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THE IMPACT OF ARTIFICIAL INTELLIGENCE ON THE DEVELOPMENT OF PREDICTIVE COMPETENCE IN MODERN SPECIALISTS

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Bibliographic Details
Title: THE IMPACT OF ARTIFICIAL INTELLIGENCE ON THE DEVELOPMENT OF PREDICTIVE COMPETENCE IN MODERN SPECIALISTS
Authors: Viacheslav Osadchyi, Maksym Pavlenko, Liliia Pavlenko, Oleksii Sysoiev, Vladyslav Kruglyk
Source: Alfred Nobel University Journal of Pedagogy and Psychology, Vol 1, Iss 29, Pp 167-178 (2025)
Publisher Information: Alfred Nobel University, 2025.
Publication Year: 2025
Subject Terms: skill transformation, human-ai interaction / human- machine interaction, generative ai, higher education, Psychology, critical thinking, predictive competence, large language models (llm), artificial intelligence (ai), cognitive processes, professional development, BF1-990
Description: The relevance of this study stems from the rapid development of artificial intelligence (AI), particularly large language models and generative technologies, which are profoundly transforming professional activity. These changes significantly influence the formation of predictive competence – a crucial human capacity for reasoned forecasting and decision-making under uncertainty. Empirical data confirm the high accuracy of AI-generated forecasts, sometimes surpassing that of humans, and indicate improved professional productivity when AI is used effectively. At the same time, diverse adaptation patterns to AI use necessitate a rethinking of the role of human judgement and raise concerns about technological dependency, algorithmic bias, and unequal access to innovation. These challenges call for a reorientation of educational approaches, placing emphasis on critical thinking and skills for effective human-AI interaction. The purpose of the study is to conduct a comprehensive analysis and theoretical substantiation of the impact of modern AI technologies – particularly large language models and generative AI – on the development and transformation of professionals’ predictive competence. Research objectives are as follows: to conceptualise predictive competence within the context of digital transformation; to analyse structural shifts in its key components (cognitive, regulatory, and communicative); to explore the mechanisms of AI’s influence on cognitive predicting processes; to systematise potential advantages and risks associated with the integration of AI in professional contexts. The study employs theoretical methods such as analysis, synthesis, and generalisation of findings from interdisciplinary research, as well as conceptual and comparative analysis of human-AI interaction models and the evolving essence of predictive competence. AI demonstrably increases the efficiency of forecasting processes but simultaneously transforms their nature – from autonomous human-generated predictions to the management of hybrid human-machine systems. This shift requires professionals to acquire new skills, including critical evaluation and validation of AI outputs, prompt engineering, and the integration of AI-generated insights into complex decision-making. The most significant transformations influence the cognitive, regulatory, and communicative components of predictive competence. The dual nature of AI’s impact is evident – offering enhanced analytical capabilities while posing risks of hallucinations, cognitive inertia, and increased digital inequality. Accordingly, the professional role evolves from that of executor to analyst, moderator, and ethical regulator of forecasting processes. Conclusions. Artificial intelligence is irreversibly reshaping the landscape of professional activity, par- ticularly in the domain of forecasting. Its influence on predictive competence is deep, multifaceted, and at times contradictory. Maximising its benefits while mitigating associated risks requires a proactive, critical, and adaptive attitude from professionals and educators alike. To this end, educational programmes should be enriched with: practice-oriented integration of AI tools into professional curricula; targeted development of skills for evaluating AI outputs; competence in prompt engineering for forecasting; the promotion of metacognitive awareness. These measures will enable the preparation of specialists who do not merely un- derstand AI but can employ it purposefully, critically, and responsibly to enhance their predictive capacities.
Document Type: Article
ISSN: 3041-220X
3041-2196
DOI: 10.32342/3041-2196-2025-1-29-15
Access URL: https://doaj.org/article/b1ea8cdc14ae47a3b9c7ebef76ab577a
Accession Number: edsair.doi.dedup.....dc46a59471990de1e3a04b9cf8f74bfe
Database: OpenAIRE
Description
Abstract:The relevance of this study stems from the rapid development of artificial intelligence (AI), particularly large language models and generative technologies, which are profoundly transforming professional activity. These changes significantly influence the formation of predictive competence – a crucial human capacity for reasoned forecasting and decision-making under uncertainty. Empirical data confirm the high accuracy of AI-generated forecasts, sometimes surpassing that of humans, and indicate improved professional productivity when AI is used effectively. At the same time, diverse adaptation patterns to AI use necessitate a rethinking of the role of human judgement and raise concerns about technological dependency, algorithmic bias, and unequal access to innovation. These challenges call for a reorientation of educational approaches, placing emphasis on critical thinking and skills for effective human-AI interaction. The purpose of the study is to conduct a comprehensive analysis and theoretical substantiation of the impact of modern AI technologies – particularly large language models and generative AI – on the development and transformation of professionals’ predictive competence. Research objectives are as follows: to conceptualise predictive competence within the context of digital transformation; to analyse structural shifts in its key components (cognitive, regulatory, and communicative); to explore the mechanisms of AI’s influence on cognitive predicting processes; to systematise potential advantages and risks associated with the integration of AI in professional contexts. The study employs theoretical methods such as analysis, synthesis, and generalisation of findings from interdisciplinary research, as well as conceptual and comparative analysis of human-AI interaction models and the evolving essence of predictive competence. AI demonstrably increases the efficiency of forecasting processes but simultaneously transforms their nature – from autonomous human-generated predictions to the management of hybrid human-machine systems. This shift requires professionals to acquire new skills, including critical evaluation and validation of AI outputs, prompt engineering, and the integration of AI-generated insights into complex decision-making. The most significant transformations influence the cognitive, regulatory, and communicative components of predictive competence. The dual nature of AI’s impact is evident – offering enhanced analytical capabilities while posing risks of hallucinations, cognitive inertia, and increased digital inequality. Accordingly, the professional role evolves from that of executor to analyst, moderator, and ethical regulator of forecasting processes. Conclusions. Artificial intelligence is irreversibly reshaping the landscape of professional activity, par- ticularly in the domain of forecasting. Its influence on predictive competence is deep, multifaceted, and at times contradictory. Maximising its benefits while mitigating associated risks requires a proactive, critical, and adaptive attitude from professionals and educators alike. To this end, educational programmes should be enriched with: practice-oriented integration of AI tools into professional curricula; targeted development of skills for evaluating AI outputs; competence in prompt engineering for forecasting; the promotion of metacognitive awareness. These measures will enable the preparation of specialists who do not merely un- derstand AI but can employ it purposefully, critically, and responsibly to enhance their predictive capacities.
ISSN:3041220X
30412196
DOI:10.32342/3041-2196-2025-1-29-15