals, peer sharing platforms, and AI-driven empathy training to enhance experiential understanding.
In 2025, experiential knowledge is captured through immersive technologies (e.g., wearables, emotion-sensing AI, and video diaries). Generative models analyze multimodal experiential data (text + emotion + behavior) to build empathetic systems, such as therapeutic bots or human-centric design assistants.
Understanding different types of knowledge is the first step of creating better knowledge management systems in personal as well as professional lives. Every type of knowledge plays an important role in improving knowledge sharing across teams.
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AI is not just a repository of data; it is becoming an active participant in the knowledge ecosystem. In 2025, AI systems are capable of generating, validating, and contextualizing various types of knowledge. From language models that help articulate tacit knowledge to machine learning algorithms that personalize declarative content, AI is reshaping how knowledge is created and consumed.
Knowledge graphs, natural language processing (NLP), and multimodal AI are driving this transformation. They enable machines to connect different types of knowledge, offering new insights and augmenting human understanding.
The shift toward collaborative knowledge is another hallmark of 2025. Knowledge is increasingly co-created through digital platforms, social networks, and decentralized systems. Wikipedia, open-source projects, and collaborative research platforms exemplify this trend.
This democratization of knowledge creation is challenging traditional hierarchies and empowering diverse voices. It also raises questions about credibility, bias, and information overload, underscoring the need for critical thinking and media literacy.
As we look to the future, the types of knowledge we value and cultivate will shape our societies, economies, and technologies. Lifelong learning, interdisciplinary education, and ethical AI integration will be central to this evolution.
Organizations must foster a culture of knowledge sharing, invest in intelligent knowledge systems, and prioritize human-centered design. Individuals, too, must adapt by becoming more reflective, adaptable, and informed consumers and creators of knowledge.
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In 2025, the landscape of knowledge is more complex and dynamic than ever. Understanding the different types of knowledge—explicit, tacit, procedural, declarative, conceptual, contextual, experiential, aesthetic, ethical, and strategic—is essential for navigating this landscape. With the rise of AI and digital transformation, our relationship with knowledge is evolving. But at its core, knowledge remains a deeply human pursuit—one that connects us, empowers us, and drives us forward.
Whether you're an educator, a leader, a technologist, or a lifelong learner, appreciating the diversity of knowledge types equips you to think more critically, act more wisely, and contribute more meaningfully in an ever-changing world.
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