Integration of artificial intelligence into educational processes for training specialists for digital oil and gas projects
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Abstract
This study focuses on the integration of artificial intelligence (AI) into the educational processes of training specialists for digital oil and gas projects. The aim of the work is to develop a conceptual model for the introduction of AI into the professional training system, ensuring the formation of competencies in demand in the context of the digital transformation of the industry. The methodological basis consisted of a systematic approach, comparative analysis, and an expert survey (n=25). The key areas of AI application have been identified: personalization of learning (74%), intelligent analysis of educational data (82%), virtual simulators and simulators (91%). A three-level AI integration model is proposed, including basic (adaptive learning systems), advanced (predictive analytics) and expert (generative AI for content development) levels. The organizational and pedagogical conditions for the effective implementation of the model are defined: modernization of the IT infrastructure of universities, improvement of the digital competencies of teachers (r=0.86), updating educational programs. The results obtained are valuable for optimizing the processes of training personnel capable of successfully solving the tasks of digital transformation of the oil and gas sector. It is advisable to direct further research towards the pilot implementation of the model and evaluation of its effectiveness.
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References
Aldowah H., Al-Samarraie H., Fauzy W.M. (2019). Educational data mining and learning analytics for 21st century higher education: A review and synthesis // Telematics and Informatics. № 37. рр. 13-49.
Becker S.A., Cummins M., Davis A., Freeman A., Hall C.G., Ananthanarayanan V. NMC horizon report: 2017 higher education edition. Austin: The New Media Consortium, 2017.
Chen L., Chen P.P., Lin Zh. Artificial intelligence in education: A review // Open access journal. 2020. pp. 6-8.
Ciolacu M., Tehrani A.F., Beer R., Popp H. Education 4.0-Fostering student's performance with machine learning methods // Mat. of the IEEE 23rd International Symposium for Design and Technology in Electronic Packaging (SIITME). 2017. pp. 438-443.
Dahlstrom E., Brooks D.C., Bichsel J. The current ecosystem of learning management systems in higher education: Student, faculty, and IT perspectives. Research report // EDUCAUSE Center for analysis and research (ECAR). 2014.
Hashim A.S., Othman M.F. A review on artificial intelligence techniques in engineering education// Mat. of the IEEE 9th Inter. сonf. on engineering education (ICEED). 2019. pp. 80-85.
Hwang G.J., Spikol D., Li K.C. Guest editorial: Trends and research issues of learning analytics and educational big data // Journal of educational technology & society. 2018. № 21(2). рр. 134-136.
Koskela M., Kiltti P., Vilpola I., Tervonen J. Suitability of a virtual learning environment for higher education // Electronic journal of e-learning. 2005. № 3(1). рр. 21-30.
Lotze T. Chatbots: Eine linguistische analyse. Bern: Peter Lang, 2016. 443 р.
Oztemel E., Gursev S. Literature review of Industry 4.0 and related technologies // Journal of intelligent manufacturing. 2020. № 31(1). рр. 127-182.
Schuster K., Groß K., Vossen R., Richert A., Jeschke S. Preparing for industry 4.0-collaborative virtual learning environments in engineering education. Cham: Springer, 2016. pp. 477-487.
Sidhu M., Kang L.Ch. New trends and futuristic information and communication technologies for engineering education: an overview // International journal of information and communication technologies and human development. 2010. № 2(4). рр. 68-79.
Siemens G., Long P. Penetrating the fog: Analytics in learning and education // Educause review. 2011. № 46(5). P. 30.
Vuorikari R., Punie Y., Gomez S.C., Van Den Brande G. DigComp 2.0: The digital competence framework for citizens. Update phase 1: The conceptual reference model (No. JRC101254) // Joint Research Centre (seville site). 2016.
Winkler R., Söllner M. Unleashing the potential of chatbots in education: A state-of-the-art analysis // The Academy of Management Annual Meeting (AOM). 2018. pp. 1-40.
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