The concept of Never-Ending Learning (NEL) originates in the field of machine learning, where it describes systems capable of learning continuously from data, improving their knowledge base over time without requiring full retraining. The foundational work was developed at Carnegie Mellon University—notably the project NELL: Never-Ending Language Learning led by Tom M. Mitchell—which demonstrated how an AI system could autonomously and iteratively extract structured knowledge from large volumes of unstructured data. In this context, NEL represents the ideal of an AI that never stops learning, refining, and expanding its models through perpetual exposure to new information. This scientific paradigm has been adapted and introduced into human learning by Professor Gabriele Carboni, who applied the principles of continuous, incremental, and data-driven learning to the educational experience. Carboni reinterpreted NEL as a model for human knowledge ecosystems, where learning becomes a persistent, relational process supported by artificial intelligence, no longer limited to the constraints of traditional academic formats.

Never-Ending Learning (NEL) redefines education as a continuous and evolving process, transforming learning from a finite product (a course, a degree, a workshop) into a dynamic ecosystem that accompanies the learner before, during, and long after the formal training event.

Instead of ending when the lesson stops, learning unfolds through an ongoing, bidirectional relationship between the learner, the knowledge base, the teacher, and the community of practice.

At the core of NEL is the integration of Artificial Intelligence, which acts as a dynamic, adaptive tutor. AI personalizes learning paths in real time by:

• adjusting explanations and difficulty based on the learner’s level of understanding,

• generating new examples, case studies, and scenarios on demand,

• connecting core concepts to current events, related fields, or emerging insights.

As a result, knowledge becomes alive, modular, and context-aware, expanding with every interaction. Learning is no longer consumed: it is co-created.

A key structural element of NEL is the headless book: a knowledge base separated from any fixed presentation format. The theoretical “body” of the book is not confined to printed pages but is delivered through an AI interface capable of reinterpreting, expanding, and tailoring the content infinitely.

This transforms the book into a dynamic knowledge engine, enabling each learner to explore the same foundational content in uniquely personalized ways.

In Contemporary Marketing Management, NEL serves as a bridge between two domains:

• the technical paradigm of continuous learning in AI systems, and

• the human-centered evolution toward lifelong, AI-augmented learning environments.

The first and most advanced implementation of the NEL model is the Contemporary Marketing Management educational ecosystem developed by Professor Gabriele Carboni.

For the first time, a university-level textbook is transformed into a living, interactive AI-powered learning environment.

This system works through three integrated layers:

1. The Knowledge Base – The foundational concepts of marketing are structured as a coherent, machine-readable corpus.

2. The Ecosystem – Students access a digital platform where they interact with an AI that uses these concepts actively and intelligently.

3. The NEL Experience – Learners can ask the AI to apply theories to their startup idea, to generate a customized marketing plan, or to re-explain complex models in simpler words or through industry-specific examples.

Through this architecture, learning no longer ends at the final chapter of a book. It continues as a perpetual conversation in which the student explores, applies, and expands the conceptual framework—supported by an AI that evolves with their needs.

NEL therefore represents a profound shift: education becomes a living ecosystem, continuously renewed through interaction, curiosity, and technological augmentation.

A university marketing textbook structured as a machine-readable knowledge base can be integrated into an AI platform where students interact directly with the concepts. Instead of following a fixed syllabus, each learner can ask the AI to apply theories to real projects, generate tailored case studies, or clarify difficult topics in new ways. Learning continues long after the course ends, evolving through ongoing dialogue between the student and the AI, which adapts and expands the foundational content over time.