MNEMONIC TECHNIQUES FOR TEACHING AND LEARNING ARTIFICIAL INTELLIGENCE TERMINOLOGY
УДК 37
Abstract
Introduction: the article explores the methodological potential of mnemonic techniques for teaching and learning English-language artificial intelligence (AI) terminology. The relevance of the research is due to the rapid development of the AI field and the need for quick, easy and effective forms of learning of its fast developing terms. The linguistic analysis is based on 1,500 artificial intelligence terms. The research reveals that the share of pure neologisms in the AI terminology system is only 3%, while 34% are formed through the terminologization (semantic reinterpretation) of common vocabulary. This feature makes it possible to apply mnemonics in teaching and learning English-language artificial intelligence terminology. Materials and methods: the methodology is based on classical mnemonic approaches adapted for working with AI terms. The experimental test of effectiveness is conducted during the 2024/2025 academic year. The study involves 69 second-year students majoring in “Applied Mathematics and Computer Science” divided into an experimental group (34 students) and a control group (35 students). The former applies mnemonic techniques for terminology training (keyword method, physical response, mind mapping, rhymes, visualization, etc.), while the latter uses traditional methods (memorizing word lists, translation, drills). Results: the outcomes of the experimental test on training 224 AI terms confirm that mnemonic techniques demonstrate significant superiority and high effectiveness over traditional vocabulary teaching methods, with 83% compared to 70% in the control group. Conclusion: the research makes it possible to conclude that mnemonic techniques are a powerful and scientifically grounded tool for teaching and learning English-language AI terminology.
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References
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