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ABSTRACT
With recent advances in artificial intelligence (AI), machine learning (ML) has been identified as particularly useful for organizations seeking to create value from data. However, as ML is commonly associated with technical professions, such as computer science and engineering, incorporating training in the use of ML into non-technical educational programs, such as social sciences courses, is challenging. Here, we present an approach to address this challenge by using no-code AI in a course for university students with diverse educational backgrounds. This approach was tested in an empirical, case-based educational setting, in which students engaged in data collection and trained ML models using a no-code AI platform. In addition, a framework consisting of five principles of instruction (problem-centered learning, activation, demonstration, application, and integration) was applied. This paper contributes to the literature on IS education by providing information for instructors on how to incorporate nocode AI in their courses and insights into the benefits and challenges of using no-code AI tools to support the ML workflow in educational settings.
Keywords: Artificial intelligence, Machine learning, IS education research, Information systems education
1. INTRODUCTION
Machine learning (ML), a subfield of artificial intelligence (AI), focuses on the development, application, and analysis of computer systems capable of learning from experience. In a common variant, supervised ML, a system is shown numerous examples of a type of data, e.g., images or texts describing particular objects or phenomena, to train it to "learn" or recognize patterns in them. The system can then use this learning to predict new "unseen" data, i.e., data it has not previously encountered (Jordan & Mitchell, 2015; Kühl et al., 2022). Leavitt et al. (2021, p. 750) define ML as "abroad subset of artificial intelligence, wherein a computer program applies algorithms and statistical models to construct complex patterns of inference within data" (see also Bishop, 2006).
Massive increases in the processing power of digital technology and available data, in combination with better algorithms, e.g., deep learning algorithms (see Lecun et al., 2015) have set the stage for increases in the use of ML in many contexts (Dwivedi et al., 2021). Accordingly, organizations are increasingly deploying intelligent systems that can process large amounts of data, provide knowledge and insights, and operate autonomously (Simsek et al., 2019; Sturm...