Designing for Learning with Generative AI is a Wicked Problem: An Illustrative Longitudinal Qualitative Case Series
Clara Scalzer, Saurav Pokhrel, Sara Hunt, Greg L Nelson

TL;DR
This study explores the complex challenges faced by computing educators in integrating generative AI into student learning, highlighting how progress in one area can hinder development in others, thus requiring multi-dimensional solutions.
Contribution
The paper presents a longitudinal qualitative case series analyzing students' experiences with GenAI, revealing the intricate, conflicting goals educators must navigate in this context.
Findings
GenAI use skills can decrease ethical awareness and vice versa.
Increased GenAI proficiency did not boost students' career confidence.
Students develop concerning patterns despite ethical and skill instruction.
Abstract
Students continue their education when they feel their learning is meaningful and relevant for their future careers. Computing educators now face the challenge of preparing students for careers increasingly shaped by generative AI (GenAI) with the goals of supporting their learning, motivation, ethics, and career development. Our longitudinal qualitative study of students in a GenAI-integrated creative media course shows how this is a "wicked" problem: progress on one goal can then impede progress on other goals. Students developed concerning patterns despite extensive instruction in critical and ethical GenAI use including prompt engineering, ethics and bias, and industry panels on GenAI's career impact. We present an analysis of two students' experiences to showcase this complexity. Increasing GenAI use skills can lower ethics; for example, Pat started from purposefully avoiding GenAI…
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Taxonomy
TopicsOnline Learning and Analytics · Artificial Intelligence in Education · Artificial Intelligence in Healthcare and Education
