Dogmatism manifests in lowered information search under uncertainty

This requires developing an expertise and a well-adjusted confidence that is distinct from existing domain expertise.

“As it stands, this requires developing an expertise and a well-adjusted confidence that is distinct from existing domain expertise, with, for example,”

Metacognitive efficiency is predictive of individual differences in dogmatism about real-world issues such as politics and climate change, with the parameters governing confidence-based control correlating with people's openness to new information.

“Conversely, metacognitive efficiency is predictive of individual differences in dogmatism about real-world issues such as politics and climate change (Fischer et al. 2019, Rollwage et al. 2018), with the parameters governing confidence-based control correlating with people's openness to new information (Schulz et al. 2020).”

GenAI systems present usability challenges around prompting, evaluating and relying on outputs, and optimizing workflows.

“Generative AI (GenAI) systems offer unprecedented opportunities for transforming professional and personal work, yet present challenges around prompting, evaluating and relying on outputs, and optimizing workflows.”

User studies reveal a range of usability challenges around prompting, evaluating and relying on outputs, and deciding on an automation strategy.

“User studies reveal a range of usability challenges around prompting [208], evaluating and relying on outputs [156], and deciding on an automation strategy: whether and how to integrate GenAI into workflows [14,154].”

These demands are exacerbated by GenAI's non-determinism and model flexibility in terms of (a) the wide range of explicit and implicit parameters that users can adjust, and (b) systems' ability to work with prompts at a wide range of abstraction.

“We posit that these demands are exacerbated by GenAI's non-determinism and model flexibility (not to be confused with metacognitive flexibility) in terms of (a) the wide range of explicit and implicit parameters that users can adjust, and (b) systems' ability to work with prompts at a wide range of abstraction [156,157].”

Evaluating these outputs for quality therefore becomes far more important and effortful (in terms of cognitive load) compared to 'auto-complete' phrase suggestions or intelligent code completion.

“Evaluating these outputs for quality therefore becomes far more important and effortful (in terms of cognitive load) compared to 'auto-complete' phrase suggestions or intelligent code completion [156,157].”

The generative nature of GenAI means that many user workflows will or have shifted from users generating content to evaluating it.

“The generative nature of GenAI-its 'originality' [157]-means that many user workflows will or have shifted from users generating content to evaluating it [149,154], as already documented in programming [156] and manufacturing design [70].”

GenAI can produce (often extensive) content, such as entire emails, presentations, or software.

“GenAI can produce (often extensive) content, such as entire emails, presentations, or software.”

End-user programmers have been reported to 'eyeball' the AI outputs of natural language queries, which some suggest may deepen the existing over-confidence that such users have in their programs' accuracy.

“Likewise, end-user programmers have been reported to”

Model flexibility in GenAI systems presents a 'fuzzy abstraction matching' problem-it becomes difficult for users to discern a system's capabilities and to match one's intent and prompting accordingly.

“which presents what Sarkar et al. [156] term a 'fuzzy abstraction matching' problem-it becomes difficult for users to discern a system's capabilities and to match one's intent and prompting accordingly”

Planning can help users address the fuzzy abstraction matching problem, translating their goals and intentions into executable actions-a form of externalization where tacit knowledge is made into explicit prompts.

“Planning can also help users address the 'fuzzy abstraction matching' problem [156], that is, translate their goals and intentions into executable actions-a form of externalization where 'tacit' knowledge is made into explicit prompts [130].”

This is arguably a necessary trade-off within current GenAI systems, as it enables diversity of output, yet it exacerbates the challenge of confidence adjustment, particularly when working iteratively across prompting and output evaluation.

“This is arguably a necessary trade-off within current GenAI systems, as it enables diversity of output [136,156], yet it exacerbates the challenge of confidence adjustment, particularly when working iteratively across prompting and output evaluation (as per §3.1).”

Enabling users to critically evaluate AI outputs can build self-awareness by forcing users to rationalize their decisions to the GenAI system, enhance metacognitive flexibility by providing users with different perspectives on their task, and adjust confidence by adapting explanations to the users' needs and the models' confidence.

“Enabling users to critically evaluate AI outputs can build self-awareness by forcing users to rationalize their decisions to the GenAI system [156], enhance metacognitive flexibility by providing users with different perspectives on their task [15], and adjust confidence by adapting explanations to the users' needs and the models' confidence [110].”

They can introduce subtle, non-intuitive errors that a human would not introduce, further complicating evaluation.

“For instance, they can introduce subtle, non-intuitive errors that a human would not introduce, further complicating evaluation [156].”