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AI Competencies for non-computer science students in undergraduate education: Towards a competency framework

Published: 06 February 2024 Publication History

Abstract

Artificial Intelligence (AI) has been increasingly applied in various societal areas such as medicine, education, and science. For example, through the generation of more accurate medical diagnoses to support patients’ treatment, more content personalization to provide adaptive learning for students and more accurate predictions for future climate changes. Consequently, there is an increasing demand for professionals from different fields with AI competencies. These future professionals need preparation during their undergraduate education to deal with the remarkable AI breakthroughs in their domains and to understand, use, and help with the responsible development of these technologies. However, to address AI to non-computer science students in undergraduate education, it is necessary to thoroughly investigate the core AI competencies essential to these students acquire in order to prepare them effectively. Based on this, the objective of the research is to develop a framework with core AI competencies that can be adopted in future work to inform AI education for this target audience. Therefore, towards the AI competency framework for non-computer science students in undergraduate education, as an initial part of the process, we conducted semi-structured interviews with professionals working in the intersection of AI and other domains. The objective of the interviews was to qualitatively investigate the AI competencies considered suitable for incorporation into the undergraduate education curricula of non-computer science students from these professionals’ points of view. In this work, we present the results of these interviews and the list of core AI competencies for non-computer science students in undergraduate education according to these professionals. In summary, this list encompasses different perspectives, varying from basic AI competencies related to AI definition, history, and capabilities to more complex theoretical knowledge and practical skills regarding data and machine learning. The list also includes responsible AI competencies, covering AI’s social, ethical, and legal aspects.

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Cited By

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  • (2024)A Framework for a Master’s in Applied Artificial Intelligence Program in Computer and Information Systems DisciplineJournal of Information Systems Education10.62273/EQZE362535:4(495-511)Online publication date: Nov-2024
  • (2024)UNESCO's AI Competency FrameworkImpacts of Generative AI on the Future of Research and Education10.4018/979-8-3693-0884-4.ch004(75-96)Online publication date: 20-Sep-2024
  • (2024)Supporting the Development of AI Literacy Competencies Within Creative Computing EnvironmentsProceedings of the 2024 ACM Conference on International Computing Education Research - Volume 210.1145/3632621.3671422(529-530)Online publication date: 12-Aug-2024

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        Koli Calling '23: Proceedings of the 23rd Koli Calling International Conference on Computing Education Research
        November 2023
        361 pages
        ISBN:9798400716539
        DOI:10.1145/3631802
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        Published: 06 February 2024

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        1. AI education
        2. competency-based education
        3. interviews
        4. undergraduate education

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        • (2024)A Framework for a Master’s in Applied Artificial Intelligence Program in Computer and Information Systems DisciplineJournal of Information Systems Education10.62273/EQZE362535:4(495-511)Online publication date: Nov-2024
        • (2024)UNESCO's AI Competency FrameworkImpacts of Generative AI on the Future of Research and Education10.4018/979-8-3693-0884-4.ch004(75-96)Online publication date: 20-Sep-2024
        • (2024)Supporting the Development of AI Literacy Competencies Within Creative Computing EnvironmentsProceedings of the 2024 ACM Conference on International Computing Education Research - Volume 210.1145/3632621.3671422(529-530)Online publication date: 12-Aug-2024

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