A paradox shadows the promise of technology-enhanced education: as learning becomes more efficient and programmable, meaningfulness often becomes thinner and more fragile. Students complete tasks, accumulate credits, and touch content streams at unprecedented scale, yet struggle to weave enduring structures of understanding, transfer insights across contexts, and anchor learning in values and life narratives. Meaningism Learning Theory (MLT) addresses this paradox by repositioning meaning–not information or short-term performance–as the central design target of education. Grounded in three axioms (3CEP: learning is change; change is meaning; education is the ethical promotion of change), MLT articulates **Dimensional Mastery** as the horizon competence for the AI era: the capacity to identify, integrate, regulate, and switch among meaning dimensions while preserving coherence and purpose. MLT operationalizes a Cultural–Action–Neural paradigm through four interlocking components: a triadic pathway from objects through relations to the active construction of meaning (Object–Relation–Construcgence, or 3LS), a Ten–Dimensional Meaning Space (10DMS) for design navigation, Six Change Catalysts (6CS) for orchestrating time, intensity, and context, and an Eight-Question Method (8QM) that standardizes inquiry and reflection. The theory translates into a five-phase instructional rhythm and a “dual tuning” strategy balancing difficulty with meaning density. Assessment emphasizes delayed retention, far transfer, process evidence, and narrative transformation. We propose falsifiable claims and a multi-site research agenda, while clarifying ethical guardrails and human–AI division of labor. The aim is to invite constructive international dialogue around a Chinese-origin theory presented with humility and transparency, open to scrutiny and co-development in diverse cultural and resource contexts.
Zhu et al. (Fri,) studied this question.