DEVELOPMENT OF DIGITAL COMPETENCE OF ACADEMIC AND RESEARCH STAFF USING GENERATIVE ARTIFICIAL INTELLIGENCE
DOI:
https://doi.org/10.31110/2616-650X-vol13i8-015Keywords:
generative artificial intelligence, digital competence, academic and research staff, professional development methodology, higher education, prompt engineering, DigCompAbstract
The article discusses the pressing issue of the gap between the rapid development of generative artificial intelligence (AI) and the level of digital competence of scientific and scientific-pedagogical workers (SPW). The authors of the study have developed and substantiated a comprehensive methodology for utilizing generative AI to target the development of digital competence in this category of specialists. The research methodology is based on systemic, competency-based, activity-based, and andragogical approaches. It involves the use of theoretical (analysis of standards, modeling) and empirical (analysis of educational resources, generative AI services, case studies, and problem situations) methods. As a result, based on the author's model of digital competence of NPP, a methodology has been developed that consists of five interrelated components: goals, content, methods, means, organizational forms, and expected results. The methodology was developed taking into account the following components of digital competence: digital learning, research, methodological, organizational-communication, and cross-functional. For each component, specific training content and practical examples of tasks are proposed, aimed at developing skills in prompt engineering, critical evaluation of generated content, utilizing AI for data analysis, preparing publications, developing training materials, and solving complex professional tasks. The technological component of the methodology allows for a flexible combination of interactive teaching methods (workshops, project activities, case studies) and organizational forms (face-to-face, distance, or combined) tailored to the needs of adult learning. The expected results are assessed using a set of quantitative and qualitative indicators and involve students achieving sufficient and high levels of digital competence. The authors believe that the proposed methodology offers a systematic solution, enabling a transition from intuitive to strategic use of AI, thereby contributing to the improvement of scientific and educational activities.
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