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<p>The sustained interest in artificial intelligence (AI) as an area of postsecondary study is evident in growing enrollment figures and in expanded course offerings focusing on subjects like machine learning, natural language processing, and computer vision. Yet, for the most part, the contributions of these courses to the development of future AI practitioners is considered only in isolation. We find that a comprehensive framework to conceptualize the combined impact of these courses throughout a student’s college education is lacking. Building on our previous research–particularly a study of computer science (CS) student attitudes and competencies related to AI and AI ethics–in this paper we begin to apply key findings towards the conceptualization of an <i>AI curriculum</i>. We argue that this curriculum must rest on three content pillars: technical foundations of AI systems, social implications of AI applications, and effective uses of AI tools. Such a holistic approach to AI education is necessary to underscore the centrality of policy considerations to the alignment of AI systems with normative objectives, and to reinforce an efficient and appropriate incorporation of AI into students’ productivity workflows. In an effort to imagine how the AI curriculum can better prepare the future AI workforce, we emphasize three core AI-related competencies that are currently under-developed among CS students studying AI. Then, we map each competency onto one or more skills which we argue should be fostered throughout the AI curriculum. Finally, we propose examples of curricular interventions to address each of these skills and provide one example of an AI-focused undergraduate course sequence, illustrating opportunities to construct a cohesive AI curriculum across multiple separate computing courses. In taking this ‘curriculum-level’ perspective, we proffer that our synthesis of disparate strands of inquiry through this paper constitutes an important contribution to the literature regarding the teaching about and teaching with AI.</p>

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Scoping the AI curriculum: key competencies for future AI practitioners

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月25日

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  • James Weichert,
  • Hoda Eldardiry

摘要

The sustained interest in artificial intelligence (AI) as an area of postsecondary study is evident in growing enrollment figures and in expanded course offerings focusing on subjects like machine learning, natural language processing, and computer vision. Yet, for the most part, the contributions of these courses to the development of future AI practitioners is considered only in isolation. We find that a comprehensive framework to conceptualize the combined impact of these courses throughout a student’s college education is lacking. Building on our previous research–particularly a study of computer science (CS) student attitudes and competencies related to AI and AI ethics–in this paper we begin to apply key findings towards the conceptualization of an AI curriculum. We argue that this curriculum must rest on three content pillars: technical foundations of AI systems, social implications of AI applications, and effective uses of AI tools. Such a holistic approach to AI education is necessary to underscore the centrality of policy considerations to the alignment of AI systems with normative objectives, and to reinforce an efficient and appropriate incorporation of AI into students’ productivity workflows. In an effort to imagine how the AI curriculum can better prepare the future AI workforce, we emphasize three core AI-related competencies that are currently under-developed among CS students studying AI. Then, we map each competency onto one or more skills which we argue should be fostered throughout the AI curriculum. Finally, we propose examples of curricular interventions to address each of these skills and provide one example of an AI-focused undergraduate course sequence, illustrating opportunities to construct a cohesive AI curriculum across multiple separate computing courses. In taking this ‘curriculum-level’ perspective, we proffer that our synthesis of disparate strands of inquiry through this paper constitutes an important contribution to the literature regarding the teaching about and teaching with AI.