Cognitive Hygiene Sources

If you want to see a chatGPT literature review of research into cognitive hygiene, it is here. Or you can view the literature yourself. Like LLM responses, these sources can be starting points for further search -- examine their bibliographies. Links to original sources of research reports listed below can be found in the lit review pdf.

Agarwal, D., Naaman, M., & Vashistha, A. (2025). AI suggestions homogenize writing

toward Western styles and diminish cultural nuances. CHI 2025. Source document.

Bastani, H., et al. (2025). Generative AI without guardrails can harm learning.

Proceedings of the National Academy of Sciences. Source document.

Boussioux, L., Lane, J. N., Zhang, M., Jacimovic, V., & Lakhani, K. R. (2024). The

crowdless future? Generative AI and creative problem solving. Organization

Science. Source document.

Brynjolfsson, E., Li, D., & Raymond, L. R. (2025). Generative AI at work. The Quarterly

Journal of Economics, 140(2), 889-942. Source document.

Danry, V., et al. (2023). Don't just tell me, ask me: AI systems that intelligently frame

explanations as questions improve human logical discernment accuracy over causal

AI explanations. CHI 2023. Source document.

Danry, V., et al. (2024). Deceptive AI systems that give explanations are more

convincing than honest AI systems and can amplify belief in false information. arXiv

preprint. Source document.

Dell'Acqua, F., McFowland, E., Mollick, E. R., et al. (2026). Navigating the jagged

technological frontier: Field experimental evidence of the effects of AI on

knowledge worker productivity and quality. Organization Science. Source

document.

Doshi, A. R., & Hauser, O. P. (2024). Generative AI enhances individual creativity but

reduces the collective diversity of novel content. Science Advances. Source

document.

Drosos, I., et al. (2025). It makes you think: Provocations help restore critical thinking

to AI-assisted knowledge work. arXiv preprint. Source document.

Girotra, K., Meincke, L., Terwiesch, C., & Ulrich, K. T. (2023). Ideas are dimes a dozen:

Large language models for idea generation in innovation. SSRN working paper.

Source document.

Hwang, A. H.-C., et al. (2025). It was 80% me, 20% AI: Seeking authenticity in co-

writing with large language models. Proceedings of the ACM on Human-Computer

Interaction. Source document.

Jakesch, M., Bhat, A., Buschek, D., Zalmanson, L., & Naaman, M. (2023). Co-writing

with opinionated language models affects users' views. CHI 2023. Source

document.

14Kestin, G., et al. (2025). AI tutoring outperforms in-class active learning. Scientific

Reports. Source document.

Klingbeil, A., Grutzner, L., & Schreck, P. (2024). Trust and reliance on AI - An

experimental study on the extent and costs of overreliance on AI. Computers in

Human Behavior, 160, 108352. Source document.

Kollock, P. (1998). Social dilemmas: The anatomy of cooperation. Annual Review of

Sociology, 24, 183-214. Source document.

Lee, H.-P., Sarkar, A., Tankelevitch, L., et al. (2025). The impact of generative AI on

critical thinking: Self-reported reductions in cognitive effort and confidence effects

from a survey of knowledge workers. CHI 2025. Source document.

Lee, M., Liang, P., & Yang, Q. (2022). CoAuthor: Designing a human-AI collaborative

writing dataset for exploring language model capabilities. CHI 2022. Source

document.

Moon, K., et al. (2025). Homogenizing effect of large language models on creative

diversity: An empirical comparison of human and ChatGPT writing. Source

document.

Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of

generative artificial intelligence. Science, 381(6654), 187-192. Source document.

Parasuraman, R., & Manzey, D. H. (2010). Complacency and bias in human use of

automation: An attentional integration. Human Factors, 52(3), 381-410. Source

document.

Peters, U., & Chin-Yee, B. (2025). Generalization bias in large language model

summarization of scientific research. Royal Society Open Science. Source

document.

Risko, E. F., & Gilbert, S. J. (2016). Cognitive offloading. Trends in Cognitive Sciences,

20(9), 676-688. Source document.

Shaer, O., et al. (2024). AI-augmented brainwriting: Investigating the use of LLMs in

group ideation. arXiv preprint. Source document.

Sparrow, B., Liu, J., & Wegner, D. M. (2011). Google effects on memory: Cognitive

consequences of having information at our fingertips. Science, 333(6043), 776-778.

Source document.

Wei, L., Yu, H., Chen, Q., et al. (2025). The effects of generative AI on collaborative

problem-solving and team creativity performance in digital story creation: An

experimental study. International Journal of Educational Technology in Higher

Education, 22, 23. Source document.

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