Interpretive cognitive model to augment artificial intelligence

Project description

Traditional approaches to knowledge discovery assume that knowledge is inherently embedded within data and can be extracted through computational methods. These approaches primarily rely on identifying structural patterns in data to uncover insights. While effective in many contexts, this “embedded paradigm” is limited by its assumption that knowledge exists objectively within data, independent of context or interpretation. This project introduces an alternative perspective, the interpretive paradigm, which views knowledge as something that is actively constructed rather than passively discovered. In the interpretive paradigm, data is not knowledge itself but a signal that requires interpretation. Knowledge emerges through the interaction between:

• the data (signal), and
• an interpreter (e.g., a model, system, or human) guided by a specific framework or schema.

This means that:

• The same dataset can produce multiple valid interpretations
• Knowledge is context-dependent and evolving, not fixed
• Interpretation plays a central role in shaping understanding

Further information

Methodology Students involved in this project will:

• Develop computational models that simulate interpretive processes
• Design schemas or frameworks that guide how data is interpreted
• Experiment with dynamic and evolving knowledge representations
• Compare the performance and outputs of this framework with traditional approaches such as Artificial Neural Networks (ANNs)

Expected Outcomes

• A novel framework for understanding and constructing knowledge
• Insights into how interpretation influences learning and decision-making
• Evaluation of whether interpretive models can generate richer or more adaptive knowledge than conventional data-driven models
• Contributions toward next-generation intelligent systems that are more context-aware and human-aligned

Assumed knowledge

Training AI/ML/DL models, Data Engineering, Programming in Python.

Note: You need to register interest in projects from different supervisors (not a number of projects with the one supervisor).
You must also contact each supervisor directly to discuss both the project details and your suitability to undertake the project.