AI for non-programmers: Applied AI in the lectures for students without programming skills
Project Overview
The document explores the integration of generative AI in education, emphasizing accessibility for non-programmers, particularly within STEM disciplines. It introduces a didactic planning script designed to facilitate learning about AI through contextual and practical applications, such as energy management, thereby enabling students to grasp the relevance of AI in addressing real-world challenges. This approach underscores the importance of incorporating AI into educational curricula to equip students with essential skills for the future workforce. By demonstrating how generative AI can be effectively utilized without extensive programming knowledge, the document advocates for the transformative potential of AI in enhancing educational experiences and preparing students for a technology-driven landscape. Overall, the findings suggest that a focus on practical applications and contextual learning can significantly enhance understanding and engagement with AI in educational settings.
Key Applications
Didactic planning script for applied AI in energy management
Context: Master students in energy management at Osnabrück University of Applied Sciences
Implementation: Three to four teaching units utilize a structured AI application pipeline, where students engage in practical AI tasks without prior programming knowledge.
Outcomes: Students gain a practical understanding of AI concepts and how they apply to energy management, enhancing their problem-solving skills.
Challenges: Limited programming skills among students may impede deeper understanding of complex AI models.
Implementation Barriers
Educational Barrier
Students often lack the foundational programming knowledge needed to fully grasp AI concepts.
Proposed Solutions: Use of simplified AI application pipelines and executable web pages to facilitate learning without programming.
Resource Barrier
Running complex AI models requires significant computational resources, which may not be available to all students.
Proposed Solutions: Adoption of cloud services and ensuring that all students have access to necessary software like IPython Notebooks.
Project Team
Julius Schöning
Researcher
Tim Wawer
Researcher
Kai-Michael Griese
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Julius Schöning, Tim Wawer, Kai-Michael Griese
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai