The value of megaconcepts in the digital age. From blueprint reconstruction to new research questions and research programmes, from a philosophy of science perspective

Abstract Artificial intelligence is increasingly automating the collection, comparison and summarisation of sources. This shifts the emphasis from access to information towards conceptual ordering, interpretation and the generation of new research questions. This article presents megaconcepts as methodological tools for this task and provides a methodological account of the previously published frameworks of five and twelve mega-concepts within a broader research programme. The article develops a blueprint method that reconstructs a work’s structure through seven fixed questions, combined with a principle of rupture that treats tensions within that structure as opportunities for further research rather than immediately classifying them as errors. It distinguishes between reconstruction, new conceptual insight, follow-up research and research direction. AI supports this process by reconstructing structures, suggesting connections and formulating possible questions, whilst the researcher remains responsible for interpretation, evaluation and the justification of further research. Human capacities for creation, evaluation and scepticism are situated within an explicitly articulated conception of the human person as embodied and ensouled. This conception understands human judgement as emerging from the interplay of thinking, feeling, sensation and intuition, and locates professional awareness beyond the limits of cognitive information processing The article situates this approach within a philosophy-of-science discussion drawing on Kuhn, Lakatos and Feyerabend. AI is not in itself a paradigm shift, but may create the technical conditions for changes in scientific practice. As factual information becomes more readily accessible, the article asks what distinguishes access to information from scientific knowledge, and how conceptual ordering, justification and disciplinary judgement contribute to that distinction. It also examines how mega-concepts can connect different theoretical approaches and support the development of research programmes. Finally, the article explores a limited democratisation of science. Wider access to AI, blueprint reconstruction and mega-concepts may enable people outside specialised academic communities to question knowledge structures, identify gaps and formulate new research questions. Broader participation does not mean that a majority determines what is true: the assessment and validation of proposed insights continue to require disciplinary expertise. The value of mega-concepts thus lies both in opening up intellectual space for new research and in broadening access to the formulation of questions, without equating that access with scientific validity.