Learners using a GenAI system issued prompts that offloaded tasks that may have been important for them to perform themselves, such as differentiating concepts and deciding what to learn¶

Claim · All claims
Evidence · 1 study · qualitative r? · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Qualitative analysis of prompts found participants asked the GenAI to differentiate between concepts, provide ideas about what they should learn, and simplify previous responses — tasks that may have been important for them to do themselves. → Yifan Liu 2026

Evidence¶

Yifan Liu 2026¶

Yifan Liu, Jaime Arguello, Orland Hoeber, Chang Liu, Soo Young Rieh, Luanne Sinnamon, et al. (2026). Report on CHIIR 2026 Workshop on Generative AI and Academic Search (GAI&AS). ACM SIGIR Forum. https://arxiv.org/abs/2606.08936 doi:10.48550/arxiv.2606.08936

q2 · i? · qualitative · r?

The report describes a qualitative analysis of prompts issued to a GenAI system by people learning about diffusion and osmosis (Urgo et al., 2026), presented in a lightning talk. Participants also issued retrieval-like prompts and prompts uniquely suited to GenAI, such as clarifying confusion and testing understanding. No effect size is printed.

"Finally, participants asked the GenAI to perform tasks that may have been important for them to do themselves. Participants asked the GenAI system to differentiate between concepts (e.g., Differences between diffusion and osmosis.); provide ideas about what they should learn (e.g., What is important to know?); and simplify a previous response (e.g., Too long, one paragraph.)"

Discussion¶

Related Claims¶

• Evidence suggests over-reliance on generative AI may negatively affect students' self-regulated learning behaviors, potentially harming retention and transfer to novel contexts — the general claim this finding bears on, with the studies behind it
• In interviews with 25 engineering students at one Swedish university, students described GenAI as acceptable when it stimulates reflection and directly copying its outputs as cheating, and one used its fallibility as a prompt to double-check — related
• Volume of offloading masks differences between offloading processes that become visible when profiling what is offloaded — related
• Volume-based offloading profiles differentiate learners primarily by prior knowledge, and offloading volume is negatively associated with essay authorship — related
• Falsifiable hypothesis: unrestricted AI use on deep-processing tasks will produce a product–process dissociation—higher-rated assignments but lower unaided delayed transfer — related
• Some content-based offloading profiles differed in prompting volume even though volume was not a clustering variable, suggesting what is offloaded constrains how much is offloaded — related
• GenAI systems achieved consistently high scores on programming assessments written entirely in Estonian — related
• Behavioural engagement indicators such as platform interaction and task completion do not by themselves establish cognitive engagement with mathematical ideas — related
• Distributed epistemic agency emerges as learners attribute authority to AI outputs and shift responsibility for knowledge validation, though some engage dialogically — related