Teach differentiated trust in knowledge sources as part of the social-institutional dimension of science education¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article argues that because students already navigate sources varying sharply in reliability and the generative model adds a source with opaque institutional backing, epistemic trust must be taught as a spectrum rather than as a binary. As the authors conclude, "teaching differentiated trust has become part of the social-institutional dimension of science education, rather than a matter of acceptance or rejection." This positions trust instruction within nature-of-science teaching rather than within adoption-versus-prohibition debates.

Design Implications¶

Context¶

Requirements¶

• Instruction must present the contrast between scientific accountability mechanisms and model governance as one of mechanism rather than guaranteed outcome, since replication difficulties and retractions are part of the scientific record

Constraints¶

• The strategy addresses text-based conversational systems as consulted by learners, not AI in general

Target Learners¶

• secondary science students
• undergraduate science students

Target Learning Goals¶

• evaluating the reliability of knowledge sources
• understanding peer review and institutional accountability

Related Strategies¶

• Design task-sensitive institutional AI policies rather than blanket permitted/prohibited distinctions
• Ask the Right Questions
• Dimensions of Critical Evaluation

Examples¶

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Key Sources¶

• Muminova, Z. O., Mamatkulov, D. M., Turokulova, R. I., Rakhmonova, N. T., Shavqiyev, O. S. O., & Zoirova, S. U. (2026). The nature of science in the age of generative artificial intelligence: Epistemic authority, materiality, and a synergetic response. EURASIA Journal of Mathematics, Science and Technology Education, 22(10), em2943. https://doi.org/10.29333/ejmste/19481