Generative AI Meets Big Data: Efficiency Gains vs. Cognitive Overload

Abstract: This mixed-methods study explores how computer science educators (N=17) handle the use of generative AI tools like ChatGPT and Copilot. While 65% of participants reported spending less time on lesson planning and grading, 68% faced "validative overload", a newly identified issue where educators spend too much time checking AI outputs. Using cognitive load theory (Sweller, 2020), we examine how specific challenges, such as debugging AI-generated code, increase unnecessary cognitive load. Our findings show that 58% of educators lack training for AI integration 73% of AI-generated coding examples need major corrections. Validation tasks add 2.4 hours per week to the workload. We suggest a three-tiered framework for responsible AI use, focusing on pedagogical alignment, validation processes, and institutional support systems.

Generative AI, Cognitive Load, Educator Workflows, Data Overload, Computer Science Education

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