Using mind maps and artificial intelligence to enhance the acquisition of specialized Russian vocabulary by Vietnamese students

Abstract

Mind maps and AI-based adaptive trainers are promoted for teaching Russian technical terms to Vietnamese engineering students, but the benefit of a map depends on how the terms are built, which has not been measured on the specialty’s actual vocabulary. The study identifies which properties of the current earth-moving machinery terminology determine how much material a map reduces to recurring nodes and what remains for adaptive practice. The material is the official text of GOST ISO 6165-2015 (sections 3 and 4): 47 terminological units in 46 clauses with definitions. Terms were coded for map nodes, component position and grammatical form; node recurrence, word inventory reduction, node fan, lexical–conceptual hierarchy agreement and inflectional load were computed and re-checked under alternative rules. The units contain 89 occurrences of 46 nodes, 43 of them (48.3%) repeats, and the map's word inventory is 36.1% smaller than the list. The node «машина» (machine) carries 15 of 39 links. All 20 subclauses with a defined parent cite it, yet five subordinate terms lack the parent term's nodes. Non-dictionary forms make up 47.7% of content word forms; node forms change in 41.5% of occurrences in section 3 and 6.2% in section 4. The map's gain comes from node reuse and is concentrated in machine-family names; gender, case and number forms in section 3 and the overloaded node «машина» define the target of adaptive practice, which should track a node together with the operation that changes its form.