Nothing Left to Verify: How Simulation Design and AI Assistants Foreclose Information Literacy in Virtual Laboratories

Laboratory work exists to teach students how to look for information, weigh it, and decide what to trust. Virtual laboratories rarely give them the chance. This paper asks why, and sets out what an environment must retain if that chance is to survive. The starting point is simple: a virtual laboratory's information landscape is written by a developer, not encountered by a student. That makes its features design parameters rather than facts of the setting, and design parameters can be set differently. The paper is conceptual. It follows a theory adaptation design, using ideas from information literacy and information seeking research to put pressure on the inquiry-cycle account that currently guides virtual laboratory design. Literature was drawn purposively from physics education research, information science, and studies of generative AI in learning. University medical physics supplies the worked example. The analysis finds that virtual laboratories foreclose the information problem twice over. Scripted design strips out measurement variation, puts the accepted value on screen, and makes the interface self-sufficient; an embedded AI assistant then answers on request, and nothing obliges the student to check. Five informational attributes of the environment, plus one precondition governing their safe use, are placed within a four-layer framework, and seven propositions connect their settings to what students do and to what they learn. No data were analysed. The five attributes are proposed rather than validated, and the claim that low verification demand is the current default is stated as a premise awaiting documentary audit. The framework still yields workable design principles: treat measurement uncertainty as a variable, take the reference value off the screen, and make checking something that gets marked.

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