AI evidence extraction

PROMPT ENGINEERING FOR AI-INTEGRATED ENGLISH LANGUAGE LEARNING AND TEACHING IN UNIVERSITIES

Olgа Bratanych · Вісник науки та освіти · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

7/10

Relevance

0/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.52058/2786-6165-2026-1(43)-1320-1333

Methodology & findings

Study design

Literature review and conceptual analysis of prompt engineering techniques in educational contexts, with taxonomy development and illustration of five key prompt engineering techniques (zero-shot, few-shot, chain-of-thought, meta prompting, and self-consistency) through examples in EFL/ESP teaching..

Main result

The study established that "prompt engineering contributes to the development of 21st-century skills (the 4 Cs: critical thinking, communication, collaboration, and creativity) and also increases students' self-regulation and motivation." Additionally, "the use of structured approaches to prompt engineering in the context of ESP/EFL teaching and learning can significantly improve the accuracy of AI responses and ensure academic integrity."

Research paradigm

interpretivist

Author conclusions

The authors conclude that "prompt engineering" should be conceptualized "as a separate but integrated component of language training" and that "the author considers the creation of prompts not only as a technical task, but as a complex linguistic activity that integrates grammatical, lexical, stylistic, and communicative aspects." This demonstrates that prompt engineering is fundamental to modern EFL/ESP pedagogy.

Risk of bias

No empirical validation or experimental control groups; Lack of quantitative data to support claims; No comparison with alternative teaching methodologies; Theoretical framework not tested against real student populations; Author-developed taxonomy not subjected to independent peer validation

Open questions raised

The paper identifies the need to adapt the UNESCO AI competency framework and train specialists capable of effectively interacting with AI. It emphasizes Ukraine's strategic plans for sovereign LLM development in 2026 and the necessity to prepare educators and learners for this technological landscape.
The authors identify the need to adapt the UNESCO AI competency framework for educational contexts and highlight the importance of training specialists capable of effectively interacting with AI at the international level, particularly in light of Ukraine's strategic plans to launch a sovereign LLM in 2026.
The paper identifies the need to adapt the UNESCO AI competency framework and train specialists for Ukraine's planned sovereign LLM launch in 2026, suggesting gaps in specialized AI literacy training for educational professionals.

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