Non-expert students exhibit intuitive behaviors towards LLM-based AI systems when prompt engineering¶

Claim · All claims
Evidence · 1 study · qualitative r? · q2
Currency · newest study 2024 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Qualitative analysis reveals characteristic intuitive behaviors of students interacting with LLM-based AI systems through prompting. → Knoth 2024

Evidence¶

Knoth 2024¶

Knoth, N., Tolzin, A., Janson, A., & Leimeister, J. M. (2024). AI literacy and its implications for prompt engineering strategies. Computers and Education: Artificial Intelligence. https://doi.org/10.1016/j.caeai.2024.100225

q2 · i? · qualitative · r?

The article reports qualitative insights into "students’ intuitive behaviors towards LLM-based AI systems" as part of its study of non-experts in higher education. The available text prints no further detail on the specific behaviors.

"We also provide qualitative insights into students’ intuitive behaviors towards LLM-based AI systems."

Discussion¶

Learner Variables¶

• Prior Knowledge — predictor: learners who differ on it differ in outcomes

Related Claims¶

• Certain aspects of AI literacy play a role in higher-quality prompt engineering by non-expert students — related
• AI intuition emphasizes epistemic judgment rather than output optimization, distinguishing it from prompt engineering — related
• Students describe concrete verification behaviors for GenAI explanations, treating evaluation as an iterative filter rather than a single accept-or-reject judgment — related
• Higher-quality prompt engineering skills predict the quality of LLM output in goal-directed use of generative AI tools — related
• Learner agency in GenAI-mediated learning is enacted through prompting, questioning, verification, comparison, revision, and disciplinary judgment — related
• GenAI use involves substantial hidden labor of prompting, verification, and correction that remains invisible in productivity evaluation — related