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Teaching Tip Using No-Code AI to Teach Machine Learning in Higher Education

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Publicado en:Journal of Information Systems Education vol. 35, no. 1 (Winter 2024), p. 56
Autor principal: Sundberg, Leif
Otros Autores: Holmström, Jonny
Publicado:
EDSIG
Materias:
Teaching
Research
Higher education
Software
Students
Software development
Datasets
Computer science
Workflow
Machine learning
Core curriculum
Social sciences
Data collection
Professions
Colleges & universities
Computers
Artificial intelligence
Education
Intelligent systems
Information systems
Algorithms
College students
Educational programs
Learning
Social programs
Coding
Mathematics
Computer Software
Teaching Methods
Engineering
Educational Technology
Writing Instruction
Problem Based Learning
Career and Technical Education
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Resumen: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.
ISSN:1055-3096
2574-3872
1055-3104
DOI:10.62273OPL2902
Fuente:ABI/INFORM Global