A Tutorial on Teaching Data Analytics with Generative AI

AI-assisted Learning and Assignment Support

Context: Various educational settings, including data analytics courses for MBA students, graduate-level business courses, and engineering labs, where students engage with AI to enhance their understanding and completion of assignments.

Implementation: Students use AI assistants (custom-made GPTs) to support their learning by facilitating assignments, generating content, and providing interactive dialogue. This includes describing data operations in natural language, creating visualizations from sketches, and receiving personalized feedback on coding tasks.

Outcomes: Increased student engagement, understanding of course material, enhanced creativity, and improved performance in quizzes and assignments. Students report higher satisfaction with learning materials and experience significant time savings.

Challenges: Dependence on AI for completing tasks may lead to shallow learning and reduced engagement with material. There is a risk of uneven knowledge distribution due to reliance on AI assistance, and potential trivialization of serious subject matter in certain contexts.

Gamified AI Learning Activities

Context: Classroom environments where students engage in competitive or collaborative activities, such as coding quizzes and peer teaching exercises, facilitated by AI.

Implementation: AI facilitates game-like activities where students answer coding-related questions or teach each other using AI-generated content. This includes structured gamified formats for quizzes and peer learning experiences utilizing AI-generated prompts and content.

Outcomes: Increased motivation, engagement, and peer interaction among students. Enhanced understanding of programming concepts and teamwork skills.

Challenges: Managing multiple AI interfaces in larger classes can be complex, and there may be uneven engagement levels among students.

Simulation and Practical Applications via AI

Context: Hands-on lab exercises in data visualization and engineering, where students apply AI-generated scenarios to solve real-world problems.

Implementation: Students interact with AI to simulate scenarios such as a nuclear meltdown or creating data visualizations. They correct AI-generated code or translate visual concepts into programming language, promoting practical application of their skills.

Outcomes: Enhanced coding skills, creativity, and teamwork. Practical understanding of complex concepts in a controlled, simulated environment.

Challenges: There is a risk of trivializing serious subjects and students may struggle with transitions from visual or simulated concepts to actual coding tasks.

AI-generated Study Aids and Content Summarization

Context: Educational settings where students prepare for assessments or engage with content through AI-generated materials, such as quiz cheat sheets from assignments.

Implementation: AI is used to extract and summarize content from student assignments, generating study aids and quiz materials to streamline homework preparation.

Outcomes: Significant time savings and improved organization for students, leading to higher performance in assessments.

Challenges: Dependence on AI for content summarization may reduce deeper engagement with the material and understanding of core concepts.

Skill Gap

Students may become overly reliant on AI, hindering their ability to learn coding independently. Additionally, they might struggle with critical thinking and problem-solving skills.

Proposed Solutions: Instructors encourage critical thinking and problem-solving alongside AI use; emphasize the importance of understanding coding principles while maintaining a balance between AI assistance and independent problem-solving.

Quality of Learning

AI-assisted learning may lead to superficial understanding of material, as students can complete assignments without deep engagement. There is a risk of students becoming overly reliant on AI for problem-solving.

Proposed Solutions: Shift focus from traditional homework to interactive, AI-supported learning experiences that require active student participation, ensuring a balance between AI assistance and independent learning.

Implementation Challenges

Integrating AI into existing curricula requires significant adjustments in teaching strategies and assessment methods. Managing multiple AI instances can also be challenging for instructors and students.

Proposed Solutions: Gradually incorporate AI tools into teaching, providing support and resources for both instructors and students. Implement structured prompts and session management techniques to streamline interactions.

Access and Equity

Not all students may have equal access to AI tools, leading to disparities in learning opportunities.

Proposed Solutions: Ensure all students have access to necessary technology and provide alternative resources for those without AI tools.

Perception Barrier

Activities may be perceived as juvenile or inappropriate for graduate-level education.

Proposed Solutions: Justification of the educational value and innovative nature of AI activities.

Engagement Barrier

Students might become overly reliant on AI for problem-solving, hindering critical thinking.

Proposed Solutions: Encouraging a balance between AI assistance and independent problem-solving.

Project Contact: Dr. Jianhua Yang

LLM Model Version: gpt-4o-mini-2024-07-18

Analysis Provider: Openai